Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis

Identification of key genes in diabetic nephropathy based on lipid metabolism

Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes with a high incidence rate. Notably, the disturbance of lipid metabolism is associated with DN progression. The present study aimed to identify lipid metabolism-related hub genes associated with DN for improved diagnosis of DN. The gene expression profile data of DN and healthy samples (GSE142153) were obtained from the Gene Expression Omnibus database, and the lipid metabolism-related genes were obtained from the Molecular Signatures Database. Differentially expressed genes (DEGs) between DN and healthy samples were analyzed. The weighted gene co-expression network analysis (WGCNA) was performed to examine the relationship between genes and clinical traits to identify the key module genes associated with DN. Next, the Venn Diagram R package was used to identify the lipid metabolism-related genes associated with DN and their protein-protein interaction (PPI) network was constructed. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The hub genes were identified using machine-learning algorithms. The Gene Set Enrichment Analysis (GSEA) was used to analyze the functions of the hub genes. The present study also investigated the immune infiltration discrepancies between DN and healthy samples, and assessed the correlation between the immune cells and hub genes. Finally, the expression levels of key genes were verified by reverse transcription-quantitative (RT-q)PCR. The present study determined 1,445 DEGs in DN samples. In addition, 694 DN-related genes in MEyellow and MEturquoise modules were identified by WGCNA. Next, the Venn Diagram R package was used to identify 17 lipid metabolism-related genes and to construct a PPI network. GO analysis revealed that these 17 genes were markedly associated with 'phospholipid biosynthetic process' and 'cholesterol biosynthetic process', while the KEGG analysis showed that they were enriched in 'glycerophospholipid metabolism' and 'fatty acid degradation'. In addition, SAMD8 and CYP51A1 were identified through the intersections of two machine-learning algorithms. The results of GSEA revealed that the 'mitochondrial matrix' and 'GTPase activity' were the markedly enriched GO terms in both SAMD8 and CYP51A1. Their KEGG pathways were mainly concentrated in the 'pathways of neurodegeneration-multiple diseases'. Immune infiltration analysis showed that nine types of immune cells had different expression levels in DN (diseased) and healthy samples. Notably, SAMD8 and CYP51A1 were both markedly associated with activated B cells and effector memory CD8 T cells. Finally, RT-qPCR confirmed the high expression of SAMD8 and CYP51A1 in DN. In conclusion, lipid metabolism-related genes SAMD8 and CYP51A1 may play key roles in DN. The present study provides fundamental information on lipid metabolism that may aid the diagnosis and treatment of DN.

Multiomic meta-analysis suggests a correlation between steroid hormone-related genes and litter size in goats

Litter size is a key indicator of production performance in livestock. However, its genetic basis in goats remains poorly understood. In this work, a genome-wide selection sweep analysis (GWSA) on 100 published goat genomes with different litter rates was performed for the first time to identify candidate genes related to kidding rate. This analysis was combined with the public RNA-sequencing data of ovary tissues (follicular phase) from high- and low-yielding goats. A total of 2278 genes were identified by GWSA. Most of these genes were enriched in signaling pathways related to ovarian follicle development and hormone secretion. Moreover, 208 differentially expressed genes between groups were obtained from the ovaries of goats with different litter sizes. These genes were substantially enriched in the cholesterol and steroid synthesis signaling pathways. Meanwhile, the weighted gene co-expression network was used to perform modular analysis of differentially expressed genes. The results showed that seven modules were reconstructed, of which one module showed a very strong correlation with litter size (r = -0.51 and p-value <0.001). There were 51 genes in this module, and 39 hub genes were screened by Pearson's correlation coefficient between core genes > 0.4, correlation coefficient between module members > 0.80 and intra-module connectivity ≥5. Finally, based on the results of GWSA and hub gene Venn analysis, seven key genes (ACSS2, HECW2, KDR, LHCGR, NAMPT, PTGFR and TFPI) were found to be associated with steroid synthesis and follicle growth development. This work contributes to understanding of the genetic basis of goat litter size and provides theoretical support for goat molecular breeding.

Unraveling the causal links and novel molecular classification of Crohn's disease in breast Cancer: a two-sample mendelian randomization and transcriptome analysis with prognostic modeling

BACKGROUND

Crohn's disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversial.

METHODS

We used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes.

RESULTS

MR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. Using these genes, a new molecular classification of BC patients was proposed.

CONCLUSIONS

CD is a risk factor for ER + BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies.

Identification of autophagy-related signatures in nonalcoholic fatty liver disease and correlation with non-parenchymal cells of the liver

Non-alcoholic fatty liver disease (NAFLD) is a chronic hepatic disease. The incidence and prevalence of NAFLD have increased greatly in recent years, and there is still a lack of effective drugs. Autophagy plays an important role in promoting liver metabolism and maintaining liver homeostasis, and defects in autophagy levels are considered to be related to the development of NAFLD. However, the molecular mechanisms of autophagy in NAFLD still remain unknown. In this study, we identified 6 autophagy-associated hub genes using gene expression profiles obtained from the GSE48452 and GSE89632 datasets. Biomarkers were screened according to gene significance (GS) and module membership (MM) using weighted gene co-expression network analysis (WGCNA), and the immune infiltration landscape of the liver in NAFLD patients was explored using the CIBERSORT algorithm. Subsequently, we analyzed the relationship between liver non-parenchymal cells and autophagy-related hub genes using scRNA-seq data (GSE129516). Finally, we separated the NAFLD patients into two groups based on 6 hub genes by consensus clustering and screened 10 potential autophagy-related small molecules based on the cMAP database.

Incorporating prior information in gene expression network-based cancer heterogeneity analysis

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.

METTL16 regulates the mRNA stability of FBXO5 via m6A modification to facilitate the malignant behavior of breast cancer

BACKGROUND

N6-methyladenosine (m6A) regulates the progression of breast cancer (BC). We aimed to investigate the action and mechanism involved of methyltransferase-like protein 16 (METTL16) in BC growth and metastasis.

METHODS

RT-qPCR, immunoblotting, and IHC were performed to test the levels of gene expression. CCK-8, clone formation, wound healing, and transwell assays were applied to measure the cell proliferation, migration, and invasion. m6A RNA methylation and MeRIP assay were utilized to confirm the m6A level of total RNA and FBXO5 mRNA. RIP was utilized to ascertain the interaction between METTL16 and FBXO5 mRNA. The in vivo murine subcutaneous tumor and metastasis model were constructed to further confirm the action of METTL16.

