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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Skin Cutaneous Melanoma Using Integrated Bioinformatic Analysis

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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Skin Cutaneous Melanoma Using Integrated Bioinformatic Analysis European Review for Medical and Pharmacological Sciences 2021; 25: 2927-2940 Identification of hub prognosis-associated oxidative stress genes in skin cutaneous melanoma using integrated bioinformatic analysis T.-Y. REN1, Y.-X. ZHANG2, W. HU1 1Department of Spine and Joint Surgery, Guilin Peoples’ Hospital, Guilin, China 2Department of Anesthesia, Guilin Peoples’ Hospital, Guilin, China Tianyu Ren and Yuxuan Zhang contributed equally to this manuscript 1 Abstract. – OBJECTIVE: Oxidative stress threat to health . In 2018, 287,723 new patients (OS) significantly correlates with cancer pro- were diagnosed with melanoma around the world gression. However, targeting OS has not been and 21.1% of these patients passed away from the considered as a therapeutic strategy in skin cu- disease2. Metastatic SKCM results in the greater taneous melanoma (SKCM) due to a lack of sys- 3 tematical studies on validated biomarkers. The number of deaths related to skin tumors . Patients work presented here aimed to identify hub prog- diagnosed with SKCM in stages I and II have a nosis-associated OS genes in SKCM and gener- 10-year overall survival rate of 75 to 98%4. How- ated an effective predictive model. ever, one-third of these patients develop metastat- PATIENTS AND METHODS: Gene expression ic melanoma. In contrast, for SKCM patients di- profiles of SKCM samples and normal skin tis- agnosed in stages IIIA to IIID, the 10-year overall sues were obtained from the Genotype-Tissue Expression (GTEx) and The Cancer Genome At- survival rate is decreased and ranges between las (TCGA) databases to identify differentially 24-88%. These data suggest that early diagnosis expressed OS genes. The validation cohort was of SKCM is essential for a favorable outcome5. obtained from the Gene Expression Omnibus Even though some theories indicate that tumor- (GEO) database. RESULTS: igenesis and progression are related to skin pig- Thirteen hub prognosis-associat- mentation6,7 and that pathogenesis is associated ed OS genes were recognized and incorporated into the prognostic risk model. Our constructed with acquired melanocytic nevi, family history 8,9 model was significantly associated with overall and genetic susceptibility , but this pathogen- survival of SKCM patients as well as was shown esis behind SKCM is still not known. Accurate to be associated with cancer progression. Our diagnosis of SKCM at early stages is the main prognostic risk model was found to improve the objective. Some work is focusing on uncovering accuracy of diagnostics, as shown using both TCGA and GEO cohorts. Both hub gene expres- new biomarkers related to the prediction of pro- sion and risk score were used to generated no- gression and prognosis of SKCM that can also be mograms that displayed favorable discriminato- used for personalized treatment10,11. However, on- ry abilities for SKCM. ly a few clinically relevant biomarkers and tools CONCLUSIONS: Overall, our study presents a for SKCM are available12. It is thus necessary to model that may provide novel insights into the uncover additional biomarkers potentially able to prognosis and survival of SKCM patients, as well as the development of individualized treat- aid in the diagnosis and identifying the prognosis ment therapy. behind SKCM cases. Oxidative stress (OS) is the result of an imbal- Key Words: ance of oxidants and antioxidants and promotes Integrated bioinformatic analysis, Oxidative stress, Prognosis, Skin cutaneous melanoma. increased levels of reactive oxygen species (ROS). ROS includes singlet oxygens, hydrogen peroxide and superoxide anion13. The overproduction of ROS is observed in patients with SKCM and Introduction suggests that ROS may drive cancer development and progression14-17. The presence of excessive Skin cutaneous melanoma (SKCM) is an ag- ROS leads to DNA damage and genotoxicity18,19 gressive malignant tumor that poses a serious and increases the chances of mutations that can Corresponding Author: Wei Hu, MD: e-mail: [email protected] 2927 T.-Y. Ren, Y.