Integrated Bioinformatics Approach for Disclosing Autophagy Pathway as a Therapeutic Target in Advanced KRAS Mutated/Positive Lung Adenocarcinoma

Q3 Computer Science

Open Bioinformatics Journal Pub Date : 2023-05-24 DOI:10.2174/18750362-v16-2305230-2022-18

Yasmeen I Dodin

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引用次数: 2

Abstract

Lung cancer is the leading cause of cancer-related deaths, accounting for 1.8 million deaths (18%). Nearly 80%-85% of lung cancer cases are non-small cell lung cancers (NSCLC). One of the most frequent genetic mutations in NSCLC is the Kirsten Rat Sarcoma Oncogene Homolog (KRAS) gene mutation. In recent years, autophagy has drawn substantial attention as a potential pathway that can be targeted in cancer driven by KRAS gene mutation to efficiently improve the therapeutic profile of different treatments. In this study, we have investigated the potential of targeting the autophagy pathway as a treatment approach in advanced KRAS-mutated lung adenocarcinoma using gene expression data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project. Compared to KRAS wild-type lung adenocarcinoma, there were found 11 differentially expressed autophagy-related genes (DEARGs), with 5 upregulated and 6 downregulated DEARGs (threshold of adjusted p-value <0.05). These DEARGs can be investigated as potential genes that can be targeted by different autophagy inhibitors.

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综合生物信息学方法揭示自噬途径作为晚期KRAS突变/阳性肺腺癌治疗靶点

癌症是癌症相关死亡的主要原因，占180万人死亡（18%）。近80%-85%的癌症病例是非小细胞肺癌（NSCLC）。NSCLC中最常见的基因突变之一是Kirsten大鼠肉瘤癌基因同源性（KRAS）基因突变。近年来，自噬作为一种潜在的途径，在KRAS基因突变的驱动下，可以靶向癌症，以有效改善不同治疗方法的治疗效果，引起了人们的广泛关注。在这项研究中，我们利用癌症基因组图谱肺腺癌（TCGA-LUAD）项目的基因表达数据，研究了靶向自噬途径作为晚期KRAS-突变肺腺癌治疗方法的潜力。与KRAS野生型肺腺癌相比，共发现11个差异表达的自噬相关基因（DEARGs），其中5个上调，6个下调（调整p值阈值<0.05）。这些DEARG可作为不同自噬抑制剂靶向的潜在基因进行研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)

CiteScore

2.40

自引率

0.00%

发文量

期刊介绍： The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.