Genome-Wide Analysis of Prognostic Alternative Splicing Signature and Splicing Factors in Lung Adenocarcinoma

Abstract

Analysis of The Cancer Genome Atlas data revealed that alternative splicing (AS) events could serve as prognostic biomarkers in various cancer types. This study examined lung adenocarcinoma (LUAD) tissues for AS and assessed AS events as potential indicators of prognosis in our cohort. RNA sequencing and bioinformatics analysis were performed. We used SUPPA2 to analyze the AS profiles. Using univariate Cox regression analysis, overall survival (OS)-related AS events were identified. Genes relating to the OS-related AS events were imported into Cytoscape, and the CytoHubba application was run. OS-related splicing factors (SFs) were explored using the log-rank test. The relationship between the percent spliced-in value of the OS-related AS events and SF expression was identified by Spearman correlation analysis. We found 1957 OS-related AS events in 1151 genes, and most were protective factors. Alternative first exon splicing was the most frequent type of splicing event. The hub genes in the gene network of the OS-related AS events were FBXW11, FBXL5, KCTD7, UBB and CDC27. The area under the curve of the MIX prediction model was 0.847 for 5-year survival based on seven OS-related AS events. Overexpression of SFs CELF2 and SRSF5 was associated with better OS. We constructed a correlation network between SFs and OS-related AS events. In conclusion, we identified prognostic predictors using AS events that stratified LUAD patients into high- and low-risk groups. The discovery of the splicing networks in this study provides an insight into the underlying mechanisms.

CELF2 is a candidate prognostic and immunotherapy biomarker in triple‐negative breast cancer and lung squamous cell carcinoma: A pan‐cancer analysis

“…Multiple studies have confirmed that CELF2 exerts tumour suppressive effects in most tumours, which is significantly increased in developing thymocytes and activated T cells, but its comprehensive understanding within the TME remains unknown 16–26 . Therefore, it is necessary to investigate the relationships between CELF2 expression and TIIC infiltration in the TME.…”

“…Previous studies have demonstrated that CELF2 could regulate multiple steps of RNA processing, such as pre‐mRNA splicing, RNA editing, polyadenylation, mRNA stability and translation 14,15 . Over the past decade, substantial studies have confirmed that CELF2 played a tumour suppressor role in breast cancer, lung cancer, hepatocellular carcinoma, gastric cancer, ovarian cancer, glioma and acute myeloid leukaemia, suggesting that it can be used as a candidate biomarker to predict cancer prognosis 16–23 . Additionally, CELF2 expression was significantly elevated in developing thymocytes and activated T cells and promoted T‐cell receptor expression and signalling through alternative splicing 24–26 .…”

“…14,15 Over the past decade, substantial studies have confirmed that CELF2 played a tumour suppressor role in breast cancer, lung cancer, hepatocellular carcinoma, gastric cancer, ovarian cancer, glioma and acute myeloid leukaemia, suggesting that it can be used as a candidate biomarker to predict cancer prognosis. [16][17][18][19][20][21][22][23] Additionally, CELF2 expression was significantly elevated in developing thymocytes and activated T cells and promoted T-cell receptor expression and signalling through alternative splicing. [24][25][26] However, the comprehensive understanding of the impacts of CELF2 on the tumour immune microenvironment remains unknown.…”

Metatranscriptomic Analysis of Human Lung Metagenomes from Patients with Lung Cancer

“…The RNA-seq data were analyzed as described previously [ 21 ]. In brief, data quality control at the Q20 level was performed using Trimmomatic [ 22 ], read alignment to the GRCh38 human genome was conducted using HISAT2 [ 23 ], expression was quantified using GENCODE v22 (excluding including mitochondrial genes), and transcripts were normalized into transcripts per million (TPM) using StringTie [ 24 ].…”

The Fusion Gene Landscape in Taiwanese Patients with Non-Small Cell Lung Cancer

“…We used Trimmomatic for quality control of the RNA-Seq data [ 28 ], as described previously [ 29 ]. For gene expression, the reads that passed quality control were aligned with the GRCh38 human genome using HISAT2 [ 30 ], and featureCounts was used to quantify gene expression with annotation done using GENCODE v22 without mitochondrial genes [ 31 ].…”