RESULTS

METTL16 was overexpression in BC cells and tissues. Inhibition of METTL16 restrained the growth and metastasis of BC. Furthermore, the METTL16 level and FBXO5 level was positively correlated in BC tissues, and METTL16 aggrandized the stability of FBXO5 mRNA depending on the m6A modification. Overexpression of FBXO5 antagonized the restrained function of METTL16 knockdown on BC cells' proliferation, migration, invasion, and EMT.

CONCLUSION

METTL16 boosts the mRNA stability of FBXO5 via m6A modification to facilitate the malignant action of BC in vitro and in vivo, offering new latent targets for cure of BC.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Comprehensive Analysis of Sphingolipid Metabolism-Related Genes in Osteoarthritic Diagnosis and Synovial Immune Dysregulation

BACKGROUND Osteoarthritis (OA) is a chronic degenerative disease characterized by synovitis and has been implicated in sphingolipid metabolism disorder. However, the role of sphingolipid metabolism pathway (SMP)-related genes in the occurrence of OA and synovial immune dysregulation remains unclear. MATERIAL AND METHODS In this study, we obtained synovium-related databases from GEO (n=40 for both healthy controls and OA) and analyzed the expression levels of SMP-related genes. Using 2 algorithms, we identified hub genes and developed a diagnostic model incorporating these hub genes to predict the occurrence of OA. Subsequently, the hub genes were further validated in peripheral blood samples from OA patients. Additionally, CIBERSORT and MCP-counter analyses were employed to explore the correlation between hub genes and immune dysregulation in OA synovium. WGCNA was used to determine enriched modules in different clusters. RESULTS Overall, the expression levels of SMP genes were upregulated in OA synovium. We identified 6 hub genes of SMP and constructed an excellent diagnostic model (AUC=0.976). The expression of re-confirmed hub genes showed associations with immune-related cell infiltration and levels of inflammatory cytokines. Furthermore, we observed heterogeneity in the expression patterns of hub genes across different clusters of OA. Notably, older patients displayed increased susceptibility to elevated levels of pain-related inflammatory cytokines and infiltration of immune cells. CONCLUSIONS The SMP-related hub genes have the potential to serve as diagnostic markers for OA patients. Moreover, the 4 hub genes of SMP demonstrate wide participation in immune dysregulation in OA synovium. The activation of different pathways is observed among different populations of patients with OA.

Integrated analysis of m6A regulator-mediated RNA methylation modification patterns and immune characteristics in Sjögren's syndrome

The epigenetic modifier N6-methyladenosine (m6A), recognized as the most prevalent internal modification in messenger RNA (mRNA), has recently emerged as a pivotal player in immune regulation. Its dysregulation has been implicated in the pathogenesis of various autoimmune conditions. However, the implications of m6A modification within the immune microenvironment of Sjögren's syndrome (SS), a chronic autoimmune disorder characterized by exocrine gland dysfunction, remain unexplored. Herein, we leverage an integrative analysis combining public database resources and novel sequencing data to investigate the expression profiles of m6A regulatory genes in SS. Our cohort comprised 220 patients diagnosed with SS and 62 healthy individuals, enabling a comprehensive evaluation of peripheral blood at the transcriptomic level. We report a significant association between SS and altered expression of key m6A regulators, with these changes closely tied to the activation of CD4+ T cells. Employing a random forest (RF) algorithm, we identified crucial genes contributing to the disease phenotype, which facilitated the development of a robust diagnostic model via multivariate logistic regression analysis. Further, unsupervised clustering revealed two distinct m6A modification patterns, which were significantly associated with variations in immunocyte infiltration, immune response activity, and biological function enrichment in SS. Subsequently, we proceeded with a screening process aimed at identifying genes that were differentially expressed (DEGs) between the two groups distinguished by m6A modification. Leveraging these DEGs, we employed weight gene co-expression network analysis (WGCNA) to uncover sets of genes that exhibited strong co-variance and hub genes that were closely linked to m6A modification. Through rigorous analysis, we identified three critical m6A regulators - METTL3, ALKBH5, and YTHDF1 - alongside two m6A-related hub genes, COMMD8 and SRP9. These elements collectively underscore a complex but discernible pattern of m6A modification that appears to be integrally linked with SS's pathogenesis. Our findings not only illuminate the significant correlation between m6A modification and the immune microenvironment in SS but also lay the groundwork for a deeper understanding of m6A regulatory mechanisms. More importantly, the identification of these key regulators and hub genes opens new avenues for the diagnosis and treatment of SS, presenting potential targets for therapeutic intervention.

Exploring the molecular mechanisms network of breast cancer by multi-omics analysis

BACKGROUND

Breast cancer (BC), the most prevalent malignancy in women globally, still lacks comprehensive research on its molecular targets and necessitates further investigation into the underlying molecular mechanisms driving its initiation and progression.

METHODS

The GSE20685 Series Matrix File downloaded from the Gene Expression Omnibus database was divided into a high-risk group (n = 49) and a low-risk group (n = 278) to construct the co-expression network.

RESULTS

Four hub genes were identified based on the Weighted Gene Co-expression Network Analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed. Hub gene immune infiltration was investigated using the Tumor Immune Estimation Resource database, and CD4+ T cell expression levels were substantially correlated with hub gene expression. Based on the CancerRxGene database (Genomics of Drug Sensitivity in Cancer database), it was found that the hub genes were highly sensitive to common chemotherapy drugs such as AKT inhibitor VIII and Erlotinib. The expression of Secreted Frizzled-Related Protein 1, melanoma-inhibiting activity (MIA), and Keratin 14 was related to tumor mutation burden, and the expression of MIA also affected the microsatellite instability of the tumor. This study employs multi-omics analysis to investigate the molecular network associated with the prognosis of BC, highlighting its intricate connection with the immune microenvironment.

CONCLUSION

These findings pinpoint four crucial genes in BC progression, offering targets for further research and therapy. Their connections to immune infiltration and chemotherapy sensitivity underscore complex interactions in the tumor microenvironment.

A prognostic model based on tumor microenvironment and immune cell in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the second leading cause of cancer-related death. Immunotherapy is one of the new options for cancer treatment. This study aimed to develop an immune-related signature associated with CRC.

METHODS

We performed differential analysis to screen out the differentially expressed genes (DEGs) of The Cancer Genome Atlas-Colorectal Cancer (TCGA-CRC) datasets. Weighted gene co-expression network analysis (WGCNA) was performed to obtain the key module genes associated with differential immune cells. The candidate genes were obtained through overlapping key DEGs and key module genes. The univariate and multivariate Cox regression analyses were adopted to build a CRC prognostic signature. We further conducted immune feature estimation and chemotherapy analysis between two risk subgroups. Finally, we verified the expression of immune-related prognostic genes at the transcriptional level.

