SAM68 promotes tumorigenesis in lung adenocarcinoma by regulating metabolic conversion via PKM alternative splicing

Abstract Background Although using a blockade of programmed death-ligand 1 (PD-L1) to enhance T cell immune responses shows great promise in tumor immunotherapy, the immune-checkpoint inhibition strategy is limited for patients with solid tumors. The mechanism and efficacy of such immune-checkpoint inhibition strategies in solid tumors remains unclear. Results Employing qRT-PCR, Sanger sequencing, and RNA BaseScope analysis, we show that human lung adenocarcinoma (LUAD) all produce a long non-coding RNA isoform of PD-L1 (PD-L1-lnc) by alternative splicing, regardless if the tumor is positive or negative for the protein PD-L1. Similar to PD-L1 mRNA, PD-L1-lnc in various lung adenocarcinoma cells is significantly upregulated by IFNγ. Both in vitro and in vivo studies demonstrate that PD-L1-lnc increases proliferation and invasion but decreases apoptosis of lung adenocarcinoma cells. Mechanistically, PD-L1-lnc promotes lung adenocarcinoma progression through directly binding to c-Myc and enhancing c-Myc transcriptional activity. Conclusions In summary, the PD-L1 gene can generate a long non-coding RNA through alternative splicing to promote lung adenocarcinoma progression by enhancing c-Myc activity. Our results argue in favor of investigating PD-L1-lnc depletion in combination with PD-L1 blockade in lung cancer therapy.

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Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Journal of Translational Medicine ◽

10.1186/s12967-020-02635-y ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Qidong Cai ◽

Boxue He ◽

Pengfei Zhang ◽

Zhenyu Zhao ◽

Xiong Peng ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Machine Learning Algorithms ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Learning Methods ◽

Machine Learning Methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

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An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Bioscience Reports ◽

10.1042/bsr20202338 ◽

2020 ◽

Vol 40 (10) ◽

Author(s):

Yidi Wang ◽

Yaxuan Wang ◽

Kenan Li ◽

Yabing Du ◽

Kang Cui ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Prognostic Value ◽

Prognostic Index ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Splicing Factors ◽

Systematic Analysis ◽

Interactive Network ◽

Underlying Mechanisms

Abstract Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

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The pattern of alternative splicing in lung adenocarcinoma shows novel events correlated with tumorigenesis and immune microenvironment

BMC Pulmonary Medicine ◽

10.1186/s12890-021-01776-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Gongjun Wang ◽

Weiwei Qi ◽

Liwei Shen ◽

Shasha Wang ◽

Ruoxi Xiao ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Immune Cells ◽

Risk Group ◽

Progression Free Survival ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Normal Tissues ◽

Cancer Genome Atlas ◽

Score Model

AbstractLung adenocarcinoma (LUAD) is the leading cause of cancer deaths worldwide due to the lack of early diagnostic markers and specific drugs. Previous studies have shown the association of LUAD growth with aberrant alternative splicing (AS). Herein, clinical data of 535 tumor tissues and 59 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. Each sample was analyzed using the ESTIMATE algorithm; a comparison between higher and lower score groups (stromal or immune) was made to determine the overall- and progression-free survival-related differentially expressed AS (DEAS) events. We then performed unsupervised clustering of these DEASs, followed by determining their relationship with survival rate, immune cells, and the tumor microenvironment (TME). Next, two prognostic signatures were developed using bioinformatics tools to explore the prognosis of cases with LUAD. Five OS- and six PFS-associated DEAS events were implemented to establish a prognostic risk score model. When compared to the high-risk group (HRG), the PFS and OS of the low-risk group (LRG) were found to be considerable. Additionally, a better prognosis was found considerably associated with the ESTIMATE score of the patients as well as immune cells infiltration. Our analysis of AS events in LUAD not only helps to clarify the tumorigenesis mechanism of AS but also provides ideas for revealing potential prognostic biomarkers and therapeutic targets.

