The mycobiome in human cancer: analytical challenges, molecular mechanisms, and therapeutic implications

Abstract

The polymorphic microbiome is considered a new hallmark of cancer. Advances in High-Throughput Sequencing have fostered rapid developments in microbiome research. The interaction between cancer cells, immune cells, and microbiota is defined as the immuno-oncology microbiome (IOM) axis. Fungal microbes (the mycobiome), although representing only ∼ 0.1-1% of the microbiome, are a critical immunologically active component of the tumor microbiome. Accumulating evidence suggests a possible involvement of commensal and pathogenic fungi in cancer initiation, progression, and treatment responsiveness. The tumor-associated mycobiome mainly consists of the gut mycobiome, the oral mycobiome, and the intratumoral mycobiome. However, the role of fungi in cancer remains poorly understood, and the diversity and complexity of analytical methods make it challenging to access this field. This review aims to elucidate the causal and complicit roles of mycobiome in cancer development and progression while highlighting the issues that need to be addressed in executing such research. We systematically summarize the advantages and limitations of current fungal detection and analysis methods. We enumerate and integrate these recent findings into our current understanding of the tumor mycobiome, accompanied by the prospect of novel and exhilarating clinical implications.

Introduction

Human tumorigenesis is a multistep process reflecting genetic alterations that drive the progressive transformation of normal cells into highly malignant derivatives [1,2,3]. In 2000, Douglas Hanahan and Robert Weinberg proposed conceptualizing six core rules of cancer cell transformation, the ‘Hallmarks of Cancer’ [1]. In 2022, in the third update of ‘Hallmarks of Cancer: New Dimensions,’ the initial six features have been expanded to fourteen, with polymorphic microbiomes being recognized as new hallmarks [4]. The immune-mediated interactions between immune cells, cancer cells, and microbes in the tumor microenvironment (TME) have been defined as the immuno-oncology microbiome (IOM) axis [5]. About 4 × 10¹³ microbial cells, spanning ∼ 3 × 10³ species, inhabit the human body, with fungi representing only ∼ 0.1-1% of the microbiome [5, 6]. The role of fungi (the mycobiome) in the IOM axis remains largely unexplored.

Indeed, there are growing reasons to conclude that polymorphic mycobiome, residing in the colon, other mucosa, and connected organs, or in tumors themselves, can diversely influence by inducing many of the hallmark capabilities [4, 5, 7,8,9]. For example, pan-cancer analysis of multiple body sites identified that in gastric cancer, genes associated with cytokine interactions, host immunity, and inflammation were positively enriched in Candida-dominant tumors, including IL1A, IL1B, IL6, IL8, CXCL1, CXCL2, and IL17C [8]. Inflammation has been shown to promote Candida colonization vigorously; Candida maintains this pro-inflammatory environment by augmenting inflammation [10,11,12]. Inflammation promotes multiple hallmark capabilities by supplying bioactive molecules to TME, including growth factors that sustain proliferative signaling, proangiogenic factors, extracellular matrix-modifying enzymes that facilitate invasion and metastasis, and inductive signals that lead to activation of epithelial-mesenchymal transition (EMT) and other hallmark-facilitating programs [2, 13,14,15,16,17].

Although changes in the composition and function of the mycobiome have been described in various diseases, research on fungal involvement in human carcinogenesis is only in its infancy [18,19,20,21,22]. In the last decade, in addition to association studies on the cancer mycobiome, several studies have begun to elucidate potential mechanistic links between fungal function and cancer-related processes [8, 23,24,25]. In 2017, Gao et al. characterized the fecal mycobiota of colorectal cancer (CRC) patients for the first time [23]. They observed fungal dysbiosis in CRC and an increased proportion of opportunistic fungi Trichoderma and Malassezia [23]. In 2019, Aykut et al. found that compared to healthy pancreas, pancreatic ductal adenocarcinoma (PDAC) harbored a 3000-fold increase in fungi and was significantly enriched in Malassezia [24]. In 2022, two groundbreaking studies revealed fungal distribution, association with immune cell types, and potential prognostic value, including synergistic effects with bacteria, by characterizing fungi across various cancers [8, 25]. In addition, the mycobiome may correlate with cancer therapy response [26, 27]. In 2024, Lin et al. evaluated the association of trans-kingdom microbes with immune checkpoint inhibitors (ICIs) by multi-cohort multi-cancer analyses [27]. They identified several microbial species that were consistently enriched in ICI responders across different cancer types, and these species were potential predictors of ICI response [27].

Favorable conditions for microbial colonization in tumors include hypoxia, disorganized vasculature, and an immunosuppressive microenvironment [28]. Based on previous studies, we summarized the potential sources of intratumoral mycobiome. (i) Mucosal disruption during tumorigenesis leads to invasion by colonizing fungi, including pancreatic, colorectal, and lung cancer [24, 25, 29]. For example, administration of GFP-labelled Saccharomyces cerevisiae to control or tumor-bearing mice via oral gavage revealed that the fungus migrated to the pancreas through the sphincter of Oddi within 30 min [24]. (ii) Fungi from other sites spread directly to the tumor site via the blood. The tumor vascular system is irregular and leaky, which may allow some fungi from the blood to enter the tumor tissue directly [28]. In lower gastrointestinal (GI) cancers, Dohlman et al. found that tumor and blood samples from the same patient harbored highly similar fungal compositions, raising the possibility that fungal DNA translocates from the GI tumor site to the bloodstream [8]. (iii) Intracellular fungi migrate to tumor tissue as fragments or intact cells [25, 30, 31].

Accumulating studies have outlined how fungi are linked to—or even cause cancer and how they could be manipulated to treat cancer [16, 32,33,34,35,36,37]. The growing appreciation of the importance of polymorphic variable microbiomes in cancer raises several questions. (i) Whether intratumoral mycobiome constitute predetermined ecological niches or represent only transient random colonization? (ii) Does the mycobiome positively or negatively affect the acquisition of cancer hallmark capabilities? (iii) To what extent are fungi etiological agents complicit or passive bystanders? (iv) Does the research into tumor-associated mycobiome have clinical implications? To answer these questions, we reviewed recent research to assess tumor-specific fungal ecologies and the causal and complicit roles of mycobiome in cancer development and progression. In addition, we discussed technological advances and limitations of fungal detection methods and fungal-based microbial therapies in cancer treatment to explore the possibilities of fungi in clinical applications.

Tumor-specific fungal ecologies

Advances in -omics analyses have revealed diverse phylogenetic communities within the human body, providing information on species diversity and abundance ranging from the distribution of “friendly commensals” to pathogenic dysbiosis (Supplementary Table 1). Hosts restrict resident microbial populations to natural niches such as the skin and gut mucosa through diverse control mechanisms, including immunity, barrier function, transit, physiological homeostasis, and host behavior [38,39,40]. The immune system is the most sophisticated host control mechanism, driving a range of responses that reshape the microbiomes and help maintain normal host-microbiota relationships, such as the expression of antimicrobial peptides [38, 41,42,43,44,45,46]. The expected outcome of many host control mechanisms is to promote beneficial symbionts, yet the disruption of such checkpoints may promote cancer development and progression.

Gut mycobiome

Dysbiosis of the gut mycobiota has been implicated in a wide range of diseases (i.e., autoimmune, metabolic, and cancer), including loss of symbionts, outgrowth of pathobionts or opportunists, and disturbed inter-microbial competition and microbial diversity [24, 47,48,49,50,51,52,53,54,55]. The gut is mainly colonized by three fungal phyla: Ascomycota, Basidiomycota, and Chytridiomycota [56, 57]. At the genus level, fungi in human feces primarily consist of Candida, Aspergillus, Cryptococcus, Saccharomyces, and so on [56, 58,59,60]. C. albicans has been identified as the most abundant fungal species in the human gut [58]. A common feature of mycobiome studies is a high degree of interindividual variability, with only a few core genera consistently present in most individuals [57, 61,62,63,64,65]. Internal transcribed spacer (ITS) sequencing samples from 16 cohorts across 11 countries worldwide confirmed the existence of four fungal enterotypes: Sacc-type (Saccharomyces-dominated), Can-type (Candida-dominated enterotype), Asp-type (Aspergillus-dominated), and Asc-type (unclassified Ascomycota and Saccharomycetales phylum-dominated) [66]. These enterotypes are taxonomically and functionally diverse in composition but exhibit stability in populations and geographic locations and are significantly correlated with bacterial enterotypes [66]. Fungal enterotypes have a substantial age preference, with Can-type being enriched in the elderly and increasing the risk of multiple diseases [66]. The main influencing factors include compromised intestinal barrier, gut aging, dysregulation of the immune system, and lifestyle-linked factors (e.g., diet, medication, and reduced social contact) [37, 66,67,68,69,70,71,72]. In addition, the fungi-contributed aerobic respiration pathway associated with the Can-type enterotype might mediate the association between the compromised intestinal barrier and aging [66, 73, 74].