RESULTS

A total of 61 candidate genes were obtained by overlapping key DEGs and key module genes associated with differential immune cells. Then, an immune-related prognostic signature was built based on the three prognostic genes (HAMP, ADAM8, and CD1B). The independent prognostic analysis suggested that age, stage, and RiskScore could be used as independent prognostic factors. Further, we found significantly higher expression of three prognostic genes in the CRC group compared with the normal group. Finally, real-time polymerase chain reaction verified the expression of three genes in patients with CRC.

CONCLUSION

The prognostic signature comprising HAMP, ADAM8, and CD1B based on immune cells was established, providing a theoretical basis and reference value for the research of CRC.

SSR1 and CKAP4 as potential biomarkers for intervertebral disc degeneration based on integrated bioinformatics analysis

Background

Intervertebral disc degeneration (IDD) is a significant cause of low back pain and poses a significant public health concern. Genetic factors play a crucial role in IDD, highlighting the need for a better understanding of the underlying mechanisms.

Aim

The aim of this study was to identify potential IDD-related biomarkers using a comprehensive bioinformatics approach and validate them in vitro.

Materials and Methods

In this study, we employed several analytical approaches to identify the key genes involved in IDD. We utilized weighted gene coexpression network analysis (WGCNA), MCODE, LASSO algorithms, and ROC curves to identify the key genes. Additionally, immune infiltrating analysis and a single-cell sequencing dataset were utilized to further explore the characteristics of the key genes. Finally, we conducted in vitro experiments on human disc tissues to validate the significance of these key genes in IDD.

Results

we obtained gene expression profiles from the GEO database (GSE23130 and GSE15227) and identified 1015 DEGs associated with IDD. Using WGCNA, we identified the blue module as significantly related to IDD. Among the DEGs, we identified 47 hub genes that overlapped with the genes in the blue module, based on criteria of |logFC| ≥ 2.0 and p.adj <0.05. Further analysis using both MCODE and LASSO algorithms enabled us to identify five key genes, of which CKAP4 and SSR1 were validated by GSE70362, demonstrating significant diagnostic value for IDD. Additionally, immune infiltrating analysis revealed that monocytes were significantly correlated with the two key genes. We also analyzed a single-cell sequencing dataset, GSE199866, which showed that both CKAP4 and SSR1 were highly expressed in fibrocartilage chondrocytes. Finally, we validated our findings in vitro by performing real time polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) on 30 human disc samples. Our results showed that CKAP4 and SSR1 were upregulated in degenerated disc samples. Taken together, our findings suggest that CKAP4 and SSR1 have the potential to serve as disease biomarkers for IDD.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

KIF26B and CREB3L1 Derived from Immunoscore Could Inhibit the Progression of Ovarian Cancer

Background

Ovarian cancer (OV) is characteristic of high incidence rate and fatality rate in the malignant tumors of female reproductive system. Researches on pathogenesis and therapeutic targets for OV need to be continued. This study mainly analyzed the immune-related pathogenesis and discovered the key immunotherapy targets for OV.

Methods

WGCNA was used for excavating hub gene modules and hub genes related to the immunity of OV. Enrichment analysis was aimed to analyze the related pathways of hub gene modules. Biological experiments were used for exploring the effect of hub genes on SKOV3 cells.

Results

We identified two hub gene modules related to the immunoscore of OV and found that these genes in the modules were related to the extracellular matrix and viral infections. At the same time, we also discovered six hub genes related to the immunity of OV. Among them, KIF26B and CREB3L1 can affect the proliferation, migration, and invasion of SKOV3 cells by the Wnt/β-catenin pathway.

Conclusions

The local infection or inflammation of ovarian may affect the immunity of OV. KIF26B and CREB3L1 are expected to be potential targets for the immunotherapy of OV.

Identification of formononetin as the active compound of CR-SR in hepatocellular carcinoma treatment: An integrated approach combining network pharmacology and weighted gene co-expression networks

Hepatocellular carcinoma (HCC) is a life-threatening disease for which there is no cure. Traditional Chinese medicine is a treasure trove of Medicinals that has been used for thousands of years. In China, the traditional herb pair, Curcumae Rhizoma and Sparganii Rhizoma (CR-SR) represent a classic herbal combination used for the treatment of HCC. However, the drug targets and pharmacological mechanism of action of CR-SR in the treatment of HCC are unclear. To address this, we screened the active components and drug targets of CR-SR from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and a high-throughput experiment- and reference-guided database of traditional Chinese medicines (HERB database). Combined with the weighted co-expression network analysis of dataset GSE76427, we constructed an active component-target-disease regulatory network. It was found that CR-SR's active components for HCC treatment included trans-gondoic acid, beta-sitosterol, stigmasterol, hederagenin, and formononetin. These compounds specifically targeted the genes Estrogen Receptor 1 (ESR1), Cyclin A2 (CCNA2), Checkpoint Kinase 1 (CHEK1), and Nuclear Receptor Coactivator 2 (NCOA2). ESR1, CCNA2, and CHEK1 genes showed significant differences in survival prognosis, expression levels, and statistical significance during the pathological stage. Moreover, their high affinity for formononetin was determined through molecular docking analysis. Cell assays and high-throughput sequencing were performed to reveal that the inhibitory effect of formononetin on HepG2 cell proliferation was related to hepatocyte metabolism and cell cycle regulation-related pathways. This study provides insights into potential HCC treatments.

Time-Course Transcriptome Analysis Reveals Distinct Phases and Identifies Two Key Genes during Severe Fever with Thrombocytopenia Syndrome Virus Infection in PMA-Induced THP-1 Cells

In recent years, there have been significant advancements in the research of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV). However, several limitations and challenges still exist. For instance, researchers face constraints regarding experimental conditions and the feasibility of sample acquisition for studying SFTSV. To enhance the quality and comprehensiveness of SFTSV research, we opted to employ PMA-induced THP-1 cells as a model for SFTSV infection. Multiple time points of SFTSV infection were designed to capture the dynamic nature of the virus-host interaction. Through a comprehensive analysis utilizing various bioinformatics approaches, including diverse clustering methods, MUfzz analysis, and LASSO/Cox machine learning, we performed dynamic analysis and identified key genes associated with SFTSV infection at the host cell transcriptomic level. Notably, successful clustering was achieved for samples infected at different time points, leading to the identification of two important genes, PHGDH and NLRP12. And these findings may provide valuable insights into the pathogenesis of SFTSV and contribute to our understanding of host-virus interactions.

Copper metabolism-related risk score identifies hepatocellular carcinoma subtypes and SLC27A5 as a potential regulator of cuproptosis

AIMS

Dysregulated copper metabolism has been noticed in many types of cancer including hepatocellular carcinoma (HCC); however, a comprehensive understanding about this dysregulation still remains unclear in HCC.

