Development of a necroptosis-related gene signature and the immune landscape in ovarian cancer

Abstract

Background Necroptosis is a novel type of programmed cell death distinct from apoptosis. However, the role of necroptosis in ovarian cancer (OC) remains unclear. The present study investigated the prognostic value of necroptosis-related genes (NRGs) and the immune landscape in OC. Methods The gene expression profiling and clinical information were downloaded from the TCGA and GTEx databases. Differentially expressed NRGs (DE-NRGs) between OC and normal tissueswere identified. The regression analyses were conducted to screen the prognostic NRGs and construct the predictive risk model. Patients were then divided into high- and low-risk groups, and the GO and KEGG analyses were performed to explore bioinformatics functions between the two groups. Subsequently, the risk level and immune status correlations were assessed through the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden (TMB) and the drug sensitivity were also analyzed based on the two-NRG signature in OC. Results Totally 42 DE-NRGs were identified in OC. The regression analyses screened out two NRGs (MAPK10 and STAT4) with prognostic values for overall survival. The ROC curve showed a better predictive ability in five-year OS using the risk score. Immune-related functions were significantly enriched in the high- and low-risk group. Macrophages M1, T cells CD4 memory activated, T cells CD8, and T cells regulatory infiltration immune cells were associated with the low-risk score. The lower tumor microenvironment score was demonstrated in the high-risk group. Patients with lower TMB in the low-risk group showed a better prognosis, and a lower TIDE score suggested a better immune checkpoint inhibitor response in the high-risk group. Besides, cisplatin and paclitaxel were found to be more sensitive in the low-risk group. Conclusions MAPK10 and STAT4 can be important prognosis factors in OC, and the two-gene signature performs well in predicting survival outcomes. Our study provided novel ways of OC prognosis estimation and potential treatment strategy.

Construction and Identification of an NLR-Associated Prognostic Signature Revealing the Heterogeneous Immune Response in Skin Cutaneous Melanoma

“…34 Several studies have suggested that MAPK10 is highly expressed in gastric and ovarian cancers and is a prognostic risk factor for tumors. [35][36][37] Our investigation verified the elevated expression of MAPK10 in SKCM tumor tissues via RT-qPCR and IHC. CCL8 (chemotactic cytokines 8) expression correlated positively with M1 macrophages, and higher CCL8 expression had significantly longer overall survival in SKCM, which may be attributed to the positive role played by M1 macrophages in anti-tumor immune activity.…”

Machine learning‐derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer

“…sig). 12 , 13 , 14 , 15 Nevertheless, the application of multigene expression signatures in clinical practice is limited due to their underutilization, utilization of inappropriate machine‐learning methods and lack of rigorous validation through additional large cohorts. To overcome these challenges, effective strategies include the development of new markers with better predictive performance or the establishment of a comprehensive rubric consisting of multiple predictive markers.…”

Editorial: Molecular landscapes of human papillomavirus-related squamous cell carcinoma

“…As an emerging technological tool, data mining has been widely applied in the field of medicine. It can assist researchers in extracting valuable information from massive data, such as identifying the pathogenesis of cancer ( 6 ), recognizing patients’ risk factors ( 7 ), and predicting prognosis ( 8 ). Additionally, data mining can help discover novel treatment strategies and drugs, as well as optimize existing treatment regimens.…”