Common Nevus and Skin Cutaneous Melanoma: Prognostic Genes Identified by Gene Co-Expression Network Analysis

A novel inflammatory response-related signature predicts the prognosis of cutaneous melanoma and the effect of antitumor drugs

Cutaneous melanoma (CM) is a skin cancer that is highly metastatic and aggressive, with a dismal prognosis. This is the first study to use inflammatory response-related genes to build a model and evaluate their predictive significance in CM. This study used public databases to download CM patients' mRNA expression profiles and clinical data to create multigene prognostic markers in the UCSC cohort. We compared overall survival (OS) between high- and low-risk groups using the Kaplan-Meier curve and determined independent predictors using Cox analysis. We also used enrichment analysis to assess immune cell infiltration fraction and immune pathway-related activity using KEGG enrichment analysis. Furthermore, we detected prognostic genes' mRNA and protein expression in CM and normal skin tissues using qRT-PCR and immunohistochemistry. Finally, we developed a 5-gene predictive model that showed that patients in the high-risk group had a considerably shorter OS than those in the low-risk group. The analysis of the receiver operating characteristic (ROC) curve proved the model's predictive ability. We also conducted a drug sensitivity analysis and discovered that the expression levels of prognostic genes were substantially linked with cancer cell sensitivity to antitumor medicines. The findings show that the model we developed, which consists of five inflammatory response-related genes, can be used to forecast the prognosis and immunological state of CM, giving personalized and precision medicine a new goal and direction.

Construction of immunotherapy-related prognostic gene signature and small molecule drug prediction for cutaneous melanoma

Background

Cutaneous melanoma (CM), a kind of skin cancer with a high rate of advanced mortality, exhibits a wide variety of driver and transmitter gene alterations in the immunological tumor microenvironment (TME) associated with tumor cell survival and proliferation.

Methods

We analyzed the immunological infiltration of TME cells in normal and malignant tissues using 469 CM and 556 normal skin samples. We used a single sample gene set enrichment assay (ssGSEA) to quantify the relative abundance of 28 cells, then used the LASSO COX regression model to develop a riskScore prognostic model, followed by a small molecule drug screening and molecular docking validation, which was then validated using qRT-PCR and IHC.

Results

We developed a prognosis model around seven essential protective genes for the first time, dramatically elevated in tumor tissues, as did immune cell infiltration. Multivariate Cox regression results indicated that riskScore is an independent and robust prognostic indicator, and its predictive value in immunotherapy was verified. Additionally, we identified Gabapentin as a possible small molecule therapeutic for CM.

Conclusions

A riskScore model was developed in this work to analyze patient prognosis, TME cell infiltration features, and treatment responsiveness. The development of this model not only aids in predicting patient response to immunotherapy but also has significant implications for the development of novel immunotherapeutic agents and the promotion of tailored treatment regimens.

The Multi-Omics Landscape and Clinical Relevance of the Immunological Signature of Phagocytosis Regulators: Implications for Risk Classification and Frontline Therapies in Skin Cutaneous Melanoma

Simple Summary

In this study, we focused on exploring phagocytosis regulators’ expression and mutational characteristics in skin cutaneous melanoma samples and delineating two molecular subtypes based on expression characteristics. We determined the relationship between phagocytosis regulators and survival by survival analysis of molecular subtypes. We then constructed a survival model (PRRS) to further quantify the criteria. Moreover, we combined pathway analysis, immune infiltration analysis, and mutation analysis to deeply explore the effects of phagocytosis regulators on skin cutaneous melanoma samples.

Abstract

Tumor-associated macrophages (TAMs) have gained considerable attention as therapeutic targets. Monoclonal antibody treatments directed against tumor antigens contribute significantly to cancer cell clearance by activating macrophages to phagocytose tumor cells. Due to its complicated genetic and molecular pathways, skin cutaneous melanoma (SKCM) has not yet attained the expected clinical efficacy and prognosis when compared to other skin cancers. Therefore, we chose TAMs as an entrance point. This study aimed to thoroughly assess the dysregulation and regulatory role of phagocytosis regulators in SKCM, as well as to understand their regulatory patterns in SKCM. This study subtyped prognosis-related phagocytosis regulators to investigate prognostic differences between subtypes. Then, we screened prognostic factors and constructed phagocytosis-related scoring models for survival prediction using differentially expressed genes (DEGs) between subtypes. Additionally, we investigated alternative treatment options using chemotherapeutic drug response data and clinical cohort treatment data. We first characterized and generalized phagocytosis regulators in SKCM and extensively examined the tumor immune cell infiltration. We created two phagocytosis regulator-related system (PRRS) phenotypes and derived PRRS scores using a principal component analysis (PCA) technique. We discovered that subtypes with low PRRS scores had a poor prognosis and decreased immune checkpoint-associated gene expression levels. We observed significant therapeutic and clinical improvements in patients with higher PRRS scores. Our findings imply that the PRRS scoring system can be employed as an independent and robust prognostic biomarker, serving as a critical reference point for developing novel immunotherapeutic methods.