METHODS

A set of bioinformatic tools was integrated to analyze the expression and prognostic significance of copper metabolism-related genes. A related risk score, termed as CMscore, was developed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression. Pathway enrichment analyses and tumor immune cell infiltration were further investigated in CMscore stratified HCC patients. Weighted correlation network analysis (WGCNA) was used to identify potential regulator of cuproptosis.

RESULTS

Copper metabolism was dysregulated in HCC. HCC patients in the high-CMscore group showed a significantly lower overall survival (OS) and enriched in most cancer-related pathways. Besides, HCC patients with high CMscore had higher expression of pro-tumor immune infiltrates and immune checkpoints. Moreover, cancer patients with high CMscore from two large cohorts exhibited significantly prolonged survival time after immunotherapy. WGCNA and subsequently correlation analysis revealed that SLC27A5 might be a potential regulator of cuproptosis in HCC. In vitro experiments revealed that SLC27A5 inhibited cell proliferation and migration of HCC cells and could upregulate FDX1, the key regulator of cuproptosis.

SIGNIFICANCE

The CMscore is helpful in clustering HCC patients with distinct prognosis, gene mutation signatures, and sensitivity to immunotherapy. SLC27A5 might serve as a potential target in the induction of cuproptosis in HCC.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

Association of Gene Polymorphisms with Breast Cancer Risk in the Kazakh Population

OBJECTIVE

The research aim is analyzing and identify reliable genetic markers of breast cancer risk in the Kazakh population.

METHODS

The databases were analyzed with the selection of polymorphisms associated with the development of breast cancer and further genotypic study of a group of women with a confirmed diagnosis of breast adenocarcinoma (group No. 1) and a group of relatively healthy women (group No. 2).

RESULT

The research presents the results of a study on the frequency of certain single-nucleotide polymorphisms in patients with breast cancer in the Republic of Kazakhstan. The frequency of single-nucleotide polymorphisms rs4646, rs1065852, rs4244285, rs67376798, rs6504950, rs2229774, rs1800056, rs16942, rs4987047 is statistically significant compared to the control group of patients. These polymorphisms in the Kazakh population have a direct association with an increased risk of breast cancer in women and may be used as cancer indicators during the genetic screening of patients with a complicated family history. Single-nucleotide polymorphisms such as rs55886062, rs3918290, rs12721655, rs4987117, rs2229774, rs11203289, rs137852576, rs11571833, rs80359062 and rs11571746 were found in more than 40. Zero percent of patients with breast cancer may be used as markers for detecting patients at increased risk of breast malignancy in the Kazakh population without a history of poor family history.

CONCLUSION

The usage of the data obtained in a set of state programs for early screening of patients will improve the rates of early breast tumor detection, form groups of patients with a high risk of disease development and improve the quality and expectancy of life.

MCM10, a potential diagnostic, immunological, and prognostic biomarker in pan-cancer

Microchromosome maintenance (MCM) proteins are a number of nuclear proteins with significant roles in the development of cancer by influencing the process of cellular DNA replication. Of the MCM protein family, MCM10 is a crucial member that maintains the stability and extension of DNA replication forks during DNA replication and is significantly overexpressed in a variety of cancer tissues, regulating the biological behaviour of cancer cells. But little is understood about MCM10's functional role and regulatory mechanisms in a range of malignancies. We investigate the impact of MCM10 in human cancers by analyzing data from databases like the Gene Expression Profiling Interaction Analysis (GEPIA2), Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), among others. Possible relationships between MCM10 and clinical staging, diagnosis, prognosis, Mutation burden (TMB), microsatellite instability (MSI), immunological checkpoints, DNA methylation, and tumor stemness were identified. The findings demonstrated that MCM10 expression was elevated in the majority of cancer types and was connected to tumor dryness, immunocytic infiltration, immunological checkpoints, TMB and MSI. Functional enrichment analysis in multiple tumors also identified possible pathways of MCM10 involvement in tumorigenesis. We also discovered promising MCM10-targeting chemotherapeutic drugs. In conclusion, MCM10 may be a desirable pan-cancer biomarker and offer fresh perspectives on cancer therapy.

ESOMIR: a curated database of biomarker genes and miRNAs associated with esophageal cancer

'Esophageal cancer' (EC) is a highly aggressive and deadly complex disease. It comprises two types, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), with Barrett's esophagus (BE) being the only known precursor. Recent research has revealed that microRNAs (miRNAs) play a crucial role in the development, prognosis and treatment of EC and are involved in various human diseases. Biological databases have become essential for cancer research as they provide information on genes, proteins, pathways and their interactions. These databases collect, store and manage large amounts of molecular data, which can be used to identify patterns, predict outcomes and generate hypotheses. However, no comprehensive database exists for EC and miRNA relationships. To address this gap, we developed a dynamic database named 'ESOMIR (miRNA in esophageal cancer) (https://esomir.dqweilab-sjtu.com)', which includes information about targeted genes and miRNAs associated with EC. The database uses analysis and prediction methods, including experimentally endorsed miRNA(s) information. ESOMIR is a user-friendly interface that allows easy access to EC-associated data by searching for miRNAs, target genes, sequences, chromosomal positions and associated signaling pathways. The search modules are designed to provide specific data access to users based on their requirements. Additionally, the database provides information about network interactions, signaling pathways and region information of chromosomes associated with the 3'untranslated region (3'UTR) or 5'UTR and exon sites. Users can also access energy levels of specific miRNAs with targeted genes. A fuzzy term search is included in each module to enhance the ease of use for researchers. ESOMIR can be a valuable tool for researchers and clinicians to gain insight into EC, including identifying biomarkers and treatments for this aggressive tumor. Database URL https://esomir.dqweilab-sjtu.com.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

Molecular characteristics of gastric cancer with ERBB2 amplification

Gastric cancer is a prevalent malignancy with a high degree of heterogeneity, which has led to a poor therapeutic response. Though there are numerous HER2-targeted medicines for HER2+ gastric cancer, many trials have not indicated an improvement in overall survival. Here 29 ERBB2 amplification (ERBB2-Amp) type gastric cancer samples with WES and RNA-seq data were selected for investigation, which copy-number aberration (CNA) was +2. Initially, the somatic mutation and copy number variant (CNV) of them, which might cause resistance to HER2-targeted therapies, were systematically investigated evaluated, as well as their mutation signatures. Moreover, 37 modules were identified using weighted gene co-expression network analysis (WGCNA), including the blue module related to DFS status and lightcyan module correlated with ARHGAP26_ARHGAP6_CLDN18 rearrangement. In addition, focal adhesion and ECM-receptor interaction pathways were considerably enriched in the turquoise module with ERBB2 gene. ExportNetworkToCytoscape determined that MIEN1 and GRB7 are tightly connected to ERBB2., Finally, 14 single-cell intestinal gastric cancer samples were investigated, and it was shown that the TFAP2A transcription factor regulon was highly expressed in ERBB2high group, as was the EMT score. Overall, our data provide comprehensive molecular characteristics of ERBB2-Amp type gastric cancer, which offers additional information to improve HER2-targeted gastric cancer treatment.

