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

ALDH2 is a novel biomarker and exerts an inhibitory effect on melanoma

Melanoma is a malignant skin tumor. This study aimed to explore and assess the effect of novel biomarkers on the progression of melanoma. Differently expressed genes (DEGs) were screened from GSE3189 and GSE46517 datasets of Gene Expression Omnibus database using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted based on the identified DEGs. Hub genes were identified and assessed using protein-protein interaction networks, principal component analysis, and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction was employed to measure the mRNA expression levels. TIMER revealed the association between aldehyde dehydrogenase 2 (ALDH2) and tumor immune microenvironment. The viability, proliferation, migration, and invasion were detected by cell counting kit-8, 5-ethynyl-2'-deoxyuridine, wound healing, and transwell assays. Total 241 common DEGs were screened out from GSE3189 and GSE46517 datasets. We determined 6 hub genes with high prediction values for melanoma, which could distinguish tumor samples from normal samples. ALDH2, ADH1B, ALDH3A2, DPT, EPHX2, and GATM were down-regulated in A375 and SK-MEL-2 cells, compared with the human normal melanin cell line (PIG1 cells). ALDH2 was selected as the candidate gene in this research, presenting a high diagnostic and predictive value for melanoma. ALDH2 had a positive correlation with the infiltrating levels of immune cells in melanoma microenvironment. Overexpression of ALDH2 inhibited cell viability, proliferation, migration, and invasion of A375/SK-MEL-2 cells. ALDH2 is a new gene biomarker of melanoma, which exerts an inhibitory effect on melanoma.

Transcriptional survey of abiotic stress response in maize (Zea mays) in the level of gene co-expression network and differential gene correlation analysis

Abstract. Maize may be exposed to several abiotic stresses in the field. Therefore, identifying the tolerance mechanisms of natural field stress is mandatory. Gene expression data of maize upon abiotic stress were collected, and 560 differentially expressed genes (DEGs) were identified through meta-analysis. The most significant gene ontology terms in up-regulated genes were 'response to abiotic stress' and 'chitinase activity'. 'Phosphorelay signal transduction system' was the most significant enriched biological process in down-regulated DEGs. The co-expression analysis unveiled seven modules of DEGs, with a notable positive correlation between the modules and abiotic stress. Furthermore, the statistical significance was strikingly high for the turquoise, green and yellow modules. The turquoise group played a central role in orchestrating crucial adaptations in metabolic and stress response pathways in maize when exposed to abiotic stress. Within three up-regulated modules, Zm.7361.1.A1_at, Zm.10386.1.A1_a_at and Zm.10151.1.A1_at emerged as hub genes. These genes might introduce novel candidates implicated in stress tolerance mechanisms, warranting further comprehensive investigation and research. In parallel, the R package glmnet was applied to fit a logistic LASSO regression model on the DEGs profile to select candidate genes associated with abiotic responses in maize. The identified hub genes and LASSO regression genes were validated on an independent microarray dataset. Additionally, Differential Gene Correlation Analysis (DGCA) was performed on LASSO and hub genes to investigate the gene-gene regulatory relationship. The P value of DGCA of 16 pairwise gene comparisons was lower than 0.01, indicating a gene-gene significant change in correlation between control and abiotic stress. Integrated weighted gene correlation network analysis and logistic LASSO analysis revealed Zm.11185.1.S1_at, Zm.2331.1.S1_x_at and Zm.17003.1.S1_at. Notably, these 3 genes were identified in the 16 gene-pair comparisons. This finding highlights the notable significance of these genes in the abiotic stress response. Additional research into maize stress tolerance may focus on these three genes.

POU2F3-Expressing Small Cell Lung Carcinoma and Large Cell Neuroendocrine Carcinoma Show Morphologic and Phenotypic Overlap

