Identification of a brand intratumor microbiome signature for predicting prognosis of hepatocellular carcinoma

PURPOSE

Given that prognosis of hepatocellular carcinoma (HCC) differs dramatically, it is imperative to uncover effective and available prognostic biomarker(s). The intratumor microbiome plays a significant role in the response to tumor microenvironment, we aimed to identify an intratumor microbiome signature for predicting the prognosis of HCC patients accurately and investigate its possible mechanisms subsequently.

METHODS

The TCGA HCC microbiome data (TCGA-LIHC-microbiome) was downloaded from cBioPortal. To create an intratumor microbiome-related prognostic signature, univariate and multivariate Cox regression analyses were used to quantify the association of microbial abundance and patients' overall survival (OS), as well as their diseases specific survival (DSS). The performance of the scoring model was evaluated by the area under the ROC curve (AUC). Based on the microbiome-related signature, clinical factors, and multi-omics molecular subtypes on the basis of "icluster" algorithm, nomograms were established to predict OS and DSS. Patients were further clustered into three subtypes based on their microbiome-related characteristics by consensus clustering. Moreover, deconvolution algorithm, weighted correlation network analysis (WGCNA) and gene set variation analysis (GSVA) were used to investigate the potential mechanisms.

RESULTS

In TCGA LIHC microbiome data, the abundances of 166 genera among the total 1406 genera were considerably associated with HCC patients' OS. From that filtered dataset we identified a 27-microbe prognostic signature and developed a microbiome-related score (MRS) model. Compared with those in the relatively low-risk group, patients in higher-risk group own a much worse OS (P < 0.0001). Besides, the time-dependent ROC curves with MRS showed excellent predictive efficacy both in OS and DSS. Moreover, MRS is an independent prognostic factor for OS and DSS over clinical factors and multi-omics-based molecular subtypes. The integration of MRS into nomograms significantly improved the efficacy of prognosis prediction (1-year AUC:0.849, 3-year AUC: 0.825, 5-year AUC: 0.822). The analysis of microbiome-based subtypes on their immune characteristics and specific gene modules inferred that the intratumor microbiome may affect the HCC patients' prognosis via modulating the cancer stemness and immune response.

CONCLUSION

MRS, a 27 intratumor microbiome-related prognostic model, was successfully established to predict HCC patients overall survive independently. And the possible underlying mechanisms were also investigated to provide a potential intervention strategy.

Immuno-oncology-microbiome axis of gastrointestinal malignancy

Research on the relationship between the microbiome and cancer has been controversial for centuries. Recent works have discovered that the intratumor microbiome is an important component of the tumor microenvironment (TME). Intratumor bacteria, the most studied intratumor microbiome, are mainly localized in tumor cells and immune cells. As the largest bacterial reservoir in human body, the gut microbiome may be one of the sources of the intratumor microbiome in gastrointestinal malignancies. An increasing number of studies have shown that the gut and intratumor microbiome play an important role in regulating the immune tone of tumors. Moreover, it has been recently proposed that the gut and intratumor microbiome can influence tumor progression by modulating host metabolism and the immune and immune tone of the TME, which is defined as the immuno-oncology-microbiome (IOM) axis. The proposal of the IOM axis provides a new target for the tumor microbiome and tumor immunity. This review aims to reveal the mechanism and progress of the gut and intratumor microbiome in gastrointestinal malignancies such as esophageal cancer, gastric cancer, liver cancer, colorectal cancer and pancreatic cancer by exploring the IOM axis. Providing new insights into the research related to gastrointestinal malignancies.

Intratumor microbiome in cancer progression: current developments, challenges and future trends

Cancer is a complicated disease attributed to multifactorial changes, which causes difficulties with treatment strategies. Various factors have been regarded as the main contributors, and infectious etiological factors have recently attracted interest. Several microbiomes contribute to carcinogenesis, cancer progression, and modulating cancer treatment by inducing cancerous epithelial cells and chronic inflammation. Most of our knowledge on the role of microbiota in tumor oncogenesis and clinical efficiency is associated with the intestinal microbiome. However, compelling evidence has also confirmed the contribution of the intratumor microbiome in cancer. Indeed, the findings of clinical tumor samples, animal models, and studies in vitro have revealed that many intratumor microbiomes promote tumorigenesis and immune evasion. In addition, the intratumor microbiome participates in regulating the immune response and even affects the outcomes of cancer treatment. This review summarizes the interplay between the intratumor microbiota and cancer, focusing on the contribution and mechanism of intratumor microbiota in cancer initiation, progression, and potential applications to cancer therapy.

A comprehensive analysis of intratumor microbiome in head and neck squamous cell carcinoma

PURPOSE

Human microbiome has been considered as the second genome of our body. The intratissue/intratumor microbiome analysis is a relatively new field and deserves more attention. In this study, we conducted a comprehensive analysis of microbiome signatures of head and neck squamous cell carcinoma (HNSC).

METHODS

The intratumor microbiome profiling and clinicopathological information about a total of 177 HNSC samples, including 155 tumors and 22 adjacent normal tissues, were obtained from The Cancer Microbiome Atlas (TCMA) and The Cancer Genome Atlas (TCGA) databases. We identified the microbes that differed between tumors and normal tissues, and assessed their utility values as diagnostic biomarkers. The microbiome signatures under different conditions of clinicopathological parameters were also analyzed.

RESULTS

The intratissue microbiome profiles differed between tumor and normal samples of HNSC. The composition of four, six, and six microbes changed in tumors compared to normal tissues at the phylum, order, and genus levels, respectively (P < 0.05). Eight of the differential microbes performed well in distinguishing tumors from normal tissues (AUC > 0.7, P ≤ 0.001). The microbiome signature was found to be associated with tumor clinicopathological characteristics such as host-gender, host-age, tumor stage, and neoplasm histologic grade.

CONCLUSION

Overall, our results revealed an intratissue microbiome signature of HNSC. We concluded that the intratumor microbiome signature may also reflect human biology in both healthy and disease status, and provide novel perspective for microbiota research about their roles in tumors.