4.4 Article

Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research

期刊

ANTI-CANCER DRUGS
卷 33, 期 9, 页码 943-959

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/CAD.0000000000001319

关键词

CIBERSORT algorithm; immunotherapy; lung adenocarcinoma; multiple omics research; selective poly adenylation

资金

  1. Scientific Research Project of Education Department of Anhui Province [YJS20210324]
  2. National Natural Science Foundation of China [81972829]
  3. Science and Technology Innovation Committee of Shenzhen Municipality [JCYJ20180228162607111, JCYJ20190809104601662]
  4. Health and Family Planning Commission of Shenzhen Municipality Research Project [SZBC2018018]
  5. China Scholarship Council (CSC) [201908470124]
  6. Peking University-University of Michigan JI Project 2018 [2019020(PUSH)-r1]
  7. Science, Technology and Innovation Commission of Shenzhen Municipality [JCYJ20180228175531145]
  8. Shenzhen Huada Life Sciences Open Fund Project [BGIRSZ20200003]

向作者/读者索取更多资源

This study aimed to investigate the application value of selective polyadenylation in lung adenocarcinoma (LUAD), particularly in immune cell infiltration, biological transcription function, and survival prognosis. The research analyzed mRNA expression data and immune cell content from LUAD patients to construct a LUAD risk score prognostic model. The study found that the immune score significantly affected the prognosis of LUAD patients, and identified specific immune cell types and immunosuppressor genes correlated with the risk score. The findings highlight the importance of selective polyadenylation in LUAD development, immune invasion, and survival prognosis.
The aim of this study was to find the application value of selective polyadenylation in immune cell infiltration, biological transcription function and risk assessment of survival and prognosis in lung adenocarcinoma (LUAD). The processed original mRNA expression data of LUAD were downloaded, and the expression profiles of 594 patient samples were collected. The (APA) events in TCGA-NA-SEQ data were evaluated by polyadenylation site use Index (PDUI) values, and the invasion of stromal cells and immune cells and tumor purity were calculated to group and select the differential genes. Lasso regression and stratified analysis were used to examine the role of risk scores in predicting patient outcomes. The study also used the GDSC database to predict the chemotherapeutic sensitivity of each tumor sample and used a regression method to obtain an IC50 estimate for each specific chemotherapeutic drug treatment. Then CIBERSORT algorithm was used to conduct Spearman correlation analysis, immune regulatory factor analysis and TIDE immune system function analysis for gene expression level and immune cell content. Finally, the Kaplan-Meier curve was used to analyze the correlation between stromal score and the immune score of LUAD. In this study, APA's LUAD risk score prognostic model was constructed. KM survival analysis showed that immune score affected the prognosis of LUAD patients (P = 0.027) but the matrix score was not statistically significant (P = 0.1). We extracted 108 genes with APA events from 827 different genes and based on PUDI clustering and heat map, the survival rate of patients in the four groups was significantly different (P = 0.05). Multiple omics studies showed that risk score was significantly positively correlated with Macrophages M0, T cells Follicular helper, B cells naive and NK cells resting. It is significantly negatively correlated with dendritic cells resting, mast cells resting, monocyte, T cells CD4 memory resting and B cells memory. We further explored the relationship between the expression of immunosuppressor genes and risk score and found that ADORA2A, BTLA, CD160, CD244, CD274, CD96, CSF1R and CTLA4 genes were highly correlated with the risk score. Selective poly adenylation plays an important role in the development and progression of LUAD, immune invasion, tumor cell invasion and metastasis and biological transcription, and affects the survival and prognosis of LUAD patients.

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