Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2024-09-12 eCollection Date: 2024-01-01 DOI:10.1177/11769351241276757
Vivek Uttam, Harmanpreet Singh Kapoor, Manjit Kaur Rana, Ritu Yadav, Hridayesh Prakash, Manju Jain, Hardeep Singh Tuli, Aklank Jain
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Abstract

Objectives: Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients.

Methods: To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed.

Results: Utilizing a cutoff criterion (log2FC > 1 + log2FC < -1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value = .048), programed cell death ligand 2 (PD-L2; P-value = .002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value = .045) expression levels were significantly higher in low-risk patients compared to high-risk patients.

Conclusion: We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future.

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免疫相关长非编码 RNA 标志决定食管癌的预后和免疫治疗一致性
研究目的方法:为了确定免疫相关lncRNAs在食管癌中的作用,我们分析了162名食管癌患者和11名非癌症患者的RNA-seq表达数据以及癌症患者的临床相关信息:为了确定免疫相关lncRNAs在食管癌中的作用,我们分析了癌症基因组图谱(TCGA)数据库中162名食管癌患者和11名非癌症患者的RNA-seq表达数据及其临床相关信息。我们采用了生物信息学和统计学分析,如差异表达分析、共表达分析、Kaplan Meier生存分析、Cox比例危险度模型、ROC风险分析模型等:利用截断标准(log2FC > 1 + log2FC)得出结论:我们相信这项研究将有助于更好地预测EC的预后并在未来对其进行有针对性的治疗。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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