Recent evidence has revealed the ecologic association between fungi and CRC, implying the importance of gut fungal dysbiosis in contributing to cancer development and progression [23, 75, 76]. The Ascomycota and Basidiomycota are the most prevalent phyla in the CRC [75,76,77,78]. CRC is associated with a higher Basidiomycota/ Ascomycota ratio, an index that defines fungal dysbiosis in an ecosystem [75, 77, 79, 80]. Coker et al. identified CRC-specific shifts in fungal composition reflected by the enrichment of six genera, including Rhodotorula and Malassezia of the Basidiomycota phylum and Acremonium of the Ascomycota phylum [77]. Four Aspergillus species, A. flavus, A. rambellii, A. sydowii, and A. ochraceoroseus, were enriched in the CRC, with the highest fold change of about four for A. flavus [77]. Lin et al. conducted a meta-analysis of 7 published fecal metagenomic datasets and an additional in-house cohort to investigate the correlation between gut fungi and CRC [78]. Signature CRC-associated fungi included six enriched (A. rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and one depleted species (A. kawachii) [78]. A. rambellii is the most abundant species in patients with CRC [78, 81]. A. rambellii significantly promotes the growth of in vitro cancer cells and in vivo tumors [78]. One possible mechanism is the ability of A. rambellii to produce a variety of aflatoxins (which have been classified as carcinogens and mutagens) [77, 78, 81,82,83,84,85]. Further mechanistic investigation is needed to assess whether A. rambellii produces aflatoxins to contribute to colorectal tumorigenesis.

A complex ecological network between fungi and bacteria in the healthy gut is severely disrupted by CRC [77]. Ecological analyses revealed higher co-occurring fungal intrakingdom and co-exclusive bacterial–fungal correlations in CRC [77]. Co-occurrence interactions between fungi and bacteria, contributed mainly by fungal Ascomycota and bacterial Proteobacteria in control, were reverted to co-exclusive interplay in CRC [77]. The overall fungi-fungi correlations are diminished in CRC, whereas bacteria-bacteria correlations strengthened during the progression of CRC [78]. These findings suggested that enteric fungi and bacteria could have antagonistic interplays in CRC: alterations in bacterial composition in CRC may provide fungi with a favorable condition for intrafungi interaction, which may mediate their effect on CRC development [77, 78].

In addition to the gastrointestinal tract, intestinal dysbiosis occurs in various cancer types [26, 86, 87]. Liu et al. found that patients with hepatocellular carcinoma (HCC) showed significantly decreased diversity of the gut mycobiome and increased abundance of C. albicans compared to patients with liver cirrhosis [87]. Abnormal colonization by C. albicans reprogrammed HCC metabolism and promoted NLRP6-dependent HCC progression [87]. The gut microbiota composition in early melanoma changes along a gradient from in situ to invasive (and metastatic) melanoma [86]. Changes in the microbiota and mycobiota are associated with the histological features of early-stage melanoma, as well as with the clinical course of advanced-stage melanoma and the response to immunotherapy through direct or indirect immunomodulation [86].

Notably, understanding gut fungi remains incomplete, and there is no consensus on how much genetic and phenotypic diversity has been discovered in the human gut fungi. Identifying characterized fungi at the species level has been challenging because of the high degree of heterogeneity of gut fungi in different populations [78, 88, 89]. Different sample types (fecal versus mucosal biopsies), sequencing methods, reference databases, host factors, and geography contribute to these differences [89]. Geographical differences include dietary habits, population levels, and exposure to different environments [88, 90]. Some variations can be explained by host-extrinsic factors, including diet, bacterial interactions, drug use, and mycovirus production, as well as host-intrinsic factors, such as genetics, immune system detection, and biological age [88].

Oral mycobiome

Ghannoum et al. revealed the presence of a core oral mycobiome consisting of 13 taxa, with Candida being the most frequent, followed by Cladosporium, Aureobasidium, Saccharomycetales, Aspergillus, Fusarium, and Cryptococcus [91]. In recent years, studies confirmed that Malassezia was a prominent commensal and was even more abundant than Candida in some subjects [92, 93]. Disruption of the oral microbiome has been proposed to indicate, trigger, or influence the course of oral diseases, especially among immunocompromised patients [94,95,96]. Currently, there are fewer studies on the relevance of the oral mycobiome to cancer, mainly focusing on C. albicans [97, 98].

Perera et al. performed ITS sequencing on 52 tissue biopsies (25 oral squamous cell carcinomas: OSCC, 27 intraoral fibroepithelial polyps: FEP) to characterize the OSCC-associated mycobiome [98]. A total of 364 species representing 160 genera and two phyla (Ascomycota and Basidiomycota) were identified, with Candida and Malassezia making up 48% and 11% of the average mycobiome, respectively [98]. The species richness and diversity were significantly lower in OSCC [98]. Species-wise, C. albicans, C. etchellsii, and Hannaella luteola–like species were enriched in OSCC, while a Hanseniaspora uvarum–like species, Malassezia restricta, and A. tamarii were the most significantly abundant in FEP [98]. C. albicans is considered a pathogenic factor in oral carcinogenesis due to its ability to induce host inflammatory responses [99]. The findings of Vesty et al. support this observation [97]. C. albicans is the dominant fungal species in the saliva of head and neck squamous cell carcinoma (HNSCC) patients, and its relative abundance is positively correlated with the concentrations of IL-1 β and IL-8 in saliva [97].

C. albicans virulence factors and adherence to oral mucosa or artificial surfaces have been extensively reviewed [100, 101]. The ability of C. albicans to colonize, penetrate, and damage tissues depends on an imbalance between C. albicans virulence factors and host defenses, often due to specific defects in the immune system [99,100,101]. C. albicans has been shown to degrade basement membranes, extracellular matrix, and E-cadherin [102,103,104,105,106]. Several cell surface proteins have been identified as adhesins recognizing host molecules and postulated to mediate the formation of C. albicans biofilms in vitro [107, 108]. Acetaldehyde, produced by alcohol metabolism, is thought to be associated with chronic alcohol intake-related oral cancer [109, 110]. In ethanol-associated oral cancer, C. albicans may be involved in the synthesis of salivary acetaldehyde, and higher levels of acetaldehyde may be associated with oral tumorigenesis [109, 110]. In addition, Candida might induce OSCC by directly producing carcinogenic compounds, such as nitrosamines [111].

Interestingly, some species within these genera, including A. tamarii and A. alternata identified in the current sample, are known to produce compounds with anticancer activity and can inhibit the growth of C. albicans [112, 113]. While these species may represent transient environmental fungi or passenger oral fungal taxa, it is also possible that carriage of some of these species confers protection against the development of oral cancer. Further research to explore these scenarios is needed to harness their potential for novel prevention and control strategies.

