Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910583958274048 |
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| author | Zheng, Huili Zhang, Qimin Gong, Yiru Liu, Zheyan Chen, Shaohan |
| author_facet | Zheng, Huili Zhang, Qimin Gong, Yiru Liu, Zheyan Chen, Shaohan |
| contents | Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in non-smoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performance with an AUC score of 0.835. The top biomarkers identified - CCAAT enhancer binding protein alpha (C/EBP-alpha), lactate dehydrogenase A4 (LDHA), UNC-45 myosin chaperone B (UNC-45B), checkpoint kinase 1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF-1-alpha) - have been validated in the literature as being significantly linked to lung cancer. These findings highlight the potential of these biomarkers for early diagnosis and personalized therapy, emphasizing the value of integrating machine learning with molecular profiling in cancer research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16068 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning Zheng, Huili Zhang, Qimin Gong, Yiru Liu, Zheyan Chen, Shaohan Genomics Artificial Intelligence Machine Learning Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in non-smoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performance with an AUC score of 0.835. The top biomarkers identified - CCAAT enhancer binding protein alpha (C/EBP-alpha), lactate dehydrogenase A4 (LDHA), UNC-45 myosin chaperone B (UNC-45B), checkpoint kinase 1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF-1-alpha) - have been validated in the literature as being significantly linked to lung cancer. These findings highlight the potential of these biomarkers for early diagnosis and personalized therapy, emphasizing the value of integrating machine learning with molecular profiling in cancer research. |
| title | Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning |
| topic | Genomics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.16068 |