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Systematic identification of cancer-related long noncoding RNAs and aberrant alternative splicing of quintuple-negative lung adenocarcinoma through RNA-Seq

Lung Cancer ◽

10.1016/j.lungcan.2017.04.009 ◽

2017 ◽

Vol 109 ◽

pp. 21-27 ◽

Cited By ~ 13

Author(s):

Lu Zhang ◽

Shiyong Li ◽

Yoon-La Choi ◽

Jinseon Lee ◽

Zhuolin Gong ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Rna Seq ◽

Systematic Identification

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

Genes ◽

10.3390/genes11111300 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1300

Author(s):

Ya-Sian Chang ◽

Siang-Jyun Tu ◽

Hui-Shan Chiang ◽

Ju-Chen Yen ◽

Ya-Ting Lee ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Area Under The Curve ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Splicing Factors ◽

Cox Regression Analysis ◽

Underlying Mechanisms

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.

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De NovoCeramide Regulates the Alternative Splicing of Caspase 9 and Bcl-x in A549 Lung Adenocarcinoma Cells

Journal of Biological Chemistry ◽

10.1074/jbc.m112010200 ◽

2002 ◽

Vol 277 (15) ◽

pp. 12587-12595 ◽

Cited By ~ 243

Author(s):

Charles E. Chalfant ◽

Kristin Rathman ◽

Ryan L. Pinkerman ◽

Rachel E. Wood ◽

Lina M. Obeid ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Caspase 9 ◽

Adenocarcinoma Cells ◽

Lung Adenocarcinoma Cells ◽

A549 Lung

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The pattern of alternative splicing in lung adenocarcinoma shows novel events correlated with tumorigenesis and immune microenvironment

10.21203/rs.3.rs-960327/v1 ◽

2021 ◽

Author(s):

Wensheng Qiu

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Immune Cells ◽

Risk Group ◽

Progression Free Survival ◽

High Risk Group ◽

Free Survival ◽

Normal Tissues ◽

Tumor Tissues ◽

Score Model

Abstract Lung adenocarcinoma (LUAD) is the leading cause of cancer deaths worldwide due to the lack of early diagnostic markers and specific drugs. Previous studies have shown the association of LUAD growth with aberrant alternative splicing (AS). Herein, clinical data of 535 tumor tissues and 59 normal tissues were extracted from the TCGA database. Each sample was analyzed using the ESTIMATE algorithm; a comparison between higher and lower score groups (stromal or immune) was made to determine the overall- and progression-free survival-related differentially expressed AS (DEAS) events. We then performed unsupervised clustering of these DEASs, followed by determining their relationship with survival rate, immune cells, and the tumor microenvironment (TME). Next, two prognostic signatures were developed using bioinformatics tools to explore the prognosis of cases with LUAD. Five OS- and six PFS-associated DEAS events were implemented to establish a prognostic risk score model. When compared to the high-risk group (HRG), the PFS and OS of the low-risk group (LRG) were found to be considerable. Additionally, a better prognosis was found considerably associated with the ESTIMATE score of the patients as well as immune cells infiltration. Our analysis of AS events in LUAD not only helps to clarify the tumorigenesis mechanism of AS, but also provides ideas for revealing potential prognostic biomarkers and therapeutic targets.

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Hypoxia-associated alternative splicing signature in lung adenocarcinoma

Epigenomics ◽

10.2217/epi-2020-0399 ◽

2020 ◽

Author(s):

Zhanyu Xu ◽

Jiangbo Wei ◽

Fanglu Qin ◽

Yu Sun ◽

Weiwei Xiang ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Predictive Power ◽

Splicing Factors ◽

Pathological Stage ◽

Prognostic Signature ◽

Diagnostic Efficacy ◽

Selection Operator

Aim: To establish a signature based on hypoxia-related alternative splicing (AS) events for lung adenocarcinoma. Materials & methods: The least absolute shrinkage and selection operator Cox approach was used to construct a prognostic model. A nomogram that integrates the final AS predictor and stage was created. The network of the key AS events and splicing factors was created. Results: We created a prognostic signature of 11 AS events. Moreover, a nomogram that constitutes the pathological stage and risk was exhibited to be greatly effective in estimating the survival likelihood of lung adenocarcinoma patients. Conclusion: Herein we developed the first-ever signature based on hypoxia-related AS events with both prognostic predictive power and diagnostic efficacy.

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