Microbes such as bacteria, viruses, fungi, and protists can be involved in practically every stage of cancer formation and progression, as well as diagnosis, therapy, prognosis, immune function, as well as drug response. Human papillomavirus (HPV) infection, for example, is strongly linked to cervical cancer and is implicated in others, including oropharyngeal cancer, anal cancer, and penile cancer. Several studies have also revealed close links between bacteria and cancer, such as those between helicobacter pylori and gastric cancer, hepatitis C virus (HCV) and primary liver cancer, intestinal flora, and colorectal cancer. According to new research, different cancers have different microbial fingerprints, and microbial DNA in the blood can be used to identify several types of cancers with acceptable accuracy.
Experiments to confirm the link between microbes and cancer, as well as the roles of microbes in cancer diagnosis and therapy, particularly immunotherapy, prognosis, and drug response, are costly and time-consuming. As a result, it's critical to develop high-level computational methods that will allow researchers to prioritize high-confidence microbe-directed hypotheses for further experimental validation in cancer studies. This Research Topic aims to: (1) discover new microbes related to cancers; (2) explore the mutual interactions between microbes and molecules in cancer patients at genetic, gene expression, protein, and metabolic levels; (3) identify significant associations between microbes and key functions in cancers, such as immune response and cancer initiation, development and progress; (4) elucidate differences in microbial compositions among tumor tissue, adjacent normal and normal tissues, and diagnose cancers based on the microbial constitution of cancer patients; (5) explore differences in microbial compositions among the distinct status of traits such as cancer grades, and tumor vs normal tissues; (6) predict prognosis of cancer patients using computational models based on microbes, molecular and pathological data; (7) reveal associations between microbes and drugs for relevant cancers.
This Research Topic welcomes Original Research and Review in the following aspects, but the topic is not limited:
• Novel computational methods in identifying the associations between cancers and microbes
• Bioinformatics analyses in identifying the immune responses and mechanisms introduced by microbes in cancers, and the change of microbes during immunotherapy
• Bioinformatics in identifying the underlying connection mechanisms between microbes and cancers, for example, the key pathways and molecular networks mediating microbes and cancers
• Machine learning models in predicting novel interactions between microbes and drugs for cancers
• Novel computational models in cancer early diagnosis based on microbes or cell-free microbial materials in blood, excrement, and urine
• Systematic analyses in exploring the difference of microbes between the different status of a trait in cancer, for example, tumor vs normal, different cancer grades, different TNM staging, recurrence vs non-recurrence, and so on
• Novel computational (e.g. multimodal deep learning) models in predicting the prognosis of cancer patients by integrating microbial, molecular, pathological, and clinical data of patients
Please note that this Research Topic will not accept manuscripts based solely on the analysis of amplicons (e.g., 16S RNA or 18S RNA genes).
Microbes such as bacteria, viruses, fungi, and protists can be involved in practically every stage of cancer formation and progression, as well as diagnosis, therapy, prognosis, immune function, as well as drug response. Human papillomavirus (HPV) infection, for example, is strongly linked to cervical cancer and is implicated in others, including oropharyngeal cancer, anal cancer, and penile cancer. Several studies have also revealed close links between bacteria and cancer, such as those between helicobacter pylori and gastric cancer, hepatitis C virus (HCV) and primary liver cancer, intestinal flora, and colorectal cancer. According to new research, different cancers have different microbial fingerprints, and microbial DNA in the blood can be used to identify several types of cancers with acceptable accuracy.
Experiments to confirm the link between microbes and cancer, as well as the roles of microbes in cancer diagnosis and therapy, particularly immunotherapy, prognosis, and drug response, are costly and time-consuming. As a result, it's critical to develop high-level computational methods that will allow researchers to prioritize high-confidence microbe-directed hypotheses for further experimental validation in cancer studies. This Research Topic aims to: (1) discover new microbes related to cancers; (2) explore the mutual interactions between microbes and molecules in cancer patients at genetic, gene expression, protein, and metabolic levels; (3) identify significant associations between microbes and key functions in cancers, such as immune response and cancer initiation, development and progress; (4) elucidate differences in microbial compositions among tumor tissue, adjacent normal and normal tissues, and diagnose cancers based on the microbial constitution of cancer patients; (5) explore differences in microbial compositions among the distinct status of traits such as cancer grades, and tumor vs normal tissues; (6) predict prognosis of cancer patients using computational models based on microbes, molecular and pathological data; (7) reveal associations between microbes and drugs for relevant cancers.
This Research Topic welcomes Original Research and Review in the following aspects, but the topic is not limited:
• Novel computational methods in identifying the associations between cancers and microbes
• Bioinformatics analyses in identifying the immune responses and mechanisms introduced by microbes in cancers, and the change of microbes during immunotherapy
• Bioinformatics in identifying the underlying connection mechanisms between microbes and cancers, for example, the key pathways and molecular networks mediating microbes and cancers
• Machine learning models in predicting novel interactions between microbes and drugs for cancers
• Novel computational models in cancer early diagnosis based on microbes or cell-free microbial materials in blood, excrement, and urine
• Systematic analyses in exploring the difference of microbes between the different status of a trait in cancer, for example, tumor vs normal, different cancer grades, different TNM staging, recurrence vs non-recurrence, and so on
• Novel computational (e.g. multimodal deep learning) models in predicting the prognosis of cancer patients by integrating microbial, molecular, pathological, and clinical data of patients
Please note that this Research Topic will not accept manuscripts based solely on the analysis of amplicons (e.g., 16S RNA or 18S RNA genes).