Intratumoral mycobiome

With the breakthroughs of technology, the organs and tumor tissues previously considered sterile have been demonstrated to harbor low-biomass fungal communities, known as the ‘intratumoral mycobiome’ (Fig. 1) [8, 25]. Dohlman et al. analysis of tumor mycobiomes revealed both pan-cancer and cancer-specific associations between tumor-associated fungi and human cancers [8]. Several Candida species, Saccharomyces cerevisiae and Cyberlindnera jadinii, are highly abundant in GI tumor mycobiome communities, while Blastomyces and Malassezia species are abundant in lung and breast tumors, respectively [8]. Candida survives and is transcriptionally active at the tumor site, predicting host oncogene expression, disease state, and survival [8]. In gastric cancer, the high rates of Candida were associated with the expression of pro-inflammatory immune pathways, whereas in colon cancer, Candida predicted metastatic disease and attenuated cell adhesion [8]. Similarly, Narunsky-Haziza et al. detected fungi in 35 cancer types, often intracellular [25]. To analyze the fungal-triggered immunome of different cancer types, the study describes three different fungi-bacteria-immune clusters, herein called “mycotypes,” named F1 (Malassezia-Ramularia-Trichosporon), F2 (Aspergillus-Candida), and F3 (multi-genera including Yarrowia) [25]. Two of these are characterized by higher inflammation and lymphocyte depletion, while the third is associated with a strong macrophage response [25]. Mycotypes significantly segregated immune response types, suggesting that different groups of intratumoral mycobiomes can elicit unique host responses. Clinically focused assessments demonstrated the prognostic and diagnostic capacities of tissue and plasma mycobiomes and synergistic predictive performance with bacteriomes [25].

Intratumoral mycobiomes are also observed in a variety of cancer types, including lung, pancreatic, papillary thyroid carcinoma, breast, gastroesophageal and prostate cancers [24, 29, 114,115,116,117,118,119,120,121]. Enriched A. sydowii has been associated with immunosuppression and poor patient outcomes in LUAD patients [29]. Gut microbiome dysbiosis is associated with Kras activation in pancreatic tumors [122]. To assess whether the fungal content of the pancreas changes during tumorigenesis, Aykut et al. examined human PDAC specimens and a mouse model of slow-progressing PDAC produced by the expression of oncogenic Kras in pancreatic progenitor cells [24]. The fungal community infiltrating PDAC was markedly enriched for Malassezia in mice and humans [24]. Antifungal therapy significantly reduced PDAC progression and improved survival, whereas Malassezia repopulation- but not Candida, Saccharomyces, or Aspergillus- accelerated oncogenesis [24]. Alam et al. found that antifungal therapy significantly reduced the progression of PDAC and improved survival [114]. However, a replicated analysis study by Fletcher et al. of the sequencing data of Aykut did not identify similar differences in human pancreatic tissue or fecal samples [123]. Standardized methods for generating and analyzing microbiome sequencing data, especially those generated from low biomass samples, are needed to improve the reproducibility of results across studies [123, 124].

Emerging evidence indicates that the gut mycobiome plays a significant role in promoting CRC tumorigenesis [125]. However, a crucial question remains: can these fungi metastasize to distant tumor sites and influence the intratumor mycobiome? Aykut et al. discovered that the gut mycobiome can migrate to the pancreas and directly influence the pancreatic microenvironment [24]. They assessed evidence of fungal dysbiosis during tumorigenesis using p48Cre; LSL-KrasG12D (KC) mice. The alpha diversity was reduced in PDAC compared to the gut [24]. Ascomycota and Basidiomycota were the only phyla detected in pancreatic tissue, whereas Mortierellomycota and Mucoromycota were additionally detected in the gut at low abundance [24]. Analysis of human tissue samples and fecal revealed that human PDA tumors harbor a mycobiome that is distinct from the gut or normal pancreas [24]. Liu et al. conducted a study to identify the potential fungal species that may contribute to the correlation between intratumor mycobiome dysbiosis and LUAD progression [29]. They utilized shotgun metagenomic deep sequencing to analyze the spatial features of the mycobiome at the species level from various ecological niches within the host. The analysis of multi-kingdom microbiota showed distinct mycobiome composition in tissue samples compared to gut or bronchoalveolar lavage fluid (BALF) samples, featuring significantly higher alpha-diversity and notably different beta-diversity [29]. Using SourceTracker to estimate the likely sources of intratumoral mycobiome, they found that the relative proportions of the intratumor mycobiome from different environments were 12% for BALF and 0.5% for stool [29]. This evidence indicates that although low in abundance, the intratumoral mycobiome seems to have a more direct oncogenic role in cancer development than the gut mycobiome.

Fig. 1

Pan-cancer analysis reveals specific intratumoral mycobiome. (a) Using sequencing data from Weizmann (WIS) and TCGA, fungal DNA was profiled, and fungal signatures associated with diverse cancers were defined. Fungi are highly enriched in cancers and may serve as potential prognostic markers, as highlighted in red. (b) The unique mycobiome signatures and immune responses in different cancers were analyzed using WGS data from various samples from the TCGA database

Detection and analysis of the mycobiome

Detection methods for fungi

Methods and approaches to detecting, identifying, and analyzing the mycobiome have evolved significantly over the last two decades (Fig. 2). Each technique contributes to understanding its complex community structure and individual or tissue-specific differences. However, the diversity of software tools and the complexity of analysis pipelines make it challenging to access this field. Microbiome studies are divided into three types at the molecule level: microbe, DNA, and mRNA [126]. The corresponding research techniques include culturomics, amplicon, metagenome, and metatranscriptome analyses. Culturomics is a high-throughput method for culturing and characterizing microbes at the microbe level [127,128,129,130,131]. Culturomics-dependent detection methods require pre-selected fungal growth conditions and are susceptible to bacterial and fungal cross-contamination [8, 12, 132, 133].

Fig. 2

Detection and analysis of tumor-associated mycobiome. (a) Different detection methods of mycobiome. The primary sample sources for detection are oral, tumor tissue, and fecal samples. Microbiome studies are divided into microbe, DNA, and mRNA. The corresponding assay techniques include culturome, amplicon (e.g.18s rDNA and ITS), metagenome, metavirome, and metatranscriptome analyses. Each detection technique has its advantages and limitations. (b) Methods for analyzing tumor-associated microbiomes include database data analysis, bulk sequencing, single-cell sequencing, and spatial transcriptome sequencing. Analysis of these data can obtain tissue-specific, cell-type-specific, or spatial-specific microbial signatures. Integrated analysis of various host-mycobiome data promises to provide unique insights into immuno-oncology-microbiome

Methods commonly used for mycobiome are amplicon and metagenome sequencing [134,135,136,137,138]. Amplicon sequencing, the most widely used high-throughput sequencing (HTS) method for microbiome analysis, can be applied to almost all sample types [126]. Amplicon sequencing is performed by PCR amplification of the target region (i.e., 18 S rDNA, 28 S DNA, and ITS regions) followed by high-throughput sequencing, where the sequences are aligned to specific databases for species confirmation and bioinformatic analysis [126]. Amplicon sequencing is cost-effective and can be applied to large-scale studies, and it generates relatively tiny amounts of data for fast and easy-to-execute analyses [126]. Amplicon sequencing can be used for low-biomass specimens or samples contaminated by host DNA [126]. For amplicons, despite the specificity of the highly variable regions, some species may be so close together in these regions, and thus, the specific fragments that can distinguish them may not be in the amplified region [126, 139]. Amplicon sequencing can provide species-level taxonomic resolution when appropriate databases are available; however, the lack of well-curated databases may limit the use of amplicons [8, 126, 140]. In addition, it is sensitive to specific primers and PCR cycles chosen, which may lead to false positive or false negative results in downstream analyses [141].

Metagenomic sequencing provides more information than amplicon sequencing [142]. Metagenomic sequencing requires sequencing all extracted DNA in a sample without PCR amplification using ITS or other specific target sequences [143, 144]. Metatranscriptomic sequencing can analyze mRNAs in microbial communities, quantify gene expression levels, and provide snapshots for functional exploration of microbial communities [145, 146]. Host RNA and other rRNAs should be removed to obtain transcriptional information about the microbiota [126]. In contrast to amplicon sequencing, metagenomic sequencing extends taxonomic resolution to the species or strain level and provides potential functional information [146]. However, it could perform better with low biomass samples or samples heavily contaminated by the host genome [126]. Methods for assessing different regions of fungal DNA may be biased, complicating the comparison of the mycobiome results from other studies. Standardization of species nomenclature, detection methods, and database construction would improve the accuracy of fungal detection and contribute significantly to the current start-up phase of cancer mycobiome research. In addition to sequencing, methods such as fungal staining, immunohistochemistry (IHC), and fluorescence in situ hybridization (FISH) can be used to explore the presence and abundance of fungi within tumors [8, 25].

