Bioinformatics analysis identifies key genes of prognostic value in lung cancer
Lung cancer is the most common human malignancy worldwide and can be divided into different types of carcinomas depending on their pathological features. Advances in medical science and technology have led to the identification of some lung cancer-related marker genes, including EGFR (epidermal...
| Asıl Yazarlar: | , |
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| Materyal Türü: | Makale |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
MRE Press
2023-07-01
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| Seri Bilgileri: | Journal of Men's Health |
| Konular: | |
| Online Erişim: | https://oss.jomh.org/files/article/20230731-62/pdf/JOMH2023042301.pdf |
| _version_ | 1827359401577545728 |
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| author | Dan Song Li Sun |
| author_facet | Dan Song Li Sun |
| author_sort | Dan Song |
| collection | DOAJ |
| description | Lung cancer is the most common human malignancy worldwide and can be divided
into different types of carcinomas depending on their pathological features.
Advances in medical science and technology have led to the identification of some
lung cancer-related marker genes, including
EGFR (epidermal growth factor receptor), BRAF
(B-Raf proto-oncogene), RAS (RAS proto-oncogene, GTPase) and HER2 (human
epidermal growth factor receptor 2). However, the underlying biomarker and key
genes associated with different types of lung cancer are still poorly understood.
In this study, we analyzed a GEO (Gene Expression Omnibus) dataset and identified
28 upregulated intersection DEGs (different expression genes) and 125
downregulated intersection DEGs among AC (adenocarcinoma), PTC (primary typical
carcinoid), PLCC (primary large cell carcinoma), PLCNC (primary large cell lung
carcinoma) and PSCLC (primary small cell lung carcinoma). Through PPI
(protein-protein interaction) network analysis, we identified 14 genes among the
DEGs, namely MFAP4 (microfibril-associated protein 4), PDZD2 (PDZ domain
containing 2), FBLN1 (fibulin 1), FBLN5 (fibulin 5), EFEMP1 (EGF containing
fibulin extracellular matrix protein 1), KDR (kinase insert domain receptor),
S1PR1 (sphingosine-1-phosphate receptor 1), CAV1 (caveolin 1), GRK5 (G
protein-coupled receptor kinase 5), EDNRA (endothelin receptor type A), EDNRB
(endothelin receptor type B), CALCRL (calcitonin receptor-like receptor), PTGER4
(prostaglandin E receptor 4), and ADRB1 (adrenoceptor beta 1), which were found
to be downregulated in different subtypes of lung cancer and associated with poor
survival outcomes. In addition, most of the screened DEGs demonstrated good
predictive ability in LUAD (lung adenocarcinoma) and LUSC (lung squamous cell
carcinoma). Among them, MFAP4 was found to promote cell proliferation while also
suppressing cell migration and angiogenesis. In summary, we propose MFAP4, PDZD2,
FBLN1, FBLN5, EFEMP1, KDR, S1PR1, CAV1, GRK5, EDNRA, EDNRB, CALCRL, PTGER4 and
ADRB1 as potential prognostic markers in lung cancer patients. |
| first_indexed | 2024-03-08T06:28:29Z |
| format | Article |
| id | doaj.art-9ac28574d64044a887142d0dcaf9360c |
| institution | Directory Open Access Journal |
| issn | 1875-6859 |
| language | English |
| last_indexed | 2024-03-08T06:28:29Z |
| publishDate | 2023-07-01 |
| publisher | MRE Press |
| record_format | Article |
| series | Journal of Men's Health |
| spelling | doaj.art-9ac28574d64044a887142d0dcaf9360c2024-02-03T13:22:46ZengMRE PressJournal of Men's Health1875-68592023-07-0119711913010.22514/jomh.2023.064S1875-6867(23)00033-7Bioinformatics analysis identifies key genes of prognostic value in lung cancerDan Song0Li Sun1Department of Medical Oncology, Xuzhou Central Hospital, 221009 Xuzhou, Jiangsu, ChinaDepartment of Medical Oncology, Xuzhou Central Hospital, 221009 Xuzhou, Jiangsu, ChinaLung cancer is the most common human malignancy worldwide and can be divided into different types of carcinomas depending on their pathological features. Advances in medical science and technology have led to the identification of some lung cancer-related marker genes, including EGFR (epidermal growth factor receptor), BRAF (B-Raf proto-oncogene), RAS (RAS proto-oncogene, GTPase) and HER2 (human epidermal growth factor receptor 2). However, the underlying biomarker and key genes associated with different types of lung cancer are still poorly understood. In this study, we analyzed a GEO (Gene Expression Omnibus) dataset and identified 28 upregulated intersection DEGs (different expression genes) and 125 downregulated intersection DEGs among AC (adenocarcinoma), PTC (primary typical carcinoid), PLCC (primary large cell carcinoma), PLCNC (primary large cell lung carcinoma) and PSCLC (primary small cell lung carcinoma). Through PPI (protein-protein interaction) network analysis, we identified 14 genes among the DEGs, namely MFAP4 (microfibril-associated protein 4), PDZD2 (PDZ domain containing 2), FBLN1 (fibulin 1), FBLN5 (fibulin 5), EFEMP1 (EGF containing fibulin extracellular matrix protein 1), KDR (kinase insert domain receptor), S1PR1 (sphingosine-1-phosphate receptor 1), CAV1 (caveolin 1), GRK5 (G protein-coupled receptor kinase 5), EDNRA (endothelin receptor type A), EDNRB (endothelin receptor type B), CALCRL (calcitonin receptor-like receptor), PTGER4 (prostaglandin E receptor 4), and ADRB1 (adrenoceptor beta 1), which were found to be downregulated in different subtypes of lung cancer and associated with poor survival outcomes. In addition, most of the screened DEGs demonstrated good predictive ability in LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma). Among them, MFAP4 was found to promote cell proliferation while also suppressing cell migration and angiogenesis. In summary, we propose MFAP4, PDZD2, FBLN1, FBLN5, EFEMP1, KDR, S1PR1, CAV1, GRK5, EDNRA, EDNRB, CALCRL, PTGER4 and ADRB1 as potential prognostic markers in lung cancer patients.https://oss.jomh.org/files/article/20230731-62/pdf/JOMH2023042301.pdflung cancerppi analysisgeotcgakey gene |
| spellingShingle | Dan Song Li Sun Bioinformatics analysis identifies key genes of prognostic value in lung cancer Journal of Men's Health lung cancer ppi analysis geo tcga key gene |
| title | Bioinformatics analysis identifies key genes of
prognostic value in lung cancer |
| title_full | Bioinformatics analysis identifies key genes of
prognostic value in lung cancer |
| title_fullStr | Bioinformatics analysis identifies key genes of
prognostic value in lung cancer |
| title_full_unstemmed | Bioinformatics analysis identifies key genes of
prognostic value in lung cancer |
| title_short | Bioinformatics analysis identifies key genes of
prognostic value in lung cancer |
| title_sort | bioinformatics analysis identifies key genes of prognostic value in lung cancer |
| topic | lung cancer ppi analysis geo tcga key gene |
| url | https://oss.jomh.org/files/article/20230731-62/pdf/JOMH2023042301.pdf |
| work_keys_str_mv | AT dansong bioinformaticsanalysisidentifieskeygenesofprognosticvalueinlungcancer AT lisun bioinformaticsanalysisidentifieskeygenesofprognosticvalueinlungcancer |