Identification of Novel Prognostic Biomarkers That are Associated with Immune Microenvironment Based on GABA-Related Molecular Subtypes in Gastric Cancer

Background

Gamma-aminobutyric acid (GABA) plays an important role in tumorigenesis and progression. Despite this, the role of Reactome GABA receptor activation (RGRA) on gastric cancer (GC) remains unclear. This study was intended to screen RGRA-related genes in GC and investigate their prognostic value.

Methods

GSVA algorithm was used to assess the score of RGRA. GC patients were divided into two subtypes based on the median score of RGRA. GSEA, functional enrichment analysis, and immune infiltration analysis were performed between the two subgroups. Then, differentially expressed analysis, and weighted gene co-expression network analysis (WGCNA) were used to identify RGRA-related genes. The prognosis and expression of core genes were analyzed and validated in the TCGA database, GEO database, and clinical samples. ssGSEA and ESTIMATE algorithms were used to assess the immune cell infiltration in the low- and high-core genes subgroups.

Results

High-RGRA subtype had a poor prognosis and activated immune-related pathways, as well as an activated immune microenvironment. ATP1A2 was identified to be the core gene. The expression of ATP1A2 was associated with the overall survival rate and tumor stage, and its expression was down-regulated in GC patients. Furthermore, ATP1A2 expression was positively correlated with the level of immune cells, including B cells, CD8 T cells, cytotoxic cells, DC, eosinophils, macrophages, mast cells, NK cells, and T cells.

Conclusion

Two RGRA-related molecular subtypes were identified that could predict the outcome in GC patients. ATP1A2 was a core immunoregulatory gene and was associated with prognosis and immune cell infiltration in GC.

Construction of the novel immune risk scoring system related to CD8+ T cells in uterine corpus endometrial carcinoma

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy.

METHODS

CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8⁺ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan-Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels.

RESULTS

Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8⁺ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8⁺ T cells.

CONCLUSIONS

In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.

Drug repurposing for Basal breast cancer subpopulations using modular network signatures

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

Identification of co-regulated genes associated with doxorubicin resistance in the MCF-7/ADR cancer cell line

Introduction

The molecular mechanism of chemotherapy resistance in breast cancer is not well understood. The identification of genes associated with chemoresistance is critical for a better understanding of the molecular processes driving resistance.

Methods

This study used a co-expression network analysis of Adriamycin (or doxorubicin)-resistant MCF-7 (MCF-7/ADR) and its parent MCF-7 cell lines to explore the mechanisms of drug resistance in breast cancer. Genes associated with doxorubicin resistance were extracted from two microarray datasets (GSE24460 and GSE76540) obtained from the Gene Expression Omnibus (GEO) database using the GEO2R web tool. The candidate differentially expressed genes (DEGs) with the highest degree and/or betweenness in the co-expression network were selected for further analysis. The expression of major DEGs was validated experimentally using qRT-PCR.

Results

We identified twelve DEGs in MCF-7/ADR compared with its parent MCF-7 cell line, including 10 upregulated and 2 downregulated DEGs. Functional enrichment suggests a key role for RNA binding by IGF2BPs and epithelial-to-mesenchymal transition pathways in drug resistance in breast cancer.

Discussion

Our findings suggested that MMP1, VIM, CNN3, LDHB, NEFH, PLS3, AKAP12, TCEAL2, and ABCB1 genes play an important role in doxorubicin resistance and could be targeted for developing novel therapies by chemical synthesis approaches.

Subcellular spatially resolved gene neighborhood networks in single cells

Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.

Identifying Hub Genes and miRNA-mRNA Regulatory Networks in Mice Infected with H1N1 Influenza Virus

H1N1 influenza virus is a major factor in seasonal influenza outbreaks. After the body is infected with the influenza virus, the expression of certain mRNAs, including miRNAs, could be affected. However, the association between these mRNAs and miRNAs remains unclear. This study is aimed at identifying differentially expressed genes (DEGs) and miRNAs (DEmiRs) caused by H1N1 influenza virus infection and constructing a miRNA-mRNA regulatory network. Nine GSE datasets were downloaded from the Gene Expression Omnibus database, of which seven were mRNA data and two were miRNA data. The limma package in R language package was used to analyze array data, and edgeR package was used to analyze high-throughput sequencing data. At the same time, the genes related to H1N1 infection were further screened by WGCNA analysis. DEGs were subjected to Gene Ontology and KEGG pathway enrichment analyses by DAVID database, while the STRING database predicted the protein-protein interaction (PPI) network. The correspondence between miRNA and target mRNA was analyzed by the miRWalk database. Cytoscape software was used to output PPI results, identify hub genes, and construct a miRNA-mRNA regulatory network. 114 DEGs and 37 candidate DEmiRs were identified for subsequent analysis. These DEGs were significantly enriched in response to the virus, cytokine activity, and symbiont-containing vacuole membrane. According to KEGG analysis, DEGs were enriched in PD-L1 expression and PD-1 checkpoint pathway. The key point Cd274 (PD-L1) was highly expressed in the H1N1-infected group. Finally, a potential miRNA-mRNA regulatory network (containing 8 candidate DEmiRs and 69 candidate DEGs) and a PPI network were constructed. After that, three hub genes were identified: Ifit3, Stat2, and Irf7. These hub genes and Cd274 were validated by another independent high-throughput dataset and were highly expressed pattern. This study will help researchers gain insights into the intrinsic effects of H1N1 influenza virus infection on the host and suggest a novel association of H1N1 virus with the host immune system.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

Weighted gene co-expression network analysis identifies the prognosis-related models of left- and right-sided colon cancer

Left-sided colon cancer (LC) and right-sided colon cancer (RC) are 2 essentially different diseases, and the potential mechanisms regulating them remain unidentified. In this study, we applied weighted gene co-expression network analysis (WGCNA) to confirm a yellow module, mainly enriched in metabolism-related signaling pathways related to LC and RC. Based on the RNA-seq data of colon cancer in The Cancer Genome Atlas (TCGA) and GSE41258 dataset with their corresponding clinical information, a training set (TCGA: LC: n = 171; RC: n = 260) and a validation set (GSE41258: LC: n = 94; RC: n = 77) were divided. Least absolute shrinkage and selection operator (LASSO) penalized COX regression analysis identified 20 prognosis-related genes (PRGs) and helped constructed 2 risk (LC-R and RC-R) models in LC and RC, respectively. The model-based risk scores accurately performed in risk stratification for colon cancer patients. The high-risk group of the LC-R model showed associations with ECM-receptor interaction, focal adhesion, and PI3K-AKT signaling pathway. Interestingly, the low-risk group of the LC-R model showed associations with immune-related signaling pathways like antigen processing and presentation. On the other hand, the high-risk group of the RC-R model showed enrichment for cell adhesion molecules and axon guidance signaling pathways. Furthermore, we identified 20 differentially expressed PRGs between LC and RC. Our findings provide new insights into the difference between LC and RC, and uncover the potential biomarkers for the treatment of LC and RC.