Methods for analyzing intratumoral mycobiome data

The application of bulk sequencing data from large databases is currently the primary approach for intratumoral mycobiome research, with advantages including: (i) It is possible to identify cell type-specific intracellular mycobiome that match host data in the same tissue; (ii) It is easier to obtain these data than clinical biopsies [147]. The pre-processing of mycobiome profiles from host bulk sequencing data involves four steps [8. 25, 147]:

• 1. Identify microbial reads from ITS sequencing, metagenomic sequencing, whole genome sequencing (WGS), and transcriptome sequencing (RNA-seq) data of tumor/normal tissues [8, 25, 148,149,150].

• 2. Taxonomic analysis of microbial profiles using analyzers (e.g., Kraken2, MetaPhlAn2, SHOGUN, MetaVelvet, MEGAHIT, or PathSeq), including known and novel microbial genomes [151,152,153,154,155].

• 3. Remove contamination signals (e.g., decontam or SourceTracker) [8, 25, 148, 156, 157].

• 4. Normalize decontaminated the mycobiome data [158, 159].

However, interpreting, analyzing, and integrating multidimensional big data into specific settings is difficult in cancer mycobiome research. Differences in the choice of databases, fungal reference genes, and comparison methods can result in significant differences in sequence comparison results. They can also impact the quantification of species/strain abundance. Computerized purification methods, including metagenomics and metabolomics, are becoming increasingly common, and they are used to significantly improve the accuracy and stability of low-biomass microbial assessments [160, 161].

Notably, in the future, the development of emerging technologies may help to infer crosstalk between the mycobiome, immune cells, and cancer cells. The main advantage of microbial profiling by single-cell sequencing over bulk data analysis is the ability of cell barcoding to pair microbes with corresponding somatic cells [162]. Emerging spatially resolved transcriptomics (SRT) technologies provide spatial information unavailable from bulk sequencing and single-cell sequencing technologies [163,164,165,166]. For example, HiPR-FISH delivers a framework for analyzing the spatial ecology of environmental microbial communities at single-cell resolution [165]. Wong-Rolle et al. developed a novel spatial metatranscriptomic method that captures intratumor microbes and hosts transcriptomic data in spatial coordinates [166]. Ghaddar et al. use the computational pipeline SAHMI (Single-cell Analysis of Host-Microbiome Interactions) to probe the microbiome in pancreatic cancer [162]. They identify a subset of tumors with microbes that are associated with key cancer hallmarks, immune activity, and prognosis [162]. SAHMI creates the opportunity to examine patterns of human-microbiome interactions from single-cell sequencing data without the need for additional experimental modifications, thereby generating testable hypotheses about host-microbiome relationships at multiple levels [162]. The framework is not tumor-specific and can be used to study a variety of tissues and disease states, as well as other infectious agents such as viruses, fungi, or helminths.

Mining the microbiome from host bulk, single-cell, and SRT data from host tissue samples can help researchers study cells’ identity and spatial distribution, cancer-associated microbes, the host cell types they interact with, and specific host genes that intracellular microbes can regulate. Integrated analysis of various host-mycobiome data promises to provide unique insights into the IOM axis:

• 1. Data from different types of mycobiome are integrated (e.g., 18s rDNA data and 16s rDNA data).

• 2. Integration of the mycobiome and host data (e.g., 18s rDNA data and single-cell/space sequencing data).

• 3. Integration of gut mycobiome, intratumoral mycobiome, and host data.

Recently, machine learning (ML) and artificial intelligence-based modalities have enabled the better identification of tumor-associated fungal signals [167,168,169]. Notably, the detection and analysis of tumor-associated mycobiome is still in its infancy. In the future, more precise and standardized computational methods will be needed to analyze tumor-associated microbiomes at higher levels of taxonomic resolution (such as at the strain level). Applying these methods has paved the way for understanding host-mycobiome relationships and interactions and how microbes are involved in cancer.

Mechanisms of mycobiome in tumorigenesis

Based on current research evidence, the mycobiome can influence cancer development through several pathways, including the following main mechanisms: (1) genome instability and mutation, (2) tumor-promoting inflammation, (3) immunosuppressive microenvironment, (4) complement system, (5) activating invasion and metastasis, (6) biofilm formation and, (7) bacterial–fungal interactions (Fig. 3).

Fig. 3

The tumor-associated-mycobiome mechanisms involved in triggering oncogenesis and cancer development. (a) Fungal-derived metabolites, such as aflatoxins and acetaldehyde, can directly damage DNA, hinder DNA repair, and induce epigenetic disorders, leading to gene mutations and carcinogenesis. (b) Fungi can exist in TME, interact with immune and cancer cells, and induce an immunosuppressive microenvironment. For example, pathogenic fungi A. fumigatus and C. albicans activate MDSCs function dependent on the Dectin-1/IL-1β signaling axis. MDSCs inhibit the activity of cytotoxic T lymphocytes and the accumulation of PD-1 CD8 T cells through multiple mechanisms. (c) Fungi are recognized by innate immune cells and induce cytokine production through the CARD9/NF-κB, CARD9/ERK, and Dectin-3/JAK-STAT pathways, leading to chronic inflammation and promoting tumor progression. (d) Fungi can also promote cancer progression through the complement system. For example, MBL binds Malassezia in pancreatic cancer to activate the complement cascade reaction. (e) Fungal metabolites, such as candidalysin, nitrosamines, and acetaldehyde, can trigger cancer cell invasion and metastasis. (f) Fungi can form biofilms on mucosal surfaces. (g) Bacteria and fungi can interact in several ways, including physical interactions by direct contact, chemical interactions, environmental modifications, metabolic by-products, and alterations in host responses

Genome instability and mutation

Acquisition of hallmark capabilities in cancer largely depends on a series of alterations in the genomes of neoplastic cells [2]. Two general roles for the tumor-promoting mycobiome are becoming increasingly clear: (1) fungal metabolites, molecules that either damage DNA directly or disrupt the maintenance of genomic integrity, and (2) stress cells in other ways that indirectly impair the fidelity of DNA replication and repair [17, 170,171,172].

Aflatoxins are produced by Aspergillus species such as A. flavus and are carcinogenic, acutely toxic, mutagenic, and teratogenic, and have been classified as Group I carcinogens [173]. Aflatoxins are most known for causing HCC and can also act synergistically with other major HCC risk factors [174,175,176,177]. In addition, aflatoxins may also promote other tumor types, such as lung, kidney, pancreatic, and bladder cancers [17, 173, 175, 178,179,180]. Until now, 20 aflatoxins have been described, with B1, B2, G1, and G2 aflatoxins being the most significant [173]. The mutagenic effects of AFB1 have been the focus of most studies, mainly attributed to the intermediate metabolite AFB1-exo-8, 9 epoxide (AFBO) [181,182,183]. AFBO is a volatile molecule with a high affinity for DNA, forming AFB1-N7-Gua adducts, thus leading to DNA mutations [170]. Three AFBO-induced DNA lesions (AP, AFB1-N7-gua, and AFB1-FAPy) have been known as the main precursors of AFB1 genotoxic and carcinogenic effects [184, 185]. AFB1-FAPy is the most mutagenic because it blocks nucleotide excision repair of DNA and induces various epigenetic changes in repair genes that impede base excision repair (BER) [186,187,188,189]. The higher resistance to DNA repair of the AFB1-FAPy adduct was attributed to its ability to stabilize the double helix—owing to its insertion between the helices [187]. Aflatoxin-induced mutations in the P53 gene can affect cell cycle progression [190,191,192,193]. In addition, AFB1 exerted equally significant or higher effects on cellular function and integrity by inducing oxidative stress (OS) [194, 195]. OS acts directly on DNA, causing oxidative DNA damage, or indirectly via the formation of by-products from lipid peroxidation of membrane phospholipids [194, 195].

