Migrasome-related lncRNAs predict prognosis and immune response of clear cell renal cell carcinoma

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive renal malignancy. Migrasomes, newly discovered organelles involved in intercellular communication, and long non-coding RNAs (lncRNAs) are emerging regulators of cancer progression. However, the role of migrasome-associated lncRNAs in ccRCC prognosis and immune response remains unclear. This study aimed to investigate the value of migrasome-related lncRNAs and develop a risk model for ccRCC.

Methods: By employing data from The Cancer Genome Atlas, we were able to identify prognostically significant migrasome-related lncRNAs through co-expression analysis, Cox regression, and least absolute shrinkage and selection operator (LASSO) regression. Prognostic models were developed and validated using these lncRNAs, and a nomogram combining the risk score with clinical features was constructed. Furthermore, our analyses encompassed gene set enrichment, immune infiltration, mutational burden, and drug sensitivity.

Results: A prognostic model incorporating 13 lncRNAs effectively stratified patients into distinct risk categories, with the high-risk cohort demonstrating markedly inferior survival rates. The prognostic accuracy was validated through multiple analyses. Gene enrichment analysis revealed a correlation between these lncRNAs and tumor development and immune pathways. High-risk patients exhibited increased immunosuppressive cell infiltration, oncogenic mutations, and potential for immune escape. Furthermore, they demonstrated a lack of response to immunotherapy and exhibited differential responses to antineoplastic agents when compared to low-risk patients. We propose a prognostic model for ccRCC based on migrasome-related lncRNAs, providing new insights into disease progression and potential individualized treatment strategies.

Conclusions: Our study proposes a prognostic model for ccRCC based on migrasome-related lncRNAs, providing new insights into disease progression and potential individualized treatment strategies.

Keywords: Migrasome; clear cell renal cell carcinoma (ccRCC); drug sensitivity; immune; prognosis.

Figure 1

Sankey diagram showed the co-expression relationship between migrasome-related genes and 728 lncRNAs. lncRNAs, long non-coding RNAs.

Figure 2

LASSO regression analysis and correlation heatmap of migrasome-related genes and lncRNAs. (A,B) LASSO regression cross-validation and coefficient trajectories. (C) Heatmap depicting the correlation between migrasome-related genes and migrasome-related lncRNAs. LASSO, least absolute shrinkage and selection operator; lncRNAs, long non-coding RNAs.

Figure 3

Risk score analysis, survival status, Kaplan-Meier survival curves, and PCA for ccRCC patients. Risk score distribution, survival status, and heatmap of differentially expressed lncRNAs for (A) all patients, (B) training cohort, and (C) testing cohort. Kaplan-Meier survival curves for (D) all patients, (E) training cohort, and (F) testing cohort. PCA based on: (G) all genes, (H) migrasome-related genes, (I) migrasome-related lncRNAs, and (J) risk lncRNAs. ccRCC, clear cell renal cell carcinoma; lncRNAs, long non-coding RNAs; PCA, principal component analysis.

Figure 4

Model prognostic value evaluation and nomogram. (A) Forest plot shows the results of univariate Cox regression. (B) Forest plot shows the results of multivariate Cox regression. (C) ROC curves comparing predictive power of risk score and clinical factors. (D) Time-dependent ROC curves demonstrating the model’s predictive accuracy at 1, 3, and 5 years. (E) The C-index curve showed that the accuracy of risk score was better than that of other clinical factors. (F) Kaplan-Meier survival curve shows progression-free survival of patients in the high and low-risk groups. (G,H) Kaplan-Meier survival curves of patients in high and low-risk groups at different stages. (I) Nomogram integrating risk score with clinical factors for individualized survival probability prediction. (J) Calibration plot showing excellent agreement between nomogram-predicted and observed survival probabilities at 1, 3, and 5 years. *, P<0.05; ***, P<0.001. AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic; T, tumor; M, metastasis; N, lymph node.

Figure 5

Gene enrichment analysis. (A) GO enrichment analysis. (B) KEGG pathway enrichment. (C) GSEA for high-risk group. (D) GSEA for high-risk group. BP, biological processes; CC, cellular components; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular functions.

Figure 6

Immune correlation analysis. (A) Box plots illustrating differential immune cell infiltration between high-risk and low-risk ccRCC patients. (B) Box plot depicting immune function scores across risk groups. (C) Violin plot comparing TIDE scores between risk groups. *, P<0.05; **, P<0.01; ***, P<0.001. aDCs, activated dendritic cells; APC, antigen-presenting cell; CCR, CC chemokine receptor; ccRCC, clear cell renal cell carcinoma; DCs, dendritic cells; TIDE, Tumor Immune Dysfunction and Exclusion; HLA, human leukocyte antigen; iDCs, Immature dendritic cells; MHC, major histocompatibility complex; NK, natural killer; pDCs, plasmacytoid dendritic cells; Tfh ,T follicular helper; Th1_cells, Th1 helper cells; Th2_cells, Th2 helper cells; TIL, tumor infiltrating lymphocyte; Treg, T regulatory cells.

Figure 7

TMB correlation analysis. (A) Waterfall plot depicting somatic mutations in high-risk ccRCC patients. (B) Waterfall plot of somatic mutations in low-risk ccRCC patients. (C) Violin plot comparing tumor mutation burden between risk groups. (D) Kaplan-Meier survival curves show the difference in survival between patients with high and low mutations. (E) Kaplan-Meier survival analysis incorporating TMB and risk score. ccRCC, clear cell renal cell carcinoma; TMB, tumor mutational burden.

Figure 8

Drug sensitivity analysis. (A) AZD7762. (B) Cediranib. (C) Entinostat. (D) Ibrutinib. (E) Osimertinib. (F) XAV939.