Considering the differences in protein expression in small cell lung carcinoma (SCLC) by molecular classification, it is likely that there are differences in morphology, but the relationship between molecular classification and morphology has not been examined. Furthermore, there are limited reports concerning this molecular classification for large cell neuroendocrine carcinoma (LCNEC) and SCLC simultaneously. Therefore, we investigated the relationship between immunohistochemistry-based molecular classification and morphology, protein expression, and clinical features of 146 consecutive resection specimens of pulmonary neuroendocrine carcinoma (NEC), focusing mainly on POU2F3, the master transcription factor involved in tuft cell generation. POU2F3-dominant SCLC (n=24) and LCNEC (n=14) showed overlap in cytomorphology, while non-POU2F3-dominant SCLC (n=71) and LCNEC (n=37) showed distinct differences in cytomorphology. In addition, POU2F3-dominant NEC exhibited significantly more abundant tumor stroma, more prominent nest formation, more frequent bronchial intraepithelial involvement, and less frequent background fibrosis than non-POU2F3-dominant NEC. Immunohistochemically, POU2F3-dominant SCLC and LCNEC were characterized by lower expression of TTF-1, CEA, and neuroendocrine markers and higher expression of bcl-2, c-Myc, and c-kit. Clinically, POU2F3-dominant NEC had a significantly better prognosis than non-POU2F3-dominant NEC for recurrence-free survival. POU2F3-dominant NEC had a higher smoking index than non-POU2F3-dominant NEC. POU2F3-dominant NEC forms a unique population, exhibiting intermediate morphologic features between SCLC and LCNEC, with distinct protein expression as tuft cell-like carcinoma. Recognition of this unique subtype may provide clues for solving the long-standing issues of NEC and appropriate therapeutic stratification. It is important to accurately identify POU2F3-expressing carcinomas by immunohistochemistry and to analyze their clinicopathological features.

POU2F3: A Sensitive and Specific Diagnostic Marker for Neuroendocrine-low/negative Small Cell Lung Cancer

POU2F3 (POU class 2 homeobox 3) is a novel transcription factor used to define the special molecular subtype of small cell lung cancer (SCLC) known as SCLC-P. Nevertheless, the sensitivity and specificity of POU2F3 immunohistochemical (IHC) staining have not been fully investigated. In this study, we explored the expression of POU2F3 by IHC in a large cohort of SCLC clinical samples (n=246), other common lung cancer types (n=2207), and various other cancer types (n=194). The results showed that POU2F3 was strongly nuclear stained in 13.41% (33/246) of SCLC cases, with negative or minimal labeling for thyroid transcription factor-1 and neuroendocrine (NE) markers. Compared with POU2F3-negative SCLC, SCLC-P harbored fewer TP53 and RB1 mutations. POU2F3 was also expressed in 3.13% (8/256) of squamous cell carcinomas (SCCs) and 20% (2/10) of large cell NE carcinomas (LCNECs), whereas other lung cancer types were negative. In addition to lung cancer, POU2F3 was positive in 22.2% (4/18) of thymic tumors. All other tumors were POU2F3-negative except for thymic carcinoma, although sparsely distributed weak nuclear staining was observed in lung adenocarcinoma, cervical SCC, and colorectal carcinoma. The sensitivity and specificity of POU2F3 in NE-low/negative SCLC were 82.1% and 99.4%, respectively. Notably, some rare unique patterns of POU2F3 expression were observed. One case of thymic SCC was characterized by diffuse and uniform cytomembrane staining. One case of esophageal NE tumor was nuclear-positive, while the normal proliferating squamous epithelium was strongly membrane-stained. This is the largest cohort of clinical samples to confirm that POU2F3 is a highly sensitive and specific diagnostic marker for NE-low/negative SCLC.

Identification of co-expressed gene networks promoting CD8+ T cell infiltration and having prognostic value in uveal melanoma

Current immunotherapies are unsatisfactory against uveal melanoma (UM); however, elevated CD8+ T cell infiltration level indicates poor prognosis in UM. Here, we aimed to identify co-expressed gene networks promoting CD8+ T cell infiltration in UM and created a prognostic hazard model based on the identified hub genes. Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Stromal-immune comprehensive score (ESTIMATE) was used to evaluate the immune-infiltration landscape of the tumor microenvironment. Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Correlation Network Analysis (WGCNA) were used to quantify CD8+ T cell infiltration level and identify hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to analyze the biological processes. Least absolute shrinkage and selection operator (LASSO) Cox regression were used to establish a prognostic model, which was further validated. Finally, pan-cancer analysis evaluated these genes to be associated with CD8+ T cell infiltration in other tumors. In conclusion, the proposed four-gene (PTPN12, IDH2, P2RX4, and KDELR2) prognostic hazard model had satisfactory prognostic ability. These hub genes may promote CD8+ T cell infiltration in UM through antigen presentation, and CD8+ T cell possibly function as Treg, resulting in poor prognosis. These findings might facilitate the development of novel immunotherapies.