The oral colonizing Candida can metabolizes ethanol to acetaldehyde, leading to epithelial dysplasia and oral carcinogenesis [171, 172]. High ethanol-derived acetaldehyde-producing Candida is more prevalent in oral cancer patients than non-oral cancer patients [172, 196, 197]. Acetaldehyde is highly reactive to DNA, binding to DNA to form DNA adducts, thereby changing its physical shape and blocking DNA synthesis and repair [198,199,200]. Acetaldehyde can also bind to proteins and directly cause structural and functional changes. These proteins include glutathione, a protein involved in alcohol-induced OS, and enzymes that contribute to DNA repair and methylation [198]. Both acetaldehyde and ethanol affect DNA methylation, which may lead to changes in the expression of oncogenes and tumor suppressor genes [198]. In addition, Candida albicans produces a cytolytic peptide toxin, Candida haemolysin, which can destroy the oral epithelium by activating the mitogen-activating enzyme (MAPK) pathway, thereby triggering the release of pro-inflammatory cytokines [201, 202]. Other genotoxic fungal metabolites include fumonisin, patulin, and nitrosamines [203,204,205,206,207]. However, the mechanisms of carcinogenic effects of many fungal metabolites are not fully understood. The mycobiome may influence carcinogenesis by secreting a more comprehensive range of biologically active metabolites identified to date [208, 209].

Tumor-promoting inflammation

Inflammation can promote multiple hallmark capabilities by providing bioactive molecules to the TME [2, 4]. Fungi can enhance the host immune response and induce a chronic inflammatory response [2, 4, 8, 210, 211]. Macrophages, dendritic cells (DC), natural killer T cells, and other innate immune cells recognize pathogen-associated molecular patterns (PAMPs) of fungal cell walls, such as β-glucans, chitin, and mannose-associated complexes, through pattern recognition receptors (PRRs) [212]. Activation of PRRs triggers signal transduction cascades that drive antifungal immunity with four main types: c-type lectin receptors (CLRs), toll-like receptors (TLRs), retinoic acid-inducible gene (RIG)-I-like receptors (RLRs), and NOD-like receptors (NLRs) [212]. CLRs (Dectin-1、Dectin-2、Dectin-3, and Mincle) are the significant PRRs that mediate the recognition of fungal pathogens [213]. CARD9 protein, one of the vital intracellular modules, is mainly expressed in myeloid cells, especially in macrophages and DCs [214]. Fungal recognition receptor triggers downstream signaling via the common adaptor protein CARD9 and the kinase SYK (Fig. 4). It is closely associated with immune cell infiltration, activation, and pro-inflammatory cytokine production [215,216,217,218,219].

Fig. 4

Tumor-associated-mycobiome regulates the physiological and immune responses of cancer through different signaling pathways. Dectin-1, Dectin-2, Dectin-3, and Mincle are CLRs that interact with Syk to activate PLCγ and then enhance the function of PKCδ that is critically involved in CARD9 phosphorylation at T231 with the aid of Vav proteins in the coiled-coil domain, allowing the formation of CBM complex (CARD9-BCL10-MALT1). The CBM complex activates NF-κB and MAPK pathways, subsequently increasing inflammatory cytokines production. CARD9 also binds with RAS-GRF-1 and H-RAS to activate ERK pathways. CARD9 can free Rac1 from LyGDI to promote ROS. Fungi induce NLRP3 inflammasome activation in MDSCs via Dectin-3, which depends on glycogen metabolism-dependent glycolysis. Fungi promote transcription of NLRP3, Pro-caspase-1, and IL-1β genes through activation of the JAK-STAT1 signaling pathway, whereas mtROS, as a second activation signal, is required for fungal-induced NLRP3 inflammasome activation. IL-1β production is regulated by two steps: transcription and maturation. Typical NF-κB first induces IL-1β into an inactive precursor called pro-IL-1β, and then pro-IL-1β is cleaved into active IL-1β by active caspase-1. SyK, spleen tyrosine kinase; PLCγ, phosphoinositide phospholipase Cγ; PKCδ, protein kinase Cδ; JAK, Janus kinase; STAT; NF-Κb, nuclear factor-κB; STAT, signal transducer and activator of transcription

Wang et al. investigated the contribution of CARD9-dependent innate immunity on the development of colitis-associated cancer (CAC) [125]. They found that CARD9-deficient macrophages showed impaired fungicidal abilities, which led to increased fungi, especially C. tropicalis, in the gut [125]. The increased fungi induced MDSC accumulation and promoted the development of CAC, while antifungal treatment ameliorated CAC in Card9−/− mice [125]. Zhang et al. further investigated the underlying molecular mechanisms: C. tropicalis induced NLRP3 inflammasome activation through Dectin-3 in MDSCs [211]. Mechanistically, C. tropicalis significantly enhanced the levels of glycolysis dependent on glycogen metabolism in MDSCs, which was required for NLRP3 inflammasome activation [211]. C. tropicalis promoted transcription of Nlrp3, Pro-caspase-1, and IL-1β genes by activating the JAK-STAT1 signaling pathway [211]. Pharmacological blockade of NLRP3 inflammasome activation ameliorates C. tropicalis-associated colon tumorigenesis [211]. Abnormal colonization of C. albicans reprogrammed HCC metabolism and promotes NLRP6-dependent tumor progression [87]. Enrichment of C. albicans in the saliva of NSCC patients was associated with increased inflammatory cytokines ΙL-1β and IL-8 [97]. However, Malik et al. find that recognizing commensal gut fungi, sensed via the CARD9-Syk signaling axis, is protective in the context of inflammation-associated cancer [76]. IL-18 maturation downstream from inflammasome activation promotes epithelial barrier restitution and IFN-γ production by intestinal CD8 + T cells [76].

These contradictory phenomena revealed the dual role of inflammasome, ΙL-1β, and IL-18 in cancer. NLRP3 inflammasome facilitates the invasion of myeloid cells like myeloid-MDSC and tumor-associated macrophage (TAM) into TMEs, which benefits tumor progression [220]. Inflammasome suppression elevated CD8 + T and CD4 + cell infiltration, decreasing TAM infiltration and amplifying the therapeutic effect of PD-L1 inhibition in tumors with high levels of inflammasome signaling activity [221]. In a subcutaneous mouse melanoma model, NLRC4 is essential for cytokine production in TAM and the development of IFN-γ-producing CD8 + and CD4 + T cells, which impede tumor progression [222]. IL-1 contributes to establishing a smoldering inflammatory milieu in TME by inducing pro-inflammatory mediators and chemokines [223]. IL-1β and its receptor are essential drivers of mesenchymal and epithelial primary carcinogenesis and metastasis [224, 225]. The mechanisms underlying the pro-tumor role of IL-1β are complex, including recruitment of myeloid cells and immunosuppression, promotion of angiogenesis and endothelial cell activation, and lymphoid cell skewing [223]. IL-18 enhances the Th1 immune response and activates T and NK cells to generate IFN-γ, potentially assisting tumor immunity [226]. IL‐18 dramatically enhances monocytic MDSC (M-MDSC) through CD11b (-) bone marrow progenitor cell differentiation, suppressing in vitro T cell expansion and IFN production [227]. These effects may provide therapeutic targets for tumor therapy; for example, blocking IL-1β reverses the immunosuppression in mouse breast cancer and synergizes with anti–PD–1 for tumor abrogation [228,229,230].

Immunosuppressive microenvironment

Fungal dysbiosis induced MDSC release and increased Th17 differentiation, significantly contributing to immunosuppression and tumor development [231, 232]. MDSC impairs the proliferation and activation of T cells through multiple mechanisms, including elevated levels of reactive oxygen species (ROS), nitric oxide (NO), arginase activity, and cyclooxygenase 2 (COX2) [233]. Th17 cells, an antigen-specific subset of CD4 T cells, contribute to coordinating immune responses against extracellular fungi [231,232,233]. Th17 cells, initially activated by C. albicans, broaden their response to target other fungi through cross-reactivity [234]. Pathogenic fungi A. fumigatus and C. albicans activate MDSC function dependent on the Dectin-1/IL-1β signaling axis [235]. The impaired fungicidal function of Card9-/- macrophages leads to increased fungal loads and accumulation of MDSCs in tumor tissue [125]. In the LUAD mouse model, A. sydowii promotes tumor progression via IL-1β-mediated MDSCs expansion and activation, suppressing cytotoxic T lymphocyte cell activity and accumulating programmed death protein 1 (PD-1) CD8 T cells [29]. This is mediated by IL-1β secretion through the β-glucan/Dectin-1/CARD9 pathway [29]. CLR/Syk/CARD9 signaling drives proinflammatory cytokine and chemokine production, inflammasome activation, myeloid phagocyte recruitment, effector function, and Th17 cell differentiation [215, 236,237,238,239].