TIGD1 Function as a Potential Cuproptosis Regulator Following a Novel Cuproptosis-Related Gene Risk Signature in Colorectal Cancer

Cuproptosis is a new form of copper-dependent programmed cell death commonly occurring within the body. There is emerging evidence indicating that cuproptosis has a significant regulatory function in the onset and progression of cancer. However, it is still unclear how cuproptosis regulates cancer and whether other genes are involved in the regulation. Using the TCGA-COAD dataset of 512 samples, we found that seven of ten cuproptosis markers showed prognostic value in colorectal cancer (CRC) using Kaplan-Meier survival analysis. Furthermore, 31 prognostic cuproptosis-related genes were identified using weighted gene co-expression network analysis and univariate Cox analysis. Subsequently, we constructed a 7-PCRG signature using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. The risk score predicting survival in patients with CRC was evaluated. Two risk groups were classified based on their risk scores. The two groups revealed a significant difference in immune cells, such as B and T cells. Furthermore, we identified differences in many immune functions and checkpoints, including CD276 and CD28. In vitro experiments showed that a hub cuproptosis-related gene, TIGD1, could significantly regulate cuproptosis in CRC after exposure to elesclomol. This study validated that cuproptosis was closely related to the progression of CRC. Seven new cuproptosis-related genes were identified, and the function of TIGD1 in cuproptosis was preliminarily understood. Since a certain concentration of copper in CRC cells is important, cuproptosis may provide a new target for cancer therapy. This study may provide novel insights into the treatment of CRC.

Up‐regulation of PRKDC was associated with poor renal dysfunction after renal transplantation: A multi‐centre analysis

Renal transplantation is the only efficacious treatment for end-stage kidney disease. However, some people have developed renal insufficiency after transplantation, the mechanisms of which have not been well clarified. Previous studies have focused on patient factors, while the effect of gene expression in the donor kidney on post-transplant renal function has been less studied. Donor kidney clinical data and mRNA expression status were extracted from the GEO database (GSE147451). Weight gene co-expression network analysis (WGCNA) and differential gene enrichment analysis were performed. For external validation, we collected data from 122 patients who accepted renal transplantation at several hospitals and measured the level of target genes by qPCR. This study included 192 patients from the GEO data set, and 13 co-expressed genes were confirmed by WGCNA and differential gene enrichment analysis. Then, the PPI network contained 17 edges as well as 12 nodes, and four central genes (PRKDC, RFC5, RFC3 and RBM14) were identified. We found by collecting data from 122 patients who underwent renal transplantation in several hospitals and by multivariate logistic regression that acute graft-versus-host disease postoperative infection, PRKDC [Hazard Ratio (HR) = 4.44; 95% CI = [1.60, 13.68]; p = 0.006] mRNA level correlated with the renal function after transplantation. The prediction model constructed had good predictive accuracy (C-index = 0.886). Elevated levels of donor kidney PRKDC are associated with renal dysfunction after transplantation. The prediction model of renal function status for post-transplant recipients based on PRKDC has good predictive accuracy and clinical application.

Comprehensive analysis of the expression and prognosis for RAI2: A promising biomarker in breast cancer

Introduction

Retinoic acid-induced 2 (RAI2) was initially related to cell differentiation and induced by retinoic acid. RAI2 has been identified as an emerging tumor suppressor in breast cancer and colorectal cancer.

Methods

In this study, we performed systematic analyses of RAI2 in breast cancer. Meta-analysis and Kaplan-Meier survival curves were applied to identify the survival prediction potential of RAI2. Moreover, the association between RAI2 expression and the abundance of six tumor-infiltrating immune cells was investigated by TIMER, including B cells, CD8+ T cells, CD4+ T cells, B cells, dendritic cells, neutrophils, and macrophages. The expression profiles of high and low RAI2 mRNA levels in GSE7390 were compared to identify differentially expressed genes (DEGs) and the biological function of these DEGs was analyzed by R software, which was further proved in GSE7390.

Results

Our results showed that the normal tissues had more RAI2 expression than breast cancer tissues. Patients with high RAI2 expression were related to a favorable prognosis and more immune infiltrates. A total of 209 DEGs and 182 DEGs were identified between the expression profiles of high and low RAI2 mRNA levels in the GSE7390 and GSE21653 databases, respectively. Furthermore, Gene Ontology (GO) enrichment indicated that these DEGs from two datasets were both mainly distributed in “biological processes” (BP), including “organelle fission” and “nuclear division”. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis demonstrated that these DEGs from two datasets were both significantly enriched in the “cell cycle”. Common hub genes between the DEGs in GSE7390 and GSE21653 were negatively associated with RAI2 expression, including CCNA2, MAD2L1, MELK, CDC20, and CCNB2.

Discussions

These results above suggested that RAI2 might play a pivotal role in preventing the initiation and progression of breast cancer. The present study may contribute to understanding the molecular mechanisms of RAI2 and enriching biomarkers to predict patient prognosis in breast cancer.

Screening ANLN and ASPM as bladder urothelial carcinoma-related biomarkers based on weighted gene co-expression network analysis

Introduction: Bladder cancer (BLCA) is one of the most common malignancies in the urinary system with a poor prognosis and high treatment costs. Identifying potential prognostic biomarkers is significant for exploring new therapeutic and predictive targets of BLCA.

Methods: In this study, we screened differentially expressed genes using the GSE37815 dataset. We then performed a weighted gene co‐expression network analysis (WGCNA) to identify the genes correlated with the histologic grade and T stage of BLCA using the GSE32548 dataset. Subsequently, Kaplan Meier survival analysis and Cox regression were used to further identify prognosis‐related hub genes using the datasets GSE13507 and TCGA‐BLCA. Moreover, we detected the expression of the hub genes in 35 paired samples, including BLCA and paracancerous tissue, from the Shantou Central Hospital by qRT‐polymerase chain reaction.

