LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing

Abstract

Background: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1's impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors.

Methods: We retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the "Seurat" package. Differential expression analysis was then performed with the "Limma" package. The construction of scale-free gene co-expression networks was achieved using the "WGCNA" package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The "STRINGdb" package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package "mlr3verse." Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA.

Results: Our investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1's involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1^high and LIG1^low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples.

Conclusions: Our research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.

Keywords: LIG1; bioinformatics; machine-learning; single-cell; tumorinfiltrating immune cell; urothelial bladder cancer.

Figure 1

Differentially expressed genes (DEGs) and functional enrichment analysis of GSE13507 and GSE3167. (A) The DEGs in GSE13507 are shown in a volcano plot. (B) The DEGs in GSE3167 are shown in a volcano plot. (C, D) The gene set enrichment analysis (GSEA) suggested that GSE13507 and GSE3167 both showed enrichment for asthma, DNA replication, and graft-versus-host disease. (E, F) Weighted gene coexpression network analysis (WGCNA) of GSE13507 and GSE3167; the strongly positive correlation modules are indicated in the figure.

Figure 2

Screening and enrichment analysis of hub genes and related multiple machine learning. (A) The venn plot shows the intersected 69 genes. (B) Hub genes are shown in the protein–protein interaction (PPI) plot. (C, D) The enrichment analysis showed that nuclear division, organelle fission, sister chromatid segregation, mitotic sister chromatid segregation, mitotic nuclear division, spindle, tubulin binding, cell cycle, DNA replication, cellular senescence and the p53 signalling pathway were activated. (E) Machine learn model comparison of the nine hub genes. (F) Reciever operating characteristic (ROC) curve of the multiple machine learning models. (G) Binomial deviance of overall survival (OS) for the LASSO coefficient profiles. (H) LASSO coefficient profiles of genes.

Figure 3

LIG1 expression, survival, and drug susceptibility analysis. (A) Expression of LIG1 in normal tissues and bladder cancer tissues (human bladder cancer). (B) Overall survival analysis of LIG1 expression. (C) The meta-survival analysis of LIG1 expression (results from GENT2). (D–F) LIG1 expression and bladder cancer progression correlation analysis (stage T and stage N). (G–I) Correlation analysis of LIG1 expression and immunotherapy. ***P value < 0.001.

Figure 4

Immunotherapy prediction and analysis of somatic mutations and copy number variance (CNV). (A–F) Immunotherapy sensitivity prediction of the different cohorts. (G) Waterfall plot of somatic mutations and CNV between low LIG1 expression and high LIG1 expression groups. (H) Heatmap of LIG1 candidate drug predictions. *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 5

(A, B) tSNE or UMAP plots to identify each cell type in bladder cancer. (C) The violin plot shows the cell markers to identify each cell type. (D) The percentage plot of different types of cells in bladder cancer and adjacent tissues. (E) LIG1 expression in the different types of cells. (F) The expression of LIG1 in epithelial cells. (G) The expression of LIG1 in endothelial cells.

Figure 6

Cell cycle phase, cellchat, and gene set variation analysis (GSVA). (A) Cell cycle phase of the different cell types. (B) Cellchat analysis of the different cell types in bladder cancer. (C, D) Interaction of the different cell types in bladder cancer. (E) GSVA enrichment analysis of the different cell types.

Figure 7

LIG1 is involved in the proliferation and migration of bladder cancer cells. (A) The qRT-PCR results show the mRNA expression level of LIG1 in bladder cancer cells (T24, 5637, HT-1376) and normal bladder epithelial cells (HCV-29). (B, D) The cell proliferation capacity was detected using the EdU assay. (C) The expression level of LIG1 protein in bladder cancer cells (T24, 5637, HT-1376, RT-112) and the normal bladder epithelial cell line (HCV-29) was measured by Western blotting. (F) Cell viability was measured using the CCK-8 assay. (E, G) The effect of LIG1 knockdown on T24 cell invasion was evaluated using the transwell assay. (H, I) Quantitative statistics of transwell migration assays in T24 cells. A wound healing assay was performed to investigate the effects of LIG1 knockdown on the migration on T24 cells (*p < 0.05, **p < 0.01, ***p < 0.001, ns, not significant).

Figure 8

LIG1 knockdown affects the cell cycle, apoptosis and epithelial–mesenchymal transition (EMT) of bladder cancer cells. (A, B) The effect of LIG1 knockdown on apoptosis of T24 cells was analysed by flow cytometry with Annexin V-APC/PI staining. (C, D) Flow cytometry showed that LIG1 silencing delayed the cell cycle of T24 cells. (E, F) In T24 cells, motor inhibition caused by LIG1 silencing was associated with EMT inhibition. EMT markers (E-cadherin, N-cadherin, Snail, CCND1 and CKD1) were detected by Western blotting. (*p < 0.05, **p < 0.01, ***p < 0.001, ns, not significant).