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis

Lung adenocarcinoma (LUAD) is often diagnosed at an advanced stage, so it is necessary to identify potential biomarkers for the early diagnosis and prognosis of LUAD. In our study, a gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) in order to obtain the key modules and genes correlated with LUAD prognosis. Four hub genes (HLF, CHRDL1, SELENBP1, and TMEM163) were screened out using least absolute shrinkage and selection operator (LASSO)–Cox regression analysis; then, a prognostic model was established for predicting overall survival (OS) based on these four hub genes..Furthermore, the prognostic values of this four-gene signature were verified in four validation sets (GSE26939, GSE31210, GSE72094, and TCGA-LUAD) as well as in the GEPIA database. To assess the prognostic values of hub genes, receiver operating characteristic (ROC) curves were constructed and a nomogram was created. We found that a higher expression of four hub genes was associated with a lower risk of patient death. In a training set, it was demonstrated that this four-gene signature was a better prognostic factor than clinical factors such as age and stage of disease. Moreover, our results revealed that these four genes were suppressor factors of LUAD and that their high expression was associated with a lower risk of death. In summary, we demonstrated that this four-gene signature could be a potential prognostic factor for LUAD patients. These findings provide a theoretical basis for exploring potential biomarkers for LUAD prognosis prediction in the future.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

Identification and Validation of Immune- and Stemness-Related Prognostic Signature of Melanoma

Purpose: Our aim was to construct a signature that accurately predicted the prognostic and immune response of melanoma. Methods: First, the weighted co-expression network analysis (WGCNA) algorithm was used to identify the hub genes related to clinical phenotypes of melanoma in the cancer genome atlas (TCGA) database. Nest, the least absolute shrinkage and selection operator (LASSO) analysis was used to dimensionality reduction of these hub genes and constructed a prognostic signature to predict the prognosis and immunosuppressive response of melanoma. Result: Through in-depth analysis, we constructed a 5-mRNA prognostic signature and verified its prognostic value in internal (TCGA-SKCM, n = 452) and external independent datasets (GSE53118, n = 79). Based on this signature, the tumor immune microenvironment (TME) of melanoma was characterized, and the result was found that patients in the high-risk group had lower CD8 T cell infiltration and immune checkpoint expression (PD-1, PD-L1, CTLA4), as well as higher M0/M2 macrophage infiltration. Our results also found the risk score based on a 5-mRNA signature was significantly associated with tumor mutational burden (TMB) and tumor stem cell markers (CD20, CD38, ABCB5, CD44, etc.). Lastly, we built a nomogram for clinician prediction for the prognosis of patients with melanoma. Conclusion: Our findings indicated that the 5-mRNA signature has an important predictive value for the overall survival of melanoma. By analyzing the tumor immune microenvironment and tumor stem cell marker between different groups, a new method is provided for the stratified diagnosis and treatment of melanoma.

Seven key hub genes identified by gene co-expression network in cutaneous squamous cell carcinoma

Background

Cutaneous squamous cell carcinoma (cSCC) often follows actinic keratosis (AK) and is the second most common skin cancer worldwide. To reduce metastasis risk, it is important to diagnose and treat cSCC early. This study aimed to identify hub genes associated with cSCC and AK.

Methods

This study used three datasets GSE45216, GSE98774, and GSE108008. We combined samples from the GSE45216 and GSE98774 datasets into the new dataset GSE45216–98774. We applied a weighted gene co-expression network analysis (WGCNA) to investigate key modules and hub genes associated with cSCC and AK. We considered the hub genes found in both the GSE45216–98774 and GSE108008 datasets as validated hub genes. We tested whether the expression of hub genes could predict patient survival outcomes in other cancers using TCGA pan-cancer data.