Results: This study showed that Anillin (ANLN) and Abnormal spindle-like microcephaly-associated gene (ASPM) were prognostic biomarkers for BLCA. High expression of ANLN and ASPM was associated with poor overall survival.The qRT‐PCR results revealed that ANLN and ASPM genes were upregulated in BLCA, and there was a correlation between the expression of ANLN and ASPM in cancer tissues and paracancerous tissue. Additionally, the increasing multiples in the ANLN gene was obvious in high-grade BLCA.

Discussion: In summary, this preliminary exploration indicated a correlation between ANLN and ASPM expression. These two genes, serving as the risk factors for BLCA progression, might be promising targets to improve the occurrence and progression of BLCA.

Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors

The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches.

AZU1 (HBP/CAP37) and PRKCG (PKC-gamma) may be candidate genes affecting the severity of acute mountain sickness

BACKGROUND

Acute Mountain Sickness (AMS) is one of the diseases that predispose to sudden ascent to high altitudes above 2500 m. Among the many studies on the occurrence and development of AMS, there are few studies on the severity of AMS. Some unidentified phenotypes or genes that determine the severity of AMS may be vital to elucidating the mechanisms of AMS. This study aims to explore the underlying genes or phenotypes associated with AMS severity and to provide evidence for a better understanding of the mechanisms of AMS.

METHODS

GSE103927 dataset was downloaded from the Gene Expression Omnibus database, and a total of 19 subjects were enrolled in the study. Subjects were divided into a moderate to severe AMS (MS-AMS, 9 subjects) group and a no or mild AMS (NM-AMS, 10 subjects) group based on the Lake Louise score (LLS). Various bioinformatics analyses were used to compare the differences between the two groups. Another dataset, Real-time quantitative PCR (RT-qPCR), and another grouping method were used to validate the analysis results.

RESULT

No statistically significant differences in phenotypic and clinical data existed between the MS-AMS and NM-AMS groups. Eight differential expression genes are associated with LLS, and their biological functions are related regulating of the apoptotic process and programmed cell death. The ROC curves showed that AZU1 and PRKCG had a better predictive performance for MS-AMS. AZU1 and PRKCG were significantly associated with the severity of AMS. The expression of AZU1 and PRKCG were significantly higher in the MS-AMS group compared to the NM-AMS group. The hypoxic environment promotes the expression of AZU1 and PRKCG. The results of these analyses were validated by an alternative grouping method and RT-qPCR results. AZU1 and PRKCG were enriched in the Neutrophil extracellular trap formation pathway, suggesting the importance of this pathway in influencing the severity of AMS.

CONCLUSION

AZU1 and PRKCG may be key genes influencing the severity of acute mountain sickness, and can be used as good diagnostic or predictive indicators of the severity of AMS. Our study provides a new perspective to explore the molecular mechanism of AMS.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Identification of new immune subtypes of renal injury associated with anti-neutrophil cytoplasmic antibody-associated vasculitis based on integrated bioinformatics analysis

Background: Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that may lead to end-stage renal disease. However, few specifific biomarkers are available for AAV-related renal injury. The aim of this study was to identify important biomarkers and explore new immune subtypes of AAV-related renal injury. Methods: In this study, messenger RNA expression profiles for antibody-associated vasculitis and AAV-associated kidney injury were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes to AAV. Key module genes from WGCNA were then intersected with AAV- and nephropathy-related genes from the Genecards database to identify key genes for AAV-associated kidney injury. Subsequently, the expression of key genes was validated in independent datasets and the correlation of genes with clinical traits of kidney injury was verified by the Nephroseq database. Finally, non-negative matrix factorization (NMF) clustering was performed to identify the immune subtypes associated with the key genes. Results: Eight co-key genes (AGTR2, ANPTL2, BDKRB1, CSF2, FGA, IL1RAPL2, PCDH11Y, and PGR) were identifified, and validated the expression levels independent datasets. Receiver operating characteristic curve analysis revealed that these eight genes have major diagnostic value as potential biomarkers of AAV-related renal injury. Through our comprehensive gene enrichment analyses, we found that they are associated with immune-related pathways. NMF clustering of key genes identified two and three immune-related molecular subtypes in the glomerular and tubular data, respectively. A correlation analysis with prognostic data from the Nephroseq database indicated that the expression of co-key genes was positively co-related with the glomerular filtration rate. Discussion: Altogether, we identifified 8 valuable biomarkers that firmly correlate with the diagnosis and prognosis of AAV-related renal injury. These markers may help identify new immune subtypes for AAV-related renal injury.

Network module function enrichment analysis of lung squamous cell carcinoma and lung adenocarcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small cell lung cancer that pose a serious threat to human health. However, both subtypes currently lack effective indicators for early diagnosis.

METHODS

To identify tumor-specific indicators and predict cancer-related signaling pathways, LUSC and LUAD gene weighted co-expression networks were constructed. Combined with clinical data, core genes in LUSC and LUAD modules were then screened using protein-protein interaction networks and their functions and pathways were analyzed. Finally, the effect of core genes on survival of LUSC and LUAD patients was evaluated.

RESULTS

We identified 12 network modules in LUSC and LUAD, respectively. LUSC modules "purple" and "green" and LUAD modules "brown" and "pink" are significantly associated with overall survival and clinical traits of tumor node metastasis, respectively. Eleven genes from LUSC and eight genes from LUAD were identified as candidate core genes, respectively. Survival analysis showed that high expression of SLIT3, ABI3BP, MYOCD, PGM5, TNXB, and DNAH9 are associated with decreased survival in LUSC patients. Furthermore, high expression of BUB1, BUB1B, TTK, and UBE2C are associated with lower patient survival.

CONCLUSIONS

We found biomarker genes and biological pathways for LUSC and LUAD. These network hub genes are associated with clinical characteristics and patient outcomes and they may play important roles in LUSC and LUAD.

Transcriptome analysis of pika heart tissue reveals mechanisms underlying the adaptation of a keystone species on the roof of the world

High-altitude environments impose intense stresses on living organisms and drive striking phenotypic and genetic adaptations, such as hypoxia resistance, cold tolerance, and increases in metabolic capacity and body mass. As one of the most successful and dominant mammals on the Qinghai-Tibetan Plateau (QHTP), the plateau pika (Ochotona curzoniae) has adapted to the extreme environments of the highest altitudes of this region and exhibits tolerance to cold and hypoxia, in contrast to closely related species that inhabit the peripheral alpine bush or forests. To explore the potential genetic mechanisms underlying the adaptation of O. curzoniae to a high-altitude environment, we sequenced the heart tissue transcriptomes of adult plateau pikas (comparing specimens from sites at two different altitudes) and Gansu pikas (O. cansus). Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify differentially expressed genes (DEGs) and their primary functions. Key genes and pathways related to high-altitude adaptation were identified. In addition to the biological processes of signal transduction, energy metabolism and material transport, the identified plateau pika genes were mainly enriched in biological pathways such as the negative regulation of smooth muscle cell proliferation, the apoptosis signalling pathway, the cellular response to DNA damage stimulus, and ossification involved in bone maturation and heart development. Our results showed that the plateau pika has adapted to the extreme environments of the QHTP via protection against cardiomyopathy, tissue structure alterations and improvements in the blood circulation system and energy metabolism. These adaptations shed light on how pikas thrive on the roof of the world.

Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

Identification of key genes associated with cancer stem cell characteristics in Wilms' tumor based on bioinformatics analysis

Background

Nephroblastoma, also known as Wilms' tumor (WT), remains one of the major causes of tumor-related deaths worldwide in children. Cancer stem cells (CSCs) are considered to be the main culprits in cancer resistance and disease recurrence, which are reported in multiple types of tumors. However, the research on CSCs in WT is limited. Therefore, our study aimed to identify the key genes related to CSCs in WT to provide new ideas for treating WT.

Methods

The RNA-seq and clinical data of WT samples were obtained from the University of California Santa Cruz (UCSC) Xena database, which included 120 WT and six para-cancerous tissues. The mRNA stemness index (mRNAsi) based on mRNA expression was calculated to evaluate tumor stem cell characteristics in WT patients. A Kaplan-Meier (KM) analysis was performed to explore the clinical characteristics of the mRNAsi in WT. A weighted gene co-expression network analysis (WGCNA) was used to identify the key modules and genes related to the mRNAsi. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to explore the signaling pathways based on the key genes. The expression levels of the key genes were validated by the Gene Expression Omnibus (GEO) database. Further, the important upstream genes were identified by DisNor and gene co-expression analyses.

Results

The mRNAsi was significantly upregulated in WT (P=7.2e-05) and showed an upward trend in line with the pathological stage. Patients with lower mRNAsi scores had better overall survival (OS) than those with higher mRNAsi scores (P=0.0087). Eleven genes were defined as the key genes associated with the mRNAsi based on our WGCNA analysis [cor.MM (correlation. Module membership) >0.8 and cor.GS (correlation. Gene significance) >0.45] and were closely related to cell proliferation-related signaling pathways (P<0.05). Moreover, using protein interaction analysis, we identified ATM and CDKN1A as the key upstream regulatory genes of the 11 key genes.

Conclusions

Our study showed that the mRNAsi score was a potential prognostic factors in WT and identified the upstream genes ATM and CDKN1A and 11 genes closely related to the mRNAsi, which may provide new insights for CSC-targeted therapy in WT and improve clinical outcomes for WT patients.

Identification and exploration of pharmacological pyroptosis-related biomarkers of ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD). Its etiology is unclear. Much evidence suggests that the death of abnormal intestinal epithelial cells (IECs) leads to intestinal barrier disruption, and the subsequent inflammatory response plays a vital role in UC. Pyroptosis is a form of programmed inflammatory cell death, and the role of pyroptosis in UC etiology remains to be explored. This study identified 10 hub genes in pyroptosis by gene expression profiles obtained from the GSE87466 dataset. Meanwhile, the biomarkers were screened based on gene significance (GS) and module membership (MM) through the Weighted Gene Co-Expression Network Analysis (WGCNA). The following analysis indicated that hub genes were closely associated with the UC progression and therapeutic drug response. The single-cell RNA (scRNA) sequencing data from UC patients within the GSE162335 dataset indicated that macrophages were most related to pyroptosis. Finally, the expression of hub genes and response to the therapeutic drug [5-aminosalicylic acid (5-ASA)] were verified in dextran sulfate sodium (DSS)-induced colitis mice. Our study identified IL1B as the critical pyroptosis-related biomarker in UC. The crosstalk between macrophage pyroptosis and IEC pyroptosis may play an essential role in UC, deserving further exploration.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

Identification of Gene Coexpression Modules and Prognostic Genes Associated with Papillary Thyroid Cancer

Thyroid cancer is a great part of the endocrine tumor with an increasing incidence. Papillary thyroid carcinoma (PTC) is the most common subtype. With the enormous pace taken in the microarray technology, bioinformatics is applied in data mining more frequently. Weighted gene coexpression network analysis (WGCNA) can perform analysis combining clinic information. We performed WGCNA for prognostic genes associated with PTC. From the GEO profile, we got ten modules. We identified a key module that was closest to patients’ survival time. Then, we screened five hub genes (ATRX, BOD1L1, CEP290, DCAF16, and NEK1) from the key module based on the clinical information from TCGA. These five genes not only significantly differ between the normal and tumor groups but have prognostic value. The receiver operating characteristic (ROC) curve indicated that they had the potential to serve as prognostic genes. We performed next-generation sequencing using the PTC tissue to get more convincing evidence. Besides, we established a new signature and verified it through K-M plots and ROC. The signature could be an independent factor for the prognosis of PTC, and we built a nomogram to carry out a quantitative study. In a word, the hub genes we explored in the study deserved more basic and clinical research.

A novel gene signature based on the hub genes of COVID-19 predicts the prognosis of idiopathic pulmonary fibrosis

Background: Increasing evidence has demonstrated that there was a strong correlation between COVID-19 and idiopathic pulmonary fibrosis (IPF). However, the studies are limited, and the real biological mechanisms behind the IPF progression were still uncleared.

Methods: GSE70866 and GSE 157103 datasets were downloaded. The weight gene co-expression network analysis (WGCNA) algorithms were conducted to identify the most correlated gene module with COVID-19. Then the genes were extracted to construct a risk signature in IPF patients by performing Univariate and Lasso Cox Regression analysis. Univariate and Multivariate Cox Regression analyses were used to identify the independent value for predicting the prognosis of IPF patients. What’s more, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were conducted to unveil the potential biological pathways. CIBERSORT algorithms were performed to calculate the correlation between the risk score and immune cells infiltrating levels.

Results: Two hundred thirty three differentially expressed genes were calculated as the hub genes in COVID-19. Fourteen of these genes were identified as the prognostic differentially expressed genes in IPF. Three (MET, UCHL1, and IGF1) of the fourteen genes were chosen to construct the risk signature. The risk signature can greatly predict the prognosis of high-risk and low-risk groups based on the calculated risk score. The functional pathway enrichment analysis and immune infiltrating analysis showed that the risk signature may regulate the immune-related pathways and immune cells.

Conclusion: We identified prognostic differentially expressed hub genes related to COVID-19 in IPF. A risk signature was constructed based on those genes and showed great value for predicting the prognosis in IPF patients. What’s more, three genes in the risk signature may be clinically valuable as potential targets for treating IPF patients and IPF patients with COVID-19.