Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma

Abstract

BackgroundsUterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.MethodsConsensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.ResultsAll UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.ConclusionWe successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Pan-cancer analysis of super-enhancer-induced LINC00862 and validation as a SIRT1-promoting factor in cervical cancer and gastric cancer

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Identification and verification of aging-related lncRNAs for prognosis prediction and immune microenvironment in patients with head and neck squamous carcinoma

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Cross‐talk of pyroptosis‐based subtypes, the development of a risk classifier and immune responses in cervical cancer

“…With the advancement of genomics, a growing number of genetic markers have been uncovered, and these biomarkers have dramatically contributed to cancer research in terms of diagnosis, treatment and prognosis assessment. [18][19][20] In our project, we explored the significance of PRGs in CC utilizing two independent CC cohorts to exploit new prognostic biomarkers and further build a PRG-related signature for predicting the outcome of CC samples. Our results provided data revealing the potential functional and prognostic capabilities of the signature in Previous studies have reported PEG3 plays a central part in p53 regulated cell death, suggesting it may serve as a potential tumor suppressor.…”

“…Biomarkers can be used as indicators of pathogenesis or response to therapeutic intervention. With the advancement of genomics, a growing number of genetic markers have been uncovered, and these biomarkers have dramatically contributed to cancer research in terms of diagnosis, treatment and prognosis assessment 18–20 …”