Results

We identified modules most relevant to cSCC and AK. Additionally, we identified and validated seven hub genes of cSCC: GATM, ARHGEF26, PTHLH, MMP1, POU2F3, MMP10 and GATA3. We did not find validated hub genes for AK. Each hub gene was significantly associated with the survival of various cancer types. Only GATA3 was significantly associated with melanoma survival.

Conclusions

We applied WGCNA to find seven hub genes that play important roles in cSCC tumorigenesis. These results provide new insights that help explain the pathogenesis of cSCC. These hub genes may become biomarkers or therapeutic targets for accurate diagnosis and treatment of cSCC in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08604-y.

Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

BACKGROUND

Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis.

METHODS

We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs.

RESULTS

Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected.

CONCLUSION

The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

Novel oxidative stress-related prognostic biomarkers for melanoma associated with tumor metastasis

Skin cutaneous melanoma (SKCM) is a prevalent skin cancer whose metastatic form is dangerous due to its high morbidity and mortality. Previous studies have systematically established the vital role of oxidative stress (OS) in melanoma progression. This study aimed to identify prognostic OS genes closely associated with SKCM and illustrate their potential mechanisms. Transcriptome data and corresponding clinical traits of patients with SKCM were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. A weighted gene co-expression network analysis was conducted to identify relationships between clinical features and OS genes in specific modules. Subsequently, Cox regression analysis was performed on candidate OS genes; four hub prognosis-associated OS genes (AKAP9, VPS13C, ACSL4, and HMOX2) were identified to construct a prognostic model. After a series of bioinformatics analysis, our prognostic model was identified significantly associated with the overall survival of patients with SKCM and metastatic ability of the cancer. Furthermore, our risk model demonstrated improved diagnostic accuracy in the Cancer Genome Atlas and Gene Expression Omnibus cohorts. In addition, we established 2 nomograms based on either risk score or hub genes, which displayed favorable discriminating ability for SKCM. Our results provide novel insight into the potential applications of OS-associated genes in SKCM.

LncRNA MIR205HG regulates melanomagenesis via the miR-299-3p/VEGFA axis

In this study, we investigated the role of lncRNA MIR205HG in melanomagenesis. Quantitative real-time PCR (qRT-PCR) analysis showed that MIR205HG levels were significantly upregulated in melanoma cell lines compared to normal human melanocytes. Similarly, MIR205HG levels were significantly higher melanoma tissues than adjacent normal skin tissues (n=30). CCK-8 and flow cytometry assays showed that MIR205HG knockdown significantly decreased the viability of melanoma cells. Dual luciferase reporter and RNA pull-down assays confirmed that MIR205HG directly binds to microRNA (miR)-299-3p. Targetscan analysis and dual luciferase reporter assays showed that miR-299-3p directly binds to the 3'UTR of VEGFA mRNA. Wound healing and transwell invasion assays showed that MIR205HG knockdown decreased in vitro migration and invasiveness of melanoma cells, and these effects were reversed by treatment with miR-299-3p inhibitor. MIR205HG-silenced melanoma cells showed increased miR-299-3p expression and lower levels of both VEGFA mRNA and protein. Tumor volumes were significantly smaller in nude mice xenografted with MIR205HG knockdown melanoma cells than the controls. These results demonstrate that MIR205HG supports melanoma growth via the miR-299-3p/VEGFA axis. This makes MIR205HG a potential therapeutic target for the treatment of melanoma.

A Comprehensive Prognostic and Immunological Analysis of a New Three-Gene Signature in Hepatocellular Carcinoma

There are few reports on the role of genes associated with the mRNA expression-based stemness index (mRNAsi) in the prognosis and immune regulation of hepatocellular carcinoma (HCC). This study is aimed at analyzing the expression profile and prognostic significance of a new mRNAsi-based three-gene signature in HCC. This three-gene signature was identified by analyzing mRNAsi data from the Cancer Genome Atlas (TCGA) HCC dataset. The prognostic value of the risk score based on the three-gene signature was evaluated by Cox regression and Kaplan-Meier analysis and then verified in the International Cancer Genome Consortium (ICGC) database. Meanwhile, the correlations between the risk score and immune cell infiltration patterns, microsatellite instability (MSI), tumor mutation burden (TMB), immune checkpoint molecules, hypoxia-related genes, immunotherapy response, and compounds targeting the gene signature were explored, respectively. The results showed that compared with normal liver tissues, the mRNAsi score of HCC tissues was significantly increased. PTDSS2, MRPL9, and SOCS were the genes most related to mRNAsi in HCC tissues. Survival analysis results suggested the risk score based on the three-gene signature was an independent predictor of the prognosis for patients with HCC. The nomogram combining the risk score and pathological stage showed a good predictive ability for the overall survival of patients with HCC patients. Meanwhile, the risk score was significantly related to immune cell infiltration patterns, MSI, TMB, several immune checkpoint molecules, and hypoxia-related genes. In addition, the risk score was associated with the immunotherapy response, and fifteen potential therapeutic drugs targeting the three-gene signature were identified. Therefore, we propose to use this three-gene signature including PTDSS2, MRPL9, and SOCS as a potential prognostic biomarker for HCC.

