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Survival-linked genes were screened by statistical testing, and an 11-gene Cox proportional risk model was constructed to predict patient outcomes and support individualized immunotherapy-related decision-making.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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of immunotherapy response  \nQiqing Zhang1 · Haidong He2 · Yi Wei2 · Guoping Li2 · Lu Shou2  \nReceived: 18 August 2024 / Accepted: 18 December 2024  \n© The Author(s) 2024 OPEN  \nAbstract  \nLung squamous cell carcinoma (LUSC) is a subtype of non-small cell lung cancer. It has a grim prognosis for patients, primarily because the disease often remains asymptomatic in its early stages. As a result, it is frequently diagnosed atan advanced stage, limiting treatment options. This underscores the importance of studying potential biomarkers and developing personalized treatment strategies. In this study, we used an advanced bioinformatics approach, integrating two authoritative databases, NCBI’s GEO and TCGA, to perform a large-scale cross-platform gene expression analysis. To deeply mine the gene expression data of a large number of lung squamous carcinoma samples, we used a screening strategy based on median absolute deviation to select genes that differed significantly in multiple datasets. The expression variations of these genes between normal and cancerous tissues provided us with valuable clues revealing key molecules that may be involved in the disease process. Through rigorous statistical tests, we identified 36 genes that were significantly associated with patient survival, and further constructed a model using Cox proportional risk model containing 11 key genes (MRPL40, GABPB1AS1, PTPN3, SNCA, PYGB, RAP1, VDR, PHPT1, KIAA0100, TBC1D30, CYP7B1) in a risk prediction model. The prediction model not only reflects the strong correlation between gene expression and LUSC prognosis, but also provides clinicians with an effective tool to predict patients’ survival prospects. In the future, this model is expected to guide the development of individualized treatment plans, thereby improving the quality of life and overall prognosis of patients.  \nKeywords Lung squamous cell carcinoma (LUSC) · Gene expression analysis · Cox proportional risk model · Biomarkers · Personalized treatment  \n1 Introduction  \nLung cancer is the leading cause of cancer deaths worldwide and one of the most common and serious cancers [1] . Studies have shown that certain characteristics make cancer patients more prone to emotional problems, which are associated with decreased quality of life and increased symptom burden [2]. So the various complex ways in which lung cancer is lethal have posed challenges to efforts aimed at extending survival and improving quality of life. For instance, Lung Squamous Cell Carcinoma(LUSC), which accounts for 20–30% of non-small cell lung cancers, is the second leading cause of lung cancer fatalities [3] . However, compared to Adenocarcinoma (the most common histological type), thereis less understanding of Squamous Cell Carcinoma, which complicates research and treatment advancements [4] .  \n* Lu Shou, [wamlxj@163.com](wamlxj@163.com); Qiqing Zhang, [zhangqiqingelva@163.com](zhangqiqingelva@163.com); Haidong He, [13666653080@163.com](13666653080@163.com); Yi Wei, [weiyi.smile@163.com](weiyi.smile@163.com); Guoping Li, [I18167105257@163.com |](I18167105257@163.com |1Department Oncology)[1](I18167105257@163.com |1Department Oncology)[Department Oncology](I18167105257@163.com |1Department Oncology), Tongde Hospital of Zhejiang Provincial, Hangzhou, China. 2Department Pulmonary and Critical Care Medicine, Tongde Hospital of Zhejiang Provincial, Hangzhou, China.  \nDiscover Oncology  \n(2024) 15:840  \n| [https://doi.org/10.1007/s12672-024-01717-3](https://doi.org/10.1007/s12672-024-01717-3)  \nLung cancer treatment today embraces a multifaceted approach, integrating conventional chemotherapy with precision medicine strategies, particularly molecular targeted therapies [5]. These therapies, designed to target specific genetic aberrations, have revolutionized the way certain lung cancers, such as those driven by EGFR mutations (Epidermal Growt","cbCaigMVr95WNCNx","https://ap.wps.com/l/cbCaigMVr95WNCNx","pdf",554549,12,"English","# Abstract\n# 1 Introduction","[{\"question\":\"Why is LUSC prognosis difficult to improve clinically?\",\"answer\":\"Early-stage LUSC often remains asymptomatic, so patients are frequently diagnosed at advanced stages, which limits treatment options and contributes to poor overall survival.\"},{\"question\":\"How does the study identify candidate genes for prognosis modeling?\",\"answer\":\"It integrates cross-platform gene expression data from NCBI GEO and TCGA, then applies a median absolute deviation-based screening strategy to select genes that differ significantly across multiple datasets.\"},{\"question\":\"What prediction model is constructed and what is its purpose?\",\"answer\":\"The work identifies survival-associated genes and builds a Cox proportional risk model containing 11 key genes to predict LUSC patient survival prospects and provide a tool for clinicians.\"}]","Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response | PDF",1789999853]