Novel Immune-Related Ferroptosis Signature in Esophageal Cancer: An Informatics Exploration of Biological Processes Related to the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network

Abstract

Background: Considering the role of immunity and ferroptosis in the invasion, proliferation and treatment of cancer, it is of interest to construct a model of prognostic-related differential expressed immune-related ferroptosis genes (PR-DE-IRFeGs), and explore the ferroptosis-related biological processes in esophageal cancer (ESCA).Methods: Four ESCA datasets were used to identify three PR-DE-IRFeGs for constructing the prognostic model. Validation of our model was based on analyses of internal and external data sets, and comparisons with past models. With the biological-based enrichment analysis as a guide, exploration for ESCA-related biological processes was undertaken with respect to the immune microenvironment, mutations, competing endogenous RNAs (ceRNA), and copy number variation (CNV). The model’s clinical applicability was measured by nomogram and correlation analysis between risk score and gene expression, and also immune-based and chemotherapeutic sensitivity.Results: Three PR-DE-IRFeGs (DDIT3, SLC2A3, and GCH1), risk factors for prognosis of ESCA patients, were the basis for constructing the prognostic model. Validation of our model shows a meaningful capability for prognosis prediction. Furthermore, many biological functions and pathways related to immunity and ferroptosis were enriched in the high-risk group, and the role of the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 network in ESCA is supported. Also, the KMT2D mutation is associated with our risk score and SLC2A3 expression. Overall, the prognostic model was associated with treatment sensitivity and levels of gene expression.Conclusion: A novel, prognostic model was shown to have high predictive value. Biological processes related to immune functions, KMT2D mutation, CNV and the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 network were involved in ESCA progression.

Identification and validation of necroptosis-related prognostic gene signature and tumor immune microenvironment infiltration characterization in esophageal carcinoma

“…We used TCGA ESCA database with external ESCA dataset GSE53624 to assess and verify the predictive ability of our prognostic model [53,54]. The Clinical and survival information of the whole set was derived from the TCGA dataset, and the external dataset GSE53624 was obtained from the GEO database through the GEO query R package.…”

Integrated Bioinformatics Analysis of Serine Racemase as an Independent Prognostic Biomarker in Endometrial Cancer

“…In the present study, SRR was found to have positive correlations with TMB and MSI in UCEC. Furthermore, the correlations between SRR and some immune inhibitors, such as PDCD1 and CTLA4, which have been reported to enhance the immune responses, were striking ( Fan et al, 2021b ; Lu et al, 2022 ). Immune checkpoint expression also differed between the SRR-high and SRR-low expression groups.…”

“…We discovered that the IC50 of doxorubicin, docetaxel, and paclitaxel was higher in the SRR-low expression group than in the SRR-high expression group, implying that patients with SRR-high expression were more sensitive to these drugs. Because chemotherapy drugs can have serious adverse effects, it is vital to screen people who are sensitive to them so that adverse reactions are minimized ( Fan et al, 2021b ; Lu et al, 2022 ). SRR expression could also be used as a biomarker to screen patients with UCEC for chemotherapy, according to our findings.…”

An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration

“…668 samples from TCGA cohort were randomly matched to the training set ( n = 334) and test set ( n = 334) on average. Lasso regression analysis can screen out highly relevant PR-DE-IRGs from the training set, thereby minimizing the risk of overfitting the screening features, and achieving the purpose of accurately predicting the patients’ prognosis ( Lu et al, 2022 ). The optimal penalty parameter ( λ ) obtained by the minimum 10-fold cross validation was used to determine five PR-DE-IRGs and corresponding coefficients for constructing the prognostic model.…”