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A Prognostic Risk Model for Programmed Cell Death and Revealing TRIB3 As a Promising Apoptosis Suppressor in Renal Cell Carcinoma

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Specialty Geriatrics
Date 2023 Nov 25
PMID 38006403
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Abstract

Programmed cell death (PCD), a common modality of cell death, affects tumor development and acts as a target for tumor therapeutics. Many modalities of PCD regulate genesis, progression and metastasis of cancers, thus affecting the patients' prognosis, but the comprehensive molecular mechanisms of PCD in tumors are lacking, especially in renal cancer. Here, seventeen PRPCDGs were identified from 1257 genes associated with thirteen PCD modalities, which were highly differentially expressed and significantly affected patients' prognosis. Then, LASSO regression analysis of these PRPCDGs screened the 9-gene PRPCDGs risk signature in TCGA-KIRC database. The PRPCDGs risk signature was closely associated with the patients' prognosis and presented stable prediction efficacy for 5- and 7-year overall survival (OS) in three different cohorts of renal cancer. Immune cell infiltration, immune checkpoint expression and pathway enrichment (including GO, KEGG pathway, tumor-associated pathways and metabolism-associated pathways) were significantly different in the high- or low-PRPCDGs-risk group. Finally, we illustrated that TRIB3 might be a protumor factor responsible for the elevated proliferation and invasion capacities of renal cell carcinoma (RCC) cells. In summary, the PRPCDGs risk signature was developed and showed stable prediction efficacy for the prognosis of patients and that (such as TRIB3) could be a potential target for RCC management.

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References
1.
Pallichankandy S, Thayyullathil F, Cheratta A, Subburayan K, Alakkal A, Sultana M . Targeting oxeiptosis-mediated tumor suppression: a novel approach to treat colorectal cancers by sanguinarine. Cell Death Discov. 2023; 9(1):94. PMC: 10011521. DOI: 10.1038/s41420-023-01376-3. View

2.
Koren E, Fuchs Y . Modes of Regulated Cell Death in Cancer. Cancer Discov. 2021; 11(2):245-265. DOI: 10.1158/2159-8290.CD-20-0789. View

3.
Moksud N, Wagner M, Pawelczyk K, Porebska I, Muszczynska-Bernhard B, Kowal A . Common inherited variants of PDCD1, CD274 and HAVCR2 genes differentially modulate the risk and prognosis of adenocarcinoma and squamous cell carcinoma. J Cancer Res Clin Oncol. 2023; 149(9):6381-6390. PMC: 10356891. DOI: 10.1007/s00432-023-04602-8. View

4.
Ferlay J, Colombet M, Soerjomataram I, Parkin D, Pineros M, Znaor A . Cancer statistics for the year 2020: An overview. Int J Cancer. 2021; . DOI: 10.1002/ijc.33588. View

5.
Inoue S, Leitner W, Golding B, Scott D . Inhibitory effects of B cells on antitumor immunity. Cancer Res. 2006; 66(15):7741-7. DOI: 10.1158/0008-5472.CAN-05-3766. View