-X. Zhang, W. Hu lead to cancer20,21. In skin cells, ROS are gener- tissue samples were obtained from the Universi- ated by the NADPH oxidase family of enzymes, ty of California Santa Cruz Xena (UCSC Xena; nitric oxide synthase, arachidonic acid oxygenase http://xena.ucsc.edu/)35. The Genotype-Tissue activities and mitochondria primarily in melano- Expression (GTEx) database (https://gtexportal. somes22. Even though melanin has protective ef- org/home/datasets) was used to obtain transcrip- fects on melanocytes when it comes to protecting tome data for 812 normal skin samples36,37. In cells against UV radiation, synthesis of melanin addition, the Gene Expression Omnibus (GEO) can also be harmful since it is associated with GSE65904 cohort (https://www.ncbi.nlm.nih. increased levels of intracellular ROS23,24. gov/geo/), which contained gene expression pro- Bioinformatics has been used to identify dis- files and clinical data for a total of 214 SKCM ease-specific biomarkers for SKCM25,26. Howev- patients, was used as a validation cohort38. R er, differentially expressed genes are identified software and specific packages were used to per- from a single analysis method which lacks dis- form bioinformatic analyses. Log2-transforma- criminatory ability for highly connected genes. tion and normalization using the “sva” package Meanwhile, sole focus on the expression levels of were performed to remove batch effects39,40. To individual genes ignores intergenomic epistasis. obtain OS-associated genes, GeneCards data- Weighted gene co-expression network (WGCN) base (https://www.genecards.org) was used to is used to evaluate the association between genes obtain 1399 protein domains for the specific OS and phenotypic traits rather than focusing on genes with a relevance score ≥ 7. individual gene expression27. During WGCN analysis (WGCNA), SKCM expression data can WGCN Construction and the be used to identify hub biomarkers for diagno- Identification of the Hub Module sis and prognosis28,29. Furthermore, differential A gene co-expression networking using the gene expression analysis of transcriptional da- “WGCNA” package41 was generated using OS ta is another powerful tool providing changes gene expression profiles for SKCM cases in the in quantitative expression levels between two TCGA. Pairwise Pearson’s correlation coeffi- subgroups30. In this study, candidate OS genes cients were determined between all the genes. differentially expressed in SKCM tissues vs. nor- The following formula was used to identify a mal skin were identified using WGCNA and weighted adjacency matrix: amn=|cmn|β (cmn = differential gene expression analysis to enhance Pearson’s correlation between gene m and gene the discriminatory ability of highly connected n; amn = adjacency between gene m and gene genes. Subsequently, univariate Cox regression n). Next, a parameter for “β” was identified to and least absolute shrinkage and selection opera- choose strong gene correlations. A topological tor (LASSO) analyses were also used to identify overlap matrix (TOM) transformed adjacencies. hub genes significantly related to SKCM progno- Average linkage hierarchical clustering was sis. A prognostic risk model was generated based used to construct OS gene dendrograms with on hub gene expression. The significance of each a minimum module size of 50. Dissimilarities gene was explored in SKCM patients. To date, of module eigengenes were calculated. Further- most prognostic risk models for SKCM were more, module eigengenes and gene significance mainly constructed based on tumor immunity31, revealed modules relevant to SKCM clinical miRNAs and lncRNA signatures32,33. However, traits. Genes in the functional module were la- these studies used simplified univariate analysis34 beled as candidates. and none systemically explored OS genes in SK- CM and their prognostic value. Thus, the study Differential Expression Analysis presented here uncovers the first OS-associated and Interactions risk model that can provide novel insight into Differential expression analysis using the diagnosis and prognosis of SKCM cases. “limma” package compared SKCM samples and normal skin tissues. Genes with a FDR < 0.05 and |log2 fold change (FC)| > 1 were regarded Patients and Methods as candidate DEOGs based on previous meth- ods42 and were visualized using a volcano plot Patients using the “ggplot2” package43. Furthermore, Both RNA-sequencing data and clinical in- genes that were overlapping between candidate formation for the 471 SKCM and 1 normal skin DEOGs, the WGCN and GSE65904 were con- 2928 OS-related genes identification in SKCM sidered as “real” DEOGs and were visualized Statistical Analysis using a Venn diagram generated by the “Venn- All statistical analyses were performed with Diagram” package44. SPSS (Statistical Product and Service Solution) software version 23.0 ((IBM Corp., Armonk, Construction of A Prognostic Model and NY, USA) 52. Measurement data were present- Evaluation of Its Efficacy ed as the mean ± SD (standard deviation), and All DEOGs were subjected to univariate Cox the difference between risk score and clinical regression analysis using the “survival” package features were compared with Student’s t-test with a cutoff criterion of p < 0.05. Thereafter, and χ2-test. Furthermore, log rank test was also genes from this analysis were integrated into applied for Kaplan-Meier survival analysis, and LASSO analysis45 to select hub OS genes and multivariate Cox regression analysis was used to generate a
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