PDAC is infiltrated by pro-tumorigenic immune cells that include TH2 and ILC2 cells, which, via their cytokine networks, foster a pro-tumorigenic program that leads to PDAC progression [240]. TH2 cells infiltrate the pancreas in the early stage of tumorigenesis and secrete type 2 cytokines (IL-4 and IL-13), which support TAMs/M2-macrophage-type immune response, in addition to a trophic action on cancer cells [240]. Alam et al. identified a proinflammatory cytokine, IL-33, released as a chemoattractant for type 2 immune cells in response to intratumoral mycobiome [114]. Fungi can synergize with biochemical factors such as ROS and OS to promote the secretion of IL-33 [114]. Antifungal treatment can significantly decrease the progression of PDAC and increase survival [114]. Notably, fungal components can be detected early in PDAC onset when large-scale OS has not been detected in TME [114]. This raises the question of whether the mycobiome initiates type-2 immune responses in PDAC. Whether fungal reprogramming is a cause or a consequence of tumorigenesis is challenging to answer fully, and more precise studies are needed to assess this dynamic crosstalk.

Complement system

The complement system is a cascade of serine proteases [241]. Three significant pathways activate the complement system: (1) the classical pathway, via antigen-antibody complexes; (2) the alternative pathway, via any permissive surfaces; and (3) the lectin pathway, via binding of pattern-recognizing mannose-binding lectins (MBLs) to carbohydrate ligands on the surface of pathogens [242,243,244]. The convergence point for all complement activation pathways is the formation of the C3 convertase complex on the surface of the target cell, which then cleaves the C3 molecule into C3a and C3b [245]. Propagation of complement activation by C3 convertase results in the generation of the C5 convertase complex on the cell surface. C5 convertase then cleaves C5 to C5a and C5b [245]. The main consequences of complement activation are tagging cells by C3b degradation products for phagocytosis, chemotaxis of inflammatory cells in response to C3a and C5a, and MAC-mediated cell lysis [245]. The complement system links innate immunity and adaptive immunity, as complement deficiency impairs B-cell and T-cell responses [246,247,248,249,250,251,252].

Aykut et al. find that Malassezia accelerates pancreatic oncogenesis [24]. Based on TCGA transcriptomics data, MBL expression was associated with reduced survival in PDA, so the authors postulated that fungi may promote tumorigenesis via MBL activation [24]. Animal experiments supported the hypothesis: MBL-null mice exhibit delayed oncogenic progression; fungal ablation therapy did not provide tumor protection in MBL-null mice [24]. Similarly, Malassezia, which binds C-type lectin receptors, failed to accelerate tumor progression in MBL-null mice [24]. Like MBL, C3 expression was associated with a trend toward reduced survival [24]. Robust expression of C3a in pancreata of mice, and this was nearly absent in MBL-null mice [24]. These data indicate pancreatic fungi may promote tumor growth through the MBL-C3 axis. However, this study did not clarify the direct link between fungi and the complement system or elucidate the underlying molecular mechanisms. Nevertheless, this outstanding study suggests that the interaction between the complement system and the mycobiome may become a novel therapeutic target for pancreatic cancer in the near future.

Indeed, two important reasons justify studying the role of complement activation in cancer progression: (1) the complement system is an essential component of the inflammatory response, and inflammation is involved in various stages of tumorigenesis and cancer progression [241, 245, 253]; (2) complement activation regulates adaptive immune response and might have a role in regulating T cell response to tumors [246,247,248]. The C3 complement cascade has been investigated in cancers and is potently oncogenic via diverse mechanisms, including increasing tumor cell proliferation, motility, and invasiveness and corrupting adaptive immune responses [241, 245].

Activating invasion and metastasis

The mycobiome may be involved in various pathological processes, thereby facilitating the establishment of metastatic ecological niches [8, 102,103,104,105,106]. Dohlman et al. found that in colon tumors, Candida may be linked to loss of gut epithelial barrier function, metastasis, and translocation of fungal cell components from the GI tract into the bloodstream [8]. Increased tight junction permeability and loss of epithelial barrier function are important risk factors for metastasis [254, 255]. C. albicans enhances intestinal inflammation through an IL-1-dependent mechanism [12, 255]. Chronic inflammation contributes to transforming intestinal epithelial cells to a mesenchymal-like state [256]. To successfully metastasize, tumor cells must acquire invasive and stem cell-like properties [2]. Cancer cells accomplish this process by hijacking the developmental program of EMT [257,258,259]. Activation of EMT causes epithelial cells to lose their apical-basal polarity and cell-cell junctions and gain invasive and migratory capabilities, which are characteristics of mesenchymal cells [260, 261].

Candidalysin is a cytolytic peptide toxin secreted by opportunistic C. albicans [262]. In 2016, Moyes et al. first identified that Candidalysin could mediate transport [263]. This toxin could mediate disruption of epithelial barrier function and immune activation via the positively charged C-terminus, triggering an inward current concomitant with calcium influx [263]. C. albicans also produce nitrosamine and metabolize ethanol into acetaldehyde [172]. Geetha et al. found that acetaldehyde could induce synergistic ROS production in Caco-2 cells through a Ca2+-dependent mechanism, leading to tight junction disruption and barrier dysfunction [264]. Of note, other species, such as C. glabrata and C. tropicalis, can produce significant amounts of acetaldehyde, even up to carcinogenic levels [265].

Biofilm formation

The relationship between fungal biofilm and carcinogenesis has seldom been reported. Alnuaimi et al. found that Candida isolated from oral cancer patients exhibited significantly higher biofilm mass, biofilm metabolic activity, phospholipase, and proteinase activity than isolates from patients with non-oral cancer [196]. Biofilms are tightly packed communities of cells attached to biotic and abiotic surfaces [266, 267]. Biofilms are essential for the survival of fungi, especially C. albicans [266,267,268,269,270]. Biofilm formation follows three stages: yeast cell adsorption and adherence, hyphae development with microcolony formation and extracellular matrix (ECM) production, and maturation with cell dispersal for potential dissemination to secondary infection sites [271, 272]. Biofilms enhance protection against the immune system and antifungal therapy. Factors like hyphal morphology, ECM, resistance-associated gene expression, fungal-bacteria interactions, and biofilm extracellular vesicles all contribute to this mechanism [271, 273, 274]. Macromolecules, such as proteins, lipids, and various polysaccharides, including α-mannans and β-glucan, as well as plentiful host cells such as neutrophils, indicating host engagement in the formation and maintenance of these biofilms [266,267,268,269,270,271,272,273]. Some Malassezia species, such as Malassezia pachydermatis and Malassezia furfur, have also been found to be capable of forming biofilms in vitro, which also have the capacity for pathogenesis and drug resistance [275, 276]. Aspergillus Cryptococcus and many other fungi also possess the ability to form biofilms and act differently in the pathological process [277, 278].

Bacterial–fungal interactions

Commensal fungi and bacteria coexist in the gut, and their patterns of interaction may be altered in the disease state, which could reflect their potential roles in CRC [77, 78, 81, 85]. Bacterial-fungal co-abundance was noted in the mycobiome within GI tumors, where fungi clustered into two groups around a core of C. albicans or Saccharomyces cerevisiae [8]. In tumors of the lower GI tract, Candida was positively associated with Dialister and was negatively associated with Ruminococcus, Akkermansia municipal, and Barnesiella intestinihominis [8]. Through extensive functional metagenomic analysis, Liu et al. revealed that bacterial-fungal interactions could contribute to CRC pathogenesis by upregulating D-arginine and D-ornithine and stimulating the butanoate metabolism pathway [81]. The CRC driver-passenger model suggests that F. nucleatum promotes colorectal tumourigenesis, and butanoic acid from the butanoate metabolism pathway is critical in supporting the TME [279]. These metabolic disturbances caused by bacteria, fungi, or their associations may indicate different host-microbe interactions critical for CRC progression.

