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Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis.

ABSTRACT: Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

SUBMITTER: Su K

PROVIDER: S-EPMC8525796 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis.

Su Kenong K Yu Qi Q Shen Ronglai R Sun Shi-Yong SY Moreno Carlos S CS Li Xiaoxian X Qin Zhaohui S ZS

Cell reports methods 20210723 4

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in indi ...[more]

PMID: 34671755

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Project description:Diagnosing, predicting disease outcome, and identifying effective treatment targets for virus-related cancers are lacking. Protein biomarkers have the potential to bridge the gap between prevention and treatment for these types of cancers. While it has been shown that certain antibodies against EBV proteins could be used to detect nasopharyngeal carcinoma (NPC), antibodies targeting are solely a tiny part of the about 80 proteins expressed by the EBV genome. Furthermore, it remains unclear what role other viruses play in NPC since many diseases are the result of multiple viral infections. For the first time, this study measured both IgA and IgG antibody responses against 646 viral proteins from 23 viruses in patients with NPC and control subjects using nucleic acid programmable protein arrays. Candidate seromarkers were then validated by ELISA using 1665 serum samples from three clinical cohorts. We demonstrated that the levels of five candidate seromarkers (EBV-BLLF3-IgA, EBV-BLRF2-IgA, EBV-BLRF2-IgG, EBV-BDLF1-IgA, EBV-BDLF1-IgG) in NPC patients were significantly elevated than controls. Additional examination revealed that NPC could be successfully diagnosed by combining the clinical biomarker EBNA1-IgA with the five anti-EBV antibodies. The sensitivity of the six-antibody signature at 95% specificity to diagnose NPC was comparable to the current clinically-approved biomarker combination, VCA-IgA, and EBNA1-IgA. However, the recombinant antigens of the five antibodies are easier to produce and standardize compared to the native viral VCA proteins. This suggests the potential replacement of the traditional VCA-IgA assay with the 5-antibodies combination to screen and diagnose NPC. Additionally, we investigated the prognostic significance of these seromarkers titers in NPC. We showed that NPC patients with elevated BLLF3-IgA and BDLF1-IgA titers in their serum exhibited significantly poorer disease-free survival, suggesting the potential of these two seromarkers as prognostic indicators of NPC. These findings will help develop serological tests to detect and treat NPC in the future.

Project description:BackgroundNeutrophils form extracellular net-like structures called neutrophil extracellular traps (NETs). Emerging evidence has shown that cancer can induce NET formation; however, it is not fully understood how NETs influence cancer biology, and no consensus has been reached on their pro- or antitumor effects. A comprehensive analysis of the global NET-associated gene regulatory network is currently unavailable and is urgently needed.MethodsWe systematically explored and discussed NET enrichment, NET-associated gene regulatory patterns, and the prognostic implications of NETs in approximately 8,000 patients across 22 major human cancer types. We identified NET-associated regulatory gene sets that we then screened for NET-associated regulatory patterns that might affect patient survival. We functionally annotated the NET-associated regulatory patterns to compare the biological differences between NET-related survival subgroups.ResultsA gene set variation analysis (GSVA) based on 23 major component genes was used to calculate a metric called the NET score. We found that the NET score was closely associated with many important cancer hallmarks, particularly inflammatory responses and epithelial-to-mesenchymal transition (EMT)-induced metastasis. Higher NET scores were related to poor immunotherapy response. Survival analysis revealed that NETs had diverse prognostic impacts among various cancer types. The NET-associated regulatory patterns linked to shorter or longer cancer patient survival were distinct from each other. Functional analysis revealed that more of the NET-associated regulatory genes linked to poor cancer survival were associated with extracellular matrix (ECM) remodeling and pan-cancerous risk factors. SPP1 was found to be highly expressed and correlated with NET formation in cancers with poor survival. We also found that the co-upregulation of NET formation and SPP1 expression was closely linked to increased EMT and poor survival, that SPP1 influenced NET-induced malignant capacity, and that SPP1 overproduction induced a robust formation of metastatic-promoting NETs.ConclusionNETs were common across cancers but displayed a diverse regulatory pattern and outcome readouts in different cancer types. SPP1 is potentially the key to NET-related poor outcomes.

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Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis.

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Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis.

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