Immune Microenvironment Related Competitive Endogenous RNA Network as Powerful Predictors for Melanoma Prognosis Based on WGCNA Analysis

Cutaneous melanoma is the most life-threatening skin malignant tumor due to its increasing metastasis and mortality rate. The abnormal competitive endogenous RNA network promotes the development of tumors and becomes biomarkers for the prognosis of various tumors. At the same time, the tumor immune microenvironment (TIME) is of great significance for tumor outcome and prognosis. From the perspective of TIME and ceRNA network, this study aims to explain the prognostic factors of cutaneous melanoma systematically and find novel and powerful biomarkers for target therapies. We obtained the transcriptome data of cutaneous melanoma from The Cancer Genome Atlas (TCGA) database, 3 survival-related mRNAs co-expression modules and 2 survival-related lncRNAs co-expression modules were identified through weighted gene co-expression network analysis (WCGNA), and 144 prognostic miRNAs were screened out by univariate Cox proportional hazard regression. Cox regression model and Kaplan-Meier survival analysis were employed to identify 4 hub prognostic mRNAs, and the prognostic ceRNA network consisting of 7 lncRNAs, 1 miRNA and 4 mRNAs was established. After analyzing the composition and proportion of total immune cells in cutaneous melanoma microenvironment through CIBERSORT algorithm, it is found through correlation analysis that lncRNA-TUG1 in the ceRNA network was closely related to the TIME. In this study, we first established cutaneous melanoma’s TIME-related ceRNA network by WGCNA. Cutaneous melanoma prognostic markers have been identified from multiple levels, which has important guiding significance for clinical diagnosis, treatment, and further scientific research on cutaneous melanoma.

Identification and Validation of Prognostically Relevant Gene Signature in Melanoma

Background

Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis.

Method

The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis.

Results

We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients.

Conclusions

We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.

Long Non-Coding RNA HAND2-AS1 Inhibits Growth and Migration of Gastric Cancer Cells Through Regulating the miR-590-3p/KCNT2 Axis

Introduction

Long non-coding RNAs (lncRNAs) are regarded as crucial regulators for cancer initiation and progression. Heart and Neural Crest Derivatives Expressed 2 antisense RNA 1 (HAND2-AS1) was recently proposed to function as tumor suppressor in several human cancers. However, its role in gastric cancer (GC) remains unclear.

Methods

HAND2-AS1 expression in GC tissues and normal tissues was analyzed at GEPIA (a web server for gene expression profiling analysis). Moreover, RT-qPCR method was utilized to explore HAND2-AS1 expression in GC cells and normal cell. In vitro experiments were carried out using cell counting kit-8 assay, colony formation assay, and flow cytometry assay, respectively. Bioinformatic analysis and luciferase activity reporter assay were performed to identify the downstream targets of HAND2-AS1.

Results

We found HAND2-AS1 has decreased expression in both GC tissues and cells. Overexpression of HAND2-AS1 was able to inhibit GC cell proliferation, colony formation, but promote apoptosis. On the contrary, knockdown of HAND2-AS1 could cause the opposite effects on GC cells. Furthermore, HAND2-AS1 was shown to function as a competitive RNA that binds with microRNA-590-3p (miR-590-3p) to affect the expression of potassium sodium-activated channel subfamily T member 2 (KCNT2).

Discussion

Our results indicated the tumor suppressive role of HAND2-AS1 in GC. Also, the newly identified HAND2-AS1/miR-590-3p/KCNT2 axis will help us to understand the role of HAND2-AS1 in cancer.

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC.

METHODS

We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice.

RESULTS

Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice.

CONCLUSION

This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.