Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response
Abstract Lung 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 at an advanced stage, limiting treatment options. This...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-024-01717-3 |
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| author | Qiqing Zhang Haidong He Yi Wei Guoping Li Lu Shou |
| author_facet | Qiqing Zhang Haidong He Yi Wei Guoping Li Lu Shou |
| author_sort | Qiqing Zhang |
| collection | DOAJ |
| description | Abstract Lung 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 at an 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. |
| format | Article |
| id | doaj-art-52effb6fee924118823e2769eb6c2a34 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-52effb6fee924118823e2769eb6c2a342025-08-20T02:43:25ZengSpringerDiscover Oncology2730-60112024-12-0115111210.1007/s12672-024-01717-3Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy responseQiqing Zhang0Haidong He1Yi Wei2Guoping Li3Lu Shou4Department Oncology, Tongde Hospital of Zhejiang ProvincialDepartment Pulmonary and Critical Care Medicine, Tongde Hospital of Zhejiang ProvincialDepartment Pulmonary and Critical Care Medicine, Tongde Hospital of Zhejiang ProvincialDepartment Pulmonary and Critical Care Medicine, Tongde Hospital of Zhejiang ProvincialDepartment Pulmonary and Critical Care Medicine, Tongde Hospital of Zhejiang ProvincialAbstract Lung 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 at an 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.https://doi.org/10.1007/s12672-024-01717-3Lung squamous cell carcinoma (LUSC)Gene expression analysisCox proportional risk modelBiomarkersPersonalized treatment |
| spellingShingle | Qiqing Zhang Haidong He Yi Wei Guoping Li Lu Shou Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response Discover Oncology Lung squamous cell carcinoma (LUSC) Gene expression analysis Cox proportional risk model Biomarkers Personalized treatment |
| title | Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response |
| title_full | Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response |
| title_fullStr | Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response |
| title_full_unstemmed | Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response |
| title_short | Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response |
| title_sort | bioinformatics based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response |
| topic | Lung squamous cell carcinoma (LUSC) Gene expression analysis Cox proportional risk model Biomarkers Personalized treatment |
| url | https://doi.org/10.1007/s12672-024-01717-3 |
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