Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer

Abstract

Cervical cancer (CESC) is a gynecologic malignant tumor associated with high incidence and mortality rates because of its distinctive management complexity. Herein, we characterized the molecular features of CESC based on the metabolic gene expression profile by establishing a novel classification system and a scoring system termed as METAscore. Integrative analysis was performed on human CESC samples from TCGA dataset. Unsupervised clustering of RNA sequencing data on 2,752 formerly described metabolic genes identified three METAclusters. These METAclusters for overall survival time, immune characteristics, metabolic features, transcriptome features, and immunotherapeutic effectiveness existed distinct differences. Then we analyzed 207 DEGs among the three METAclusters and as well identified three geneclusters. Correspondingly, these three geneclusters also differently expressed among the aforementioned features, supporting the reliability of the metabolism-relevant molecular classification. Finally METAscore was constructed which emerged as an independent prognostic biomarker, related to CESC transcriptome features, metabolic features, immune characteristics, and linked to the sensitivity of immunotherapy for individual patient. These findings depicted a new classification and a scoring system in CESC based on the metabolic pattern, thereby furthering the understanding of CESC genetic signatures and aiding in the prediction of the effectiveness to anticancer immunotherapies.

Metabolism-associated molecular classification of uterine corpus endometrial carcinoma

“…In order to model the two primary mechanisms of tumor immune infiltration, TIDE algorithm was applied: the stimulation of T-cell dysfunction accompanying high cytotoxic T-lymphocyte (CTL) infiltration and the prevention of T-cell infiltration with low CTL levels, which estimates the potential response to immunotherapy ( Li et al, 2021 ). Using the TIDE algorithm, the UCEC metabolic C1 subtype was predicted to be more responsive to immunotherapy than the C2 subtype ( Figure 4A ).…”

“…Many studies about the metabolic subtype classification have been reported; hepatocellular carcinoma (HCC) cancer was classified into three subclasses using a panel of metabolic genes ( Yang et al, 2020 ). In cervical cancer, based on 2,752 previously described metabolic genes, unsupervised clustering of RNA sequencing data identified three META clusters ( Li et al, 2021 ). However, the study on the metabolism-related molecular subtype classification of UCEC has yet to be reported.…”

Construction and comprehensive analysis of a novel prognostic signature associated with pyroptosis molecular subtypes in patients with pancreatic adenocarcinoma

“…Prior to grouping and modelling PRDEGs, it was realized that PMSRPS were established based on pyroptosis molecular subtypes(i.e. PRGclusters), failure to confirm the stability of subtypes inevitably affects the credibility of the signature, prompting us to perform another consensus clustering and typing of PRDEGs that match transcriptional data (37). The optimal cluster number supported the existence of three distinct and robust geneClusters in patients with PAAD (Figure 3H).…”

Identification of Two Novel Immune Subtypes Characterized by Distinct Prognosis and Tumor Microenvironment in Osteosarcoma

“…Tumor immune microenvironment (TIME) represents a complex interactions of tumor-infiltrating immune cell populations and individual immune status [9]. Previous studies have revealed that it could be accurately quantified by single-sample gene set enrichment analysis (ssGSEA) algorithm based on immune gene sets [10][11][12][13]. However, the exploration of TIME in osteosarcoma has rarely been reported.…”