Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in esophageal carcinoma

Abstract Background As the telomere functions continued to be unraveled, we aim to develop a prognostic gene signature that telomere-related features for patients with esophageal carcinoma. Methods First, differentially expressed genes (DEGs) related to telomere in ESCA were detected, Based on telomere-related DEGs, variables were determined by the LASSO regression and multivariate Cox, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Independent GEO dataset were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC2 database. Finally, we selected MAPK12 involved in the signature, which has not been reported in ESCA, for further experimental validation. Results ESCA patients with differentially expressed genes (DEGs) related to telomere exhibited related GO and KEGG terms. The prognostic signature incorporated 3 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.75 (1-year), 0.75 (3-year) and 0.69 (5-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of ESCA patients. Through GSEA and GSVA, we found that tumor progression and metastasis-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that MAPK12 could promote the proliferation and migration of ESCA cells. Conclusions We first developed and validated a new signature that incorporates genes that relate to telomeres in ESCA patients. Our study explored the potential clinical significance of this biomarker. Our prognostic model is capable of independently predicting survival of ESCA patients, as demonstrated by the high-throughput data mining results. The theoretical basis provided by these results can be used to further explore the molecular pathogenesis of ESCA and identify therapeutic approaches..

Medienart:	Preprint

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	ResearchSquare.com - (2024) vom: 11. Apr. Zur Gesamtaufnahme - year:2024

Sprache:	Englisch

Beteiligte Personen:	Chen, Shihui [VerfasserIn] Li, Xiaopeng [VerfasserIn] Ye, Changchun [VerfasserIn] Zhao, Chenye [VerfasserIn] Dong, Zepeng [VerfasserIn] Yuan, Hang [VerfasserIn] Gu, Shuyuan [VerfasserIn] Sun, Xuejun [VerfasserIn] Zhao, Wei [VerfasserIn]

Links:	Volltext [kostenfrei]

Themen:	570 Biology

doi:	10.21203/rs.3.rs-4028429/v1

funding:
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PPN (Katalog-ID):	XRA042906504