AI Article Synopsis

  • The tumor microenvironment (TME) influences cancer development and treatment responses, making it crucial to identify biomarkers reflecting its diversity.
  • Using data from The Cancer Genome Atlas, researchers categorized cancer patients based on immune and stromal scores to analyze the TME's impact on prognosis.
  • A five-gene signature related to the IFNγ response was discovered as a significant prognostic factor for skin cutaneous melanoma (SKCM), showing potential for improving patient outcomes based on immune response.

Article Abstract

Background: The tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME.

Methods And Materials: Based upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high- and low-scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database.

Results: Among the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P < .0001). We identified and validated a five-IFNγ response-related gene signature (UBE2L6, PARP14, IFIH1, IRF2, and GBP4), which was closely correlated with the prognosis for SKCM afflicted patients. Multivariate Cox regression analysis indicated that this risk model was an independent prognostic factor for SKCM. Tumor-infiltrating lymphocytes and specific immune checkpoint molecules had notably differential levels of expression in high- compared to low-risk samples.

Conclusion: In this study, we established a novel five-IFNγ response-related gene signature that provided a better and increasingly comprehensive understanding of tumor immune landscape, and which demonstrated good performance in predicting outcomes for patients afflicted by SKCM.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643661PMC
http://dx.doi.org/10.1002/cam4.3438DOI Listing

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