Transcriptomic Study on Human Skin Samples: Identification of Two Subclasses of Actinic Keratoses

Actinic keratoses (AKs) are sun-damaged skin areas that affect 20% of the European adult population and more than 50% of people aged 70 years and over. There are currently no clinical or histological features allowing us to identify to which clinical class (i.e., regression or progression) an AK belongs. A transcriptomic approach seems to be a robust tool for AK characterization, but there is a need for additional studies, including more patients and elucidating the molecular signature of an AK. In this context, the present study, including the largest number of patients to date, is the first aiming at identifying biological features to objectively distinguish different AK signatures. We highlight two distinct molecular profiles: AKs featuring a molecular profile similar to squamous cell carcinomas (SCCs), which are called "lesional AKs" (AK_Ls), and AKs featuring a molecular profile similar to normal skin tissue, which are called "non-lesional AKs" (AK_NLs). The molecular profiles of both AK subclasses were studied, and 316 differentially expressed genes (DEGs) were identified between the two classes. The 103 upregulated genes in AK_L were related to the inflammatory response. Interestingly, downregulated genes were associated with keratinization. Finally, based on a connectivity map approach, our data highlight that the VEGF pathway could be a promising therapeutic target for high-risk lesions.

Parkinson's Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. Gene expression profiles were screened from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) were selected for the enrichment analysis. A protein-protein interaction (PPI) network was built with the STRING database (Search Tool for the Retrieval of Interacting Genes), and two ML approaches, namely least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were employed to identify candidate genes. The external validation dataset further tested the expression degree and diagnostic value of candidate biomarkers. To assess the validity of the diagnosis, we determined the receiver operating characteristic (ROC) curve. A convolution tool was employed to evaluate the composition of immune cells by CIBERSORT, and we performed correlation analyses on the basis of the training dataset. Twenty-seven DEGs were screened in the PD and control samples. Our results from the enrichment analysis showed a close association with inflammatory and immune-associated diseases. Both the LASSO and SVM algorithms screened eight and six characteristic genes. AGTR1, GBE1, TPBG, and HSPA6 are overlapping hub genes strongly related to PD. Our results of the area under the ROC (AUC), including AGTR1 (AUC = 0.933), GBE1 (AUC = 0.967), TPBG (AUC = 0.767), and HSPA6 (AUC = 0.633), suggested that these genes have good diagnostic value, and these genes were significantly associated with the degree of immune cell infiltration. AGTR1, GBE1, TPBG, and HSPA6 were identified as potential biomarkers in the diagnosis of PD and provide a novel viewpoint for further study on PD immune mechanism and therapy.

Bioinformatic Exploration of Hub Genes and Potential Therapeutic Drugs for Endothelial Dysfunction in Hypoxic Pulmonary Hypertension

Hypoxic pulmonary hypertension (HPH) is a fatal chronic pulmonary circulatory disease, characterized by hypoxic pulmonary vascular constriction and remodeling. Studies performed to date have confirmed that endothelial dysfunction plays crucial roles in HPH, while the underlying mechanisms have not been fully revealed. The microarray dataset GSE11341 was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between hypoxic and normoxic microvascular endothelial cell, followed by Gene Ontology (GO) annotation/Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis, and protein-protein interaction (PPI) network construction. Next, GSE160255 and RT-qPCR were used to validate hub genes. Meanwhile, GO/KEGG and GSEA were performed for each hub gene to uncover the potential mechanism. A nomogram based on hub genes was established. Furthermore, mRNA-miRNA network was predicted by miRNet, and the Connectivity Map (CMAP) database was in use to identify similarly acting therapeutic candidates. A total of 148 DEGs were screened in GSE11341, and three hub genes (VEGFA, CDC25A, and LOX) were determined and validated via GSE160255 and RT-qPCR. Abnormalities in the pathway of vascular smooth muscle contraction, lysosome, and glycolysis might play important roles in HPH pathogenesis. The hub gene-miRNA network showed that hsa-mir-24-3p, hsa-mir-124-3p, hsa-mir-195-5p, hsa-mir-146a-5p, hsa-mir-155-5p, and hsa-mir-23b-3p were associated with HPH. And on the basis of the identified hub genes, a practical nomogram is developed. To repurpose known and therapeutic drugs, three candidate compounds (procaterol, avanafil, and lestaurtinib) with a high level of confidence were obtained from the CMAP database. Taken together, the identification of these three hub genes, enrichment pathways, and potential therapeutic drugs might have important clinical implications for HPH diagnosis and treatment.