Bacteria and fungi can interact in several ways, including physical interactions by direct cell-cell contact, chemical interactions through the secretion of small molecules typically involved in quorum sensing, environmental modifications (e.g., pH changes), use of metabolic by-products, and alterations in host responses [280]. This exchange allows microbial interactions to maintain ecosystem resilience or drive ecological dysregulation states [280,281,282,283]. For example, Bacteria can exploit the microenvironment established by fungi for their growth [280,281,282,283]. This aggregation synergy facilitates colonization of the oral and gastrointestinal tracts, enhances pathogenesis, and maintains local inflammation, thereby promoting tumorigenesis [6, 280, 281]. The hypoxic microenvironment produced by C. albicans in its biofilm favors the growth of anaerobic bacteria such as Clostridium perfringens and Bacteroides fragilis [284]. In the presence of antibiotics, the release of peptidoglycan from bacteria promotes hyphae formation, mucosal invasion, and extraintestinal dissemination of C. albicans [285, 286]. Understanding the mechanisms of these interactions is critical to the prevention and management of multiple microbial infections and may have potential value in identifying new targets for future anticancer therapies.

The mycobiomes as cancer biomarkers

Although research on cancer-associated mycobiome is just beginning, the mycobiome analysis may have clinical value in predicting disease. Shotgun macrogenomic analysis revealed the presence of distinct fungal clusters in CRC and control groups [23, 77, 78, 287]. Early and advanced CRC have different fungal compositions, with higher diversity observed in advanced CRC [23]. Several Candida species were enriched in tumor samples at multiple GI loci, and tumor-associated Candida DNA predicted reduced survival [8]. Analysis of salivary mycobiome in a cohort of OSCC patients identifies higher salivary carriage of Malassezia as an independent and favorable predictor of overall survival [288, 289]. Saliva mycobiota could distinguish PDAC patients from healthy controls [290]. Aspergillus and Cladosporium achieved high classification powers to discriminate between PDAC patients and healthy controls [290]. As the disease progressed, alpha diversity was reduced, and the gut microbiota differed significantly between patients with different stages of melanoma [86]. Patients with TNM stage III-IV intrahepatic cholangiocarcinoma (ICC) had substantially more C. albicans than those with stage I-II [291]. Fungal DNA of blood origin has recently permitted modest differentiation between different cancer types and healthy controls, suggesting that fungal signatures obtained by liquid biopsy may be associated with tumor progression and stage [25]. Fungal signaling can be indirectly exploited in cancer stratification by measuring serum immunoglobulin levels targeting different fungal species [292, 293]. Notably, diagnostic biomarkers from different kingdoms were superior to single kingdoms [78, 81]. Lin et al. demonstrated that combining fungal and bacterial biomarkers was more accurate than panels with pure bacterial species to discriminate between CRC patients and healthy individuals [78]. Liu et al. discovered bacterial and non-bacterial markers and evaluated their performance in detecting CRC patients across cohorts [81]. They found fungal, archaeal, and viral species could distinguish CRC patients from healthy controls [81]. The predictive value of different kingdom models varied, and the bacteria- and fungi-based models, respectively, showed superior accuracy over the archaea- and virus-based models generally [81]. Although the underlying mechanism has not been demonstrated, the role of multi-kingdom microbes in cancer markers provides strong support for future studies of the cancer microbiome. However, the feasibility of these methods is currently limited by the high cost of sequencing, the need for standardized protocols, and the substantial variability of mycobiomes in terms of geography, diet, and other environmental factors.

The mycobiome and cancer therapy

Cancer treatment through modulation of the mycobiome or using exogenous microbiota is one of the potentially valuable translational aspects. It may optimize the decision-making process for oncology therapy. In bladder cancer patients, an increased prevalence of Hypocreales, Agaricomycetes and Saccharomycetes in the faeces was noted in chemotherapy non-responders. At the same time, amounts of Saccharomcetales were documented in chemotherapy-responding patients [26]. The mycobiome was clustered by chemotherapy response status, suggesting that different mycobiome characteristics may be associated with treatment responsiveness [26]. One possible interpretation is that patients with a ‘favourable’ mycobiome composition (eg, high diversity, and low abundance of Agaricomycetes and Sacchaaromycetes) may have enhanced systemic immune response to chemotherapy through antigen presentation [26]. Robinson et al. characterized the oral mycobiome in the setting of remission-induction chemotherapy for acute myeloid leukemia [294]. Patients receiving high-intensity chemotherapy had lower α-diversity and a greater decrease in Malassezia levels than those receiving low-intensity chemotherapy [294]. Although causality was not further explored, these findings strongly support the timely evaluation of cancer patients through clinical trials or exploratory studies.

Shiao et al. found that intestinal fungi regulate antitumor immune responses following radiotherapy (RT) in mouse models of breast cancer and melanoma, whereas fungi and bacteria have opposite effects on these responses [37]. Depletion of commensal bacteria reduces the efficacy of radiation therapy [37]. Antifungal treatment enhanced the ability of RT to delay tumor growth and improve survival in mice [37]. Antibiotic treatment expanded specific communities of commensal fungi of the Saccharomycetales order, highlighting an increase in the genera Saccharomyces and Candida [37]. Experiments revealed that C. albicans overgrowth increases depleted CD8 T cells, ultimately impairing the antitumor response to RT and reducing survival [37]. This phenotype was Dectin-1-dependent and was accompanied by a more immunosuppressive TME [37]. Fungal depletion combined with RT significantly increased the total CD8 T cell population and CTLs while decreasing the T cell depletion phenotype and suppressive TAMs [37]. The findings that fungi and bacteria have opposing but dual importance in tumor responsiveness to RT opens up a novel area of exploration that needs to be validated in clinical trials using FDA-approved antifungal drugs in combination with RT. However, Mäkinen et al. observed that the prevalence of salivary Candida was largely unaffected in the radiotherapy-treated and untreated patient groups [295]. Although not explored further, this study suggests that there may be differences in the correlation between RT and fungi in different ecological niches. These differences may be related to several factors, such as disease status, sample source, combined treatment modalities, and host immunity.

Using biological response modifiers (BRMs) to enhance tumor defense response is one of the most attractive alternatives to cytotoxic drugs [296, 297]. As a pattern recognition molecule, fungal β-glucan has been shown to trigger phagocytosis, superoxide production by NADPH oxidase, and the production of inflammatory cytokines in macrophages [298,299,300]. Liu et al. found that particulate yeast-derived β-glucan reduced tumor load by inducing the conversion of polarized M2 macrophages or immunosuppressive TAM to an M1-like phenotype with potent immune-stimulating activity [301]. Beta-glucan purified from mutated Saccharomyces cerevisiae inhibits colon cancer and melanoma metastasis via activating macrophages and NK cells [296]. The use of β-glucan resulted in the reversion of the tolerogenic melanoma microenvironment to an immunogenic microenvironment, allowing M1-type activation of macrophages, induction of proinflammatory cytokines/chemokines including IFN-γ, TNF-α, CXCL9, and CXCL10, and enhanced PD-L1 expression [302]. Although fungal modulation of the TME has been demonstrated as a new therapeutic modality, more prospective studies are needed for confirmation.

