Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA

Construction of pan-cancer regulatory networks based on causal inference

The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC).

Tankyrase1/2 inhibitor XAV-939 reverts EMT and suggests that PARylation partially regulates aerobic activities in human hepatocytes and HepG2 cells

The maintenance of a highly functional metabolic epithelium in vitro is challenging. Metabolic impairments in primary human hepatocytes (PHHs) over time is primarily due to epithelial-to-mesenchymal transitioning (EMT). The immature hepatoma cell line HepG2 was used as an in vitro model to explore strategies for enhancing the hepatic phenotype. The phenotypic characterization includes measuring the urea cycle, lipid storage, tricarboxylic acid-related metabolites, reactive oxygen species, endoplasmic reticulum calcium efflux, mitochondrial membrane potentials, oxygen consumptions rate, and CYP450 biotransformation capacity. Expression studies were performed with transcriptomics, co-immunoprecipitation and proteomics. CRISPR/Cas9 was also employed to genetically engineer HepG2 cells. After confirming that PHHs develop an EMT phenotype, expression of tankyrase1/2 was found to increase over time. EMT was reverted when blocking tankyrases1/2-dependent poly-ADP-ribosylation (PARylation) activity, by biochemical and genetic perturbation. Wnt/β-catenin inhibitor XAV-939 blocks tankyrase1/2 and treatment elevated several oxygen-consuming reactions (electron-transport chain, OXHPOS, CYP450 mono-oxidase activity, phase I/II xenobiotic biotransformation, and prandial turnover), suggesting that cell metabolism was enhanced. Glutathione-dependent redox homeostasis was also significantly improved in the XAV-939 condition. Oxygen consumption rate and proteomics experiments in tankyrase1/2 double knockout HepG2 cells then uncovered PARylation as master regulator of aerobic-dependent cell respiration. Furthermore, novel tankyrase1/2-dependent PARylation targets, including mitochondrial DLST, and OGDH, were revealed. This work exposed a new mechanistic framework by linking PARylation to respiration and metabolism, thereby broadening the current understanding that underlies these vital processes. XAV-939 poses an immediate and straightforward strategy to improve aerobic activities, and metabolism, in (immature) cell cultures.

Bioinformatics analysis and experimental verification of TIGD1 in non-small cell lung cancer

Introduction

Non-small cell lung cancer (NSCLC) is a prevalent respiratory system tumor. Triggered transposable element derivative 1 (TIGD1) exhibits significant overexpression in various tumor cells and tissues, suggesting its involvement in cancer progression.

Methods

Clinical data and gene expression profiles of lung adenocarcinoma were collected from TCGA, UCSC XENA, and GEO databases. Computational techniques and empirical studies were employed to analyze the role of TIGD1 in NSCLC. Cellular experiments were conducted using the H1299 cell line, including RNA interference, cell viability assays, quantitative PCR, wound-healing assays, western blotting, and plate clone formation assays.

Results

Bioinformatics analysis revealed TIGD1's potential as a biomarker for diagnosing and predicting lung cancer. It also indicated promise as a target for immune-related therapy and targeted drug therapy. Cellular studies confirmed TIGD1's involvement in cancer cell proliferation, invasion, and migration. Furthermore, an association between TIGD1 and the PI3K/AKT signaling pathway was suggested.

Discussion

The findings suggest that TIGD1 plays a vital role in NSCLC progression, making it a potential diagnostic biomarker and therapeutic target. The association with the PI3K/AKT signaling pathway provides insights into the underlying molecular mechanisms. Integrating computational analysis with empirical studies enhances our understanding of TIGD1's significance in NSCLC and opens avenues for further research into targeted therapies.

Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics

Background