A Necroptosis-Related Gene Signature to Predict the Prognosis of Skin Cutaneous Melanoma

The prognosis of skin cutaneous melanoma (SKCM) remains poor, and patients with SKCM show a poor response to immunotherapy. Thus, we aimed to identify necroptosis-related biomarkers, which can help predict the prognosis of SKCM and improve the effectiveness of precision medicine. Data of SKCM were obtained from The Cancer Genome Atlas (TCGA) and GEO databases. TCGA samples were classified into two clusters by consensus clustering of necroptosis-related genes. Univariate Cox and least absolute shrinkage and selection operator regression analyses led to the identification of 11 genes, which were used to construct a prognostic model. GSE65904 was used as the test set. Principal component, t-distributed stochastic neighbor embedding, and Kaplan–Meier survival analyses indicated that samples in the train and test sets could be divided into two groups, with the high-risk group showing a worse prognosis. Univariate and multivariate Cox regression analyses were performed, and a nomogram, calibration curve, and time-dependent receiver operating characteristic curve were constructed to verify the efficacy of our model. The 1-, 3-, and 5-year areas under the receiver operating characteristic curves for the train set were 0.702, 0.663, and 0.701 and for the test set were 0.613, 0.627, and 0.637, respectively. Moreover, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses between the high- and low-risk groups. Single sample gene set enrichment analysis, immune cell infiltration analysis, tumor microenvironment scores, immune checkpoint analysis, and half-maximal inhibitory concentration prediction indicated that the high-risk group showed weaker antitumor immunity; further, the response to immune checkpoint inhibitors was worse, and the high-risk group was sensitive to fewer antitumor drugs. Tumor mutational burden analysis, Kaplan–Meier survival analysis, and correlation analysis between risk score and RNA stemness score revealed that the high-risk group with low tumor mutational burden and high RNA stemness score was potentially associated with poor prognosis. To conclude, our model, which was based on 11 necroptosis-related genes, could predict the prognosis of SKCM; in addition, it has guiding significance for the selection of clinical treatment and provides new research directions to enhance necroptosis against SKCM.

Non-Melanoma Skin Cancer: A Genetic Update and Future Perspectives

Simple Summary

Non-melanoma skin cancer (NMSC) is the main type of cancer in the Caucasian population, and the number of cases continues to rise. Research mostly focuses on clinical characteristics analysis, but genetic features are crucial to malignancies’ establishment and advance. We aim to explore the genetic basics of skin cancer, surrounding microenvironment interactions, and regulation mechanisms to provide a broader perspective for new therapies’ development.

Abstract

Skin cancer is one of the main types of cancer worldwide, and non-melanoma skin cancer (NMSC) is the most frequent within this group. Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common types. Multifactorial features are well-known for cancer development, and new hallmarks are gaining relevance. Genetics and epigenetic regulation play an essential role in cancer susceptibility and progression, as well as the variety of cells and molecules that interact in the tumor microenvironment. In this review, we provide an update on the genetic features of NMSC, candidate genes, and new therapies, considering diverse perspectives of skin carcinogenesis. The global health situation and the pandemic have been challenging for health care systems, especially in the diagnosis and treatment of patients with cancer. We provide innovative approaches to overcome the difficulties in the current clinical dynamics.

Integrative analysis reveals methylenetetrahydrofolate dehydrogenase 1-like as an independent shared diagnostic and prognostic biomarker in five different human cancers

BACKGROUND

Defects in methylenetetrahydrofolate dehydrogenase 1-like (MTHFD1L) expression have earlier been examined in only a few human cancers.

OBJECTIVES

Multi-omics profiling of MTHFD1L as a shared biomarker in distinct subtypes of human cancers.

METHODS

In the current study, for the multi-omics analysis of MTHFD1L in 24 major subtypes of human cancers, a comprehensive in silico approach was adopted to mine different open access online databases including UALCAN, Kaplan-Meier (KM) plotter, LOGpc, GEPIA, Human Protein Atlas (HPA), Gene Expression across Normal and Tumor tissue (GENT2), MEXPRESS, cBioportal, STRING, DAVID, TIMER, and Comparative Toxicogenomics Database (CTD).

RESULTS

We noticed that the expression of MTHFD1L was significantly higher in all the analyzed 24 subtypes of human cancers as compared with the normal controls. Moreover, MTHDF1L overexpression was also found to be significantly associated with the reduced overall survival (OS) duration of Bladder urothelial cancer (BLCA), Head and neck cancer (HNSC), Kidney renal papillary cell carcinoma (KIRP), Lung adenocarcinoma (LUAD), and Uterine corpus endometrial carcinoma (UCEC). This implies that MTHFD1L plays a significant role in the development and progression of these cancers. We further noticed that MTHFD1L was also overexpressed in BLCA, HNSC, KIRP, LUAD, and UCEC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of MTHFD1L-associated genes in five diverse pathways. We also explored few interesting correlations between MTHFD1L expression and its promoter methylation, genetic alterations, CNVs, and between CD8+ T immune cells level.

CONCLUSION

In conclusion, our results elucidated that MTHFD1L can serve as a shared diagnostic and prognostic biomarker in BLCA, HNSC, KIRP, LUAD, and UCEC patients of different clinicopathological features.