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. 2024 Jan 26;15:1258475. doi: 10.3389/fimmu.2024.1258475

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Copyright © 2024 Li, Wang, Wan, Huang, Yin, Sun, Jin and Wang

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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DRGs-based clustering and prognosis analysis in pan-cancer. (A) The unsupervised clustering of DRGs in pan-cancer based on the 14 DRGs (MYL6, CD2AP, INF2, PDLIM1, ACTN4, FLNB, ACTB, MYH9, IQGAP1, CAPZB, DSTN, MYH10, FLNA, TLN1). (B) PCA analysis shows the sample distribution amongst subgroups (DSP1, DSP2, DSP3). (C) DRGs expression profile feature in subgroups. (D) Tumor sample distribution amongst subgroups. (E) Subgroup distribution proportion in 36 kinds of cancer. (F) OS, DSS, PFI, and DFI analysis among different DSP groups in pan-cancer were all significant (p<0.001). (G) The univariate Cox regression (OS) of DSP clusters in every type of cancer from TCGA, in which significance was observed in BLCA, CESC, COAD, CRCA, Glioma, HNSC, KICH, KIRC, LCA, LUAD, LUSC, PRAD, STAD, UCEC and UVM. OS analysis (H) and DSS analysis (I) in COAD, CRCA, GBM, glioma, HNSC, LUAD, LCA, STAD, and UCEC. * p<0.05, **p<0.01, ***p<0.001.