Fecal microbiota transplants (FMT) apply a fecal suspension from healthy donors into the patient’s gut to reconstitute the intestinal microbiota. FMT is an approved treatment for recurrent or refractory Clostridioides difficile infections (CDI) with 90% therapeutic efficacy [303, 304]. This success has inspired the expansion of its application to other diseases such as inflammatory bowel disease (IBD), graft versus host disease (GVHD), metabolic syndrome, and immune checkpoint inhibitor-associated colitis (ICIAC) [305,306,307,308]. At present, the exploration of mycobiome in FMT is still in its infancy. In 2020, Leonardi et al. found that high Candida abundance pre-FMT was associated with a clinical response, while decreased Candida abundance post-FMT indicated a reduction in the disease severity, by analyzing samples from a large placebo-controlled trial of FMT for ulcerative colitis [309]. High pre-FMT Candida was associated with increased bacterial diversity post-FMT and the presence of genera linked to FMT responsiveness [309]. These results suggest that fungi may influence the transplantation rate of FMT. Zuo et al. found that C. albicans reduced FMT efficacy in a mouse model of CDI, whereas antifungal therapy restored its efficacy, supporting a potential causal relationship between gut fungal dysbiosis and FMT outcome [310].

FMT has not been extended to the treatment of malignancies, except for some attempts to improve the effectiveness of immunotherapy. In melanoma, the immune response triggered by ICI therapies shapes a host niche favorable to gut taxa that synergistically supports immune cell function and tumor recognition while being well-adapted to host conditions [311]. Harnessing or inducing (e.g., FMT) this ICI-conducive gut microbiota will pave the way for more effective tumor treatment. Kim et al. conducted a clinical trial combining an anti-PD-1 inhibitor with FMT from an anti-PD-1 responder in 13 patients with anti-PD-1-refractory advanced solid cancers [312]. They found that FMT improved the efficacy of anti-PD-1 inhibitors in unresectable or metastatic solid cancers refractory to anti-PD-1 inhibitors [312]. A meta-analysis illustrated the response of microbes from different kingdoms, including fungi, to ICI treatment for various cancer types, with results highlighting the critical involvement of trans-kingdom microbes in ICI treatment and the role of microbes in cancer immunotherapy [27]. In addition, as a probiotic, Saccharomyces boulardii (Sb) has been applied to human gastrointestinal disorders such as diarrhea and inflammatory IBD [313,314,315]. In human colonic cancer cells, Sb prevented EGF-induced proliferation, reduced cell colony formation, and promoted apoptosis [316]. These emerging fields are characterized by exciting research directions that, in the future, may lead to innovative options for interesting cancer interventions (Fig. 5). From a holistic microbial ecology perspective, a therapeutic approach based on core fungal reconstitution should go beyond probiotic supplementation alone [27, 311, 312, 316].

Fig. 5

Tumor-associated mycobiome and cancer therapy. (a) The fungus can interact with chemotherapy. Elevated fungal diversity in patients who responded to chemotherapy compared to non-responders. However, high-intensity chemotherapy can reduce alpha diversity and fungal abundance, e.g., Malassezia. A bidirectional effect may exist between cancer therapies and human-mycobiome. For example, antifungals may kill pathogenic fungi but may also lead to microbiome dysbiosis, favoring the invasion and colonization of other opportunistic pathogenic fungi. (b) Fungal-bacterial interactions may affect the efficacy of radiotherapy. Fungal ablation induces an increase in CD8 T cells, which increases the RT efficacy, whereas bacterial ablation induces an increase in M2 macrophages, which decreases the RT efficacy. (c) Components of fungi, such as β-glucan, can enhance the efficacy of immunotherapy. β-glucan can promote the conversion of M2-type macrophages to M1-type, secrete cytokines and chemokines such as INF-γ, TNF-α, IL-12, CXCL9 and CXCL10, and increase the expression of PD-L1, which promotes anti-tumour therapeutic effects. Systemic administration of β-glucan induces T-lymphocyte activation, secretes INF-γ, and promotes anti-tumour immunity. (d) Gut mycobiome can influence the therapeutic efficacy of FMT. High Candida abundance pre-FMT is associated with clinical response and increased bacterial diversity post-FMT. (e) S. boulardii, a probiotic, inhibits EGF-induced proliferation, reduces colony formation, and promotes apoptosis

Challenges and perspectives

Advances in metagenomics and the accumulation of experimental data have driven more profound studies of the mycobiome. However, such multidisciplinary studies face numerous technical and conceptual limitations, and recognizing and addressing these limitations can contribute to understanding the mycobiome and its contribution to cancer.

The low fungal biomass presented many challenges. Analysis of fungal communities in human and mouse models revealed that despite bioinformatic analysis, they may still show different compositional differences [75,76,77,78]. These differences in results are mainly related to the procedure and choice of method used for fungal testing. (i) Lack of controllability of data sources. Samples are susceptible to environmental and reagent contamination during collection and experimentation, which will greatly reduce the reliability of analytical results. Few studies described the procedures in detail for controlling sample contamination. More elaborate samples and cohorts are needed to eliminate potential contamination to help characterize and understand the role of fungi. (ii) Limitations of fungal databases, including non-standardizing fungal species nomenclature and annotation, inadequate data collection and analysis methods, and data and resource reproducibility crises. (iii) Lack of harmonisation of bioinformatics analysis tools. Standard bioinformatics techniques must be developed to eliminate the discrepancies associated with using different computational channels [126]. (iv) The reproducibility of the results was not validated, and samples obtained from a sterile environment for repeat sequencing were required to verify the accuracy of the pan-cancer analysis. Future research will require greater coordination and more data sharing, careful elimination of potential data and sample contamination, uniform denoising and genetic database comparisons.

Differences in specimen source, population and ethnicity, cancer type, and pathological subtype may also contribute to study difficulties and differences in results. Variations in specific environmental configurations pose a significant challenge in generalizing results when distinguishing between signal and noise, such as individual host characteristics, genetic characteristics, and environmental factors. One common feature of mycobiome studies is a high degree of inter-individual variability. In 2017, the NIH launched a large-scale human microbiome project to investigate the mycobiome of 317 fecal samples from healthy volunteers based on sequencing of ITS2 and 18 S rRNA regions [57]. The survey found a much lower diversity of fungi than bacteria in the healthy human gut, with high inter-individual variability of fungi, indicating instability in the flora [57]. Extrinsic factors that lead to microbiome perturbations, such as antibiotics or antifungal treatment, provide opportunities for rare fungal species to emerge as immunologically dominant taxa with the potential to cause immune-mediated disorders [57]. Nevertheless, the mycobiome heterogeneity may still offer benefits, such as analysis of differences in specific mycobiome in similar disease conditions, and this heterogeneous profile can help predict individualized cancer-related changes.

Studies on cancer mycobiomes are not limited to identifying correlations but should move towards establishing causality, collaboration, and mechanistic research. Correlation studies are essential for establishing causal investigations. Without sufficient experimental validation, researchers must exercise caution and refrain from exaggerating causality, ensuring that conclusions are accurate and reliable. Establishing causal links between fungi and cancer phenotypes remains a significant challenge. This requires controlled experiments and extensive validation to demonstrate that specific fungi (1) are physically present and (2) directly or indirectly influence tumor development or progression [35]. Establishing animal models (e.g., germ-free mice) can simulate the cancer state and validate the role of whole microbiome configurations, defined alliances, or individual microorganisms. Experimental vehicles with immune and microbiota ecotopes or metabolites, such as organoids, can help validate the cancer-promoting effects of fungi. In addition, developing or applying new technologies could determine the spatial distribution of these organisms in areas where tumors develop.

Several studies reported inter-kingdom interactions between microbial communities in cancer, and they suggested that multi-species communities collectively, rather than individually, contribute to the development of ecological dysbiosis [25, 77, 78]. However, most studies of fungal and bacterial interactions within tumors are mainly based on metagenomics data. The clinical significance and relevance of fungal biomarkers in cancer has not been clarified. A multi-omics approach combining metagenomics, metatranscriptomics, and metaproteomics experiments on the same set of samples - balancing all cancer stages - will provide higher confidence in biomarkers. Evidence from several clinical and preclinical studies highlights the importance of considering conventional chemotherapy or immunotherapy in combination with microbiome modulation regimens, as these are potentially effective strategies for managing cancer. Although the mycobiome has provided tantalizing results in some mouse tumor models, they have not yet been translated into clinical therapeutic interventions in humans. Microbiome work should focus on understanding the complex and dynamic relationships between these communities (e.g., bacteria and fungi), which will facilitate optimizing these interactions for beneficial applications.