Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments
Head and neck squamous cell carcinoma (HNSCC) ranks as the sixth most common cancer among systemic malignant tumors, with 600 000 new cases occurring every year worldwide. Since HNSCC has high heterogeneity and complex pathogenesis, no effective prognostic indicator has yet been identified. Here, we...
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| Format: | Article |
| Language: | English |
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Wiley
2021-07-01
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| Series: | FEBS Open Bio |
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| Online Access: | https://doi.org/10.1002/2211-5463.13134 |
| _version_ | 1818588779534876672 |
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| author | Shao Lina |
| author_facet | Shao Lina |
| author_sort | Shao Lina |
| collection | DOAJ |
| description | Head and neck squamous cell carcinoma (HNSCC) ranks as the sixth most common cancer among systemic malignant tumors, with 600 000 new cases occurring every year worldwide. Since HNSCC has high heterogeneity and complex pathogenesis, no effective prognostic indicator has yet been identified. Here, we aimed to identify a lncRNA signature associated with the prognosis of HNSCC as a potential new biomarker. LncRNA expression data were downloaded from The Cancer Genome Atlas database. A polygenic risk score model was constructed by using Lasso–Cox regression analysis. Weighted gene co‐expression network analysis (WGCNA) was applied to analyze the co‐expression modules of lncRNAs associated with the prognosis of HNSCC. The robustness of the signature was validated in testing and external cohorts. Polymerase chain reaction was performed to detect the expression levels of identified lncRNAs in cancer and adjacent tissues. We constructed an 8‐lncRNA signature (LINC00567, LINC00996, MTOR‐AS1, PRKG1‐AS1, RAB11B‐AS1, RPS6KA2‐AS1, SH3BP5‐AS1, ZNF451‐AS1) that could be used as an independent prognostic factor of HNSCC. The signature showed strong robustness and had stable prediction performance in different cohorts. WGCNA results showed that modules related to risk score mainly participated in biological processes such as blood vessel development, positive regulation of catabolic processes, and regulation of growth. The prognostic risk score model based on lncRNA for HNSCC may help clinicians conduct individualized treatment plans. |
| first_indexed | 2024-12-16T09:30:11Z |
| format | Article |
| id | doaj.art-7238d5c68a6545749a50d2e5e390773a |
| institution | Directory Open Access Journal |
| issn | 2211-5463 |
| language | English |
| last_indexed | 2024-12-16T09:30:11Z |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | FEBS Open Bio |
| spelling | doaj.art-7238d5c68a6545749a50d2e5e390773a2022-12-21T22:36:33ZengWileyFEBS Open Bio2211-54632021-07-011172060207310.1002/2211-5463.13134Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experimentsShao Lina0Department of Endodontics School and Hospital of Stomatology China Medical University Shenyang ChinaHead and neck squamous cell carcinoma (HNSCC) ranks as the sixth most common cancer among systemic malignant tumors, with 600 000 new cases occurring every year worldwide. Since HNSCC has high heterogeneity and complex pathogenesis, no effective prognostic indicator has yet been identified. Here, we aimed to identify a lncRNA signature associated with the prognosis of HNSCC as a potential new biomarker. LncRNA expression data were downloaded from The Cancer Genome Atlas database. A polygenic risk score model was constructed by using Lasso–Cox regression analysis. Weighted gene co‐expression network analysis (WGCNA) was applied to analyze the co‐expression modules of lncRNAs associated with the prognosis of HNSCC. The robustness of the signature was validated in testing and external cohorts. Polymerase chain reaction was performed to detect the expression levels of identified lncRNAs in cancer and adjacent tissues. We constructed an 8‐lncRNA signature (LINC00567, LINC00996, MTOR‐AS1, PRKG1‐AS1, RAB11B‐AS1, RPS6KA2‐AS1, SH3BP5‐AS1, ZNF451‐AS1) that could be used as an independent prognostic factor of HNSCC. The signature showed strong robustness and had stable prediction performance in different cohorts. WGCNA results showed that modules related to risk score mainly participated in biological processes such as blood vessel development, positive regulation of catabolic processes, and regulation of growth. The prognostic risk score model based on lncRNA for HNSCC may help clinicians conduct individualized treatment plans.https://doi.org/10.1002/2211-5463.13134head and neck cancerrisk score prognostic modelThe Cancer Genome AtlasWGCNA |
| spellingShingle | Shao Lina Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments FEBS Open Bio head and neck cancer risk score prognostic model The Cancer Genome Atlas WGCNA |
| title | Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments |
| title_full | Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments |
| title_fullStr | Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments |
| title_full_unstemmed | Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments |
| title_short | Identification of hub lncRNAs in head and neck cancer based on weighted gene co‐expression network analysis and experiments |
| title_sort | identification of hub lncrnas in head and neck cancer based on weighted gene co expression network analysis and experiments |
| topic | head and neck cancer risk score prognostic model The Cancer Genome Atlas WGCNA |
| url | https://doi.org/10.1002/2211-5463.13134 |
| work_keys_str_mv | AT shaolina identificationofhublncrnasinheadandneckcancerbasedonweightedgenecoexpressionnetworkanalysisandexperiments |