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非小细胞肺癌精准治疗的预后指标。

Prognostic Indicators for Precision Treatment of Non-Small Cell Lung Carcinoma.

机构信息

Basic and Translational Research Division, Saroj Gupta Cancer Centre and Research Institute, Mahatma Gandhi Road, Kolkata 700063, West Bengal, India.

School of Health Sciences and Translational Research, Sister Nivedita University, Newtown, Kolkata 700156, West Bengal, India.

出版信息

Cells. 2024 Oct 28;13(21):1785. doi: 10.3390/cells13211785.

DOI:10.3390/cells13211785
PMID:39513892
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11545304/
Abstract

Non-small cell lung cancer (NSCLC) has established predictive biomarkers that enable decisions on treatment regimens for many patients. However, resistance to therapy is widespread. It is therefore essential to have a panel of molecular biomarkers that may help overcome therapy resistance and prevent adverse effects of treatment. We performed in silico analysis of NSCLC prognostic indicators, separately for adenocarcinomas and squamous carcinomas, by using The Cancer Genome Atlas (TCGA) and non-TCGA data sources in cBioPortal as well as UALCAN. This review describes lung cancer biology, elaborating on the key genetic alterations and specific genes responsible for resistance to conventional treatments. Importantly, we examined the mechanisms associated with resistance to immune checkpoint inhibitors. Our analysis indicated that a robust prognostic biomarker was lacking for NSCLC, especially for squamous cell carcinomas. In this work, our screening uncovered previously unidentified prognostic gene expression indicators, namely, , homologs, and for adenocarcinoma, and and for squamous cell carcinoma. It was further observed that overexpression of these genes was associated with poor prognosis. Additionally, homolog and unexpectedly harbored copy number amplifications. In conclusion, this study elucidated novel prognostic indicators for NSCLC that may serve as targets to overcome therapy resistance toward improved patient outcomes.

摘要

非小细胞肺癌 (NSCLC) 已确立了预测性生物标志物，使许多患者能够针对治疗方案做出决策。然而，治疗耐药性普遍存在。因此，拥有一组可能有助于克服治疗耐药性并预防治疗不良反应的分子生物标志物至关重要。我们通过使用 cBioPortal 中的癌症基因组图谱 (TCGA) 和非 TCGA 数据源以及 UALCAN，对 NSCLC 的预后指标进行了计算机分析，分别针对腺癌和鳞状细胞癌。本综述描述了肺癌生物学，阐述了导致对常规治疗耐药的关键遗传改变和特定基因。重要的是，我们研究了与免疫检查点抑制剂耐药相关的机制。我们的分析表明，NSCLC 缺乏稳健的预后生物标志物，尤其是鳞状细胞癌。在这项工作中，我们的筛选揭示了先前未被识别的预后基因表达指标，即 、 同源物和 用于腺癌，以及 用于鳞状细胞癌。进一步观察到这些基因的过表达与预后不良相关。此外， 同源物和 出人意料地存在拷贝数扩增。总之，这项研究阐明了 NSCLC 的新预后指标，它们可能成为克服治疗耐药性以改善患者预后的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/da18f72f7807/cells-13-01785-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/b38e737dd402/cells-13-01785-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/126d08f85b73/cells-13-01785-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/2ae9004d9d57/cells-13-01785-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/e869984a72f0/cells-13-01785-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/52a7257a230c/cells-13-01785-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/da18f72f7807/cells-13-01785-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/b38e737dd402/cells-13-01785-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/ffb598309023/cells-13-01785-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/6714aa264605/cells-13-01785-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/be05775df86f/cells-13-01785-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/126d08f85b73/cells-13-01785-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/2ae9004d9d57/cells-13-01785-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/e869984a72f0/cells-13-01785-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/52a7257a230c/cells-13-01785-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f22/11545304/da18f72f7807/cells-13-01785-g009.jpg

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