Screening and identification of potential prognostic biomarkers in metastatic skin cutaneous melanoma by bioinformatics analysis

Sheng, Zu-Feng; Han, Wei; Huang, Biao; Shen, Ge

doi:10.1111/jcmm.15822

Cited by 17 publications

(15 citation statements)

References 15 publications

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“…Some scholars ( 50 ) believe that the upregulation of SPRR is often accompanied by damage to the normal structure of the stratum corneum, which affects the function of epithelial barrier and leads to the decrease of skin hydration, antioxidation, wound healing, drug sensitivity and anti-infection ability. At the same time, some studies have shown that the expression of SPRR1B is down-regulated in metastatic melanoma ( 51 ). The results of TMA also confirmed that IGF2BP3 was significantly up-regulated in melanoma, and also confirmed that SPRR1B was significantly down-regulated in metastatic cancer before tumor invasion and metastasis.…”

Section: Discussionmentioning

confidence: 99%

Integration Analysis of m6A Related Genes in Skin Cutaneous Melanoma and the Biological Function Research of the SPRR1B

et al. 2021

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Melanoma has gradually entered the public view because of its high morbidity and rising prevalence rate, which is a serious threat to human life and health. Recently, N6-methyladenine (m6A) modification has been increasingly confirmed as a potential role in the development of tumogenesis. The purpose of this study is to explore the role and function of m6a-related regulators in the development of melanoma disease at the molecular, cellular and clinical levels through bioinformatics and traditional experiments. We screened and validated differential expression genes (DEGs) in m6A regulators via the GEO, GTEx, TCGA database. The biological processes and signaling pathway involved by DEGs were improved by constructing bioinformational methods such as PPI, GO enrichment, KEGG enrichment, GSEA enrichment, and immune infiltration analysis. And then, we explored the biological function of the key gene, SPRR1B, through cell invasion, migration, infiltration, and tissue chips. The gene IGF2BP3 which was differentially expressed in m6A regulatory factor gene was screened. The results of the enrichment analysis are significantly enriched in the biological processes and pathways of the skin barrier, epidermal differentiation, cytoskeleton, lymphocyte migration and other pathways, pointing to the direction of tumor immunity and tumor metastasis. Tumor immune-related genes YTHDC1, YTHDC2 and ALKBH5 were found. Knock SPRR1B reduction group had a significantly lower invasive ability, the ability to migrate. Nomogram prediction model shows that SPRR1B increased, expressing a worse prognosis. For this purpose, the relationship between m6A regulatory factor and melanoma progression was explored. At the same time, it was found that the abnormal up-regulated expression of SPRR1B before metastasis would lead to poor prognosis of melanoma. SPRR1B promotes the proliferation, invasion and migration of human melanoma cells.

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Section: Discussionmentioning

confidence: 99%

Integration Analysis of m6A Related Genes in Skin Cutaneous Melanoma and the Biological Function Research of the SPRR1B

et al. 2021

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“…Previous studies have suggested that SPRR1B is overexpressed in oral squamous cell carcinoma stem‐cell‐like cells, thereby affecting cell growth 22 . SPRR1B has also been identified as one of the predictive indicators for the prognosis of patients with pancreatic cancer 23 or metastatic transformation of melanoma 24 . In head and neck squamous cell carcinoma (HNSCC), SPRR1B affects HNSCC angiogenesis by targeting MDA‐9/Syntenin, thereby affecting tumor invasion and differentiation 25 .…”

Section: Discussionmentioning

confidence: 99%

Identification of small proline‐rich protein 1B (SPRR1B) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

Zhang

Shi

et al. 2021

Thoracic Cancer

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Background With the ongoing development of targeted therapy and immunotherapy in recent years, the overall five‐year survival rate of NSCLC patients has not improved, and the search for novel diagnostic and prognostic markers for lung adenocarcinoma continues. Methods Lung adenocarcinoma (LUAD) gene expression data and relevant clinical information were obtained from the TCGA. Hub genes were identified with weighted gene co‐expression network analysis (WGCNA) and protein–protein interaction network (PPI). Survival analyses were also performed using GEPIA. The 536 LUAD patients were divided into two groups according to the SPRR1B expression level and analyzed by gene set enrichment analysis (GSEA) and verified by immunoblotting. The effects of SPRR1B on cell proliferation and cell metastasis and apoptosis were evaluated by 5‐ethynyl‐2′‐deoxyuridine (EdU) staining, colony formation assay, transwell migration and invasion assay, and flow cytometry, respectively. Results A total of 2269 DEGs were analyzed by WGCNA and five hub genes (CCK, FETUB, PCSK9, SPRR1B, and SPRR2D) were identified. Among them, SPRR1B was selected as one of the most significant prognostic genes in LUAD. SPRR1B was found to be highly expressed in lung adenocarcinoma cells compared with that in normal bronchial epithelial cells. In addition, silencing of SPRR1B could inhibit the cell proliferation, invasion, and migration of lung adenocarcinoma cells, but induced cell apoptosis and G2/M phase arrest in vitro. The result of GSEA and immunoblotting revealed that SPRR1B activated the MAPK signaling pathway involved in the proliferation and metastasis of lung cancer. Conclusions Our findings demonstrate that SPRR1B may function as a prognosis predictor in lung adenocarcinoma.

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“…Several recent studies constructed protein-protein interaction (PPI) networks to identify hub genes in melanoma. Sheng et al constructed a PPI network to analyze differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) database [35]. The study identified DGS3, DSC3, PKP1, EVPL, IVL, FLG, SPRR1A, and SPRR1B as potential biomarkers that predict the metastases of cutaneous melanoma [35].…”

Section: Current Bioinformatics In Melanoma Risk Assessementmentioning

confidence: 99%

“…Sheng et al constructed a PPI network to analyze differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) database [35]. The study identified DGS3, DSC3, PKP1, EVPL, IVL, FLG, SPRR1A, and SPRR1B as potential biomarkers that predict the metastases of cutaneous melanoma [35]. Another study constructed a PPI network from melanoma gene expression data from UCSC Xena and GEO and found FOXM1, EXO1, KIF20A, TPX2, and CDC20 as genes associated with reduced overall survival [36].…”

Section: Current Bioinformatics In Melanoma Risk Assessementmentioning

confidence: 99%

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Hoegler

Zhou

2021

Genes

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Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

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Screening and identification of potential prognostic biomarkers in metastatic skin cutaneous melanoma by bioinformatics analysis

Cited by 17 publications

References 15 publications

Integration Analysis of m6A Related Genes in Skin Cutaneous Melanoma and the Biological Function Research of the SPRR1B

Integration Analysis of m6A Related Genes in Skin Cutaneous Melanoma and the Biological Function Research of the SPRR1B

Identification of small proline‐rich protein 1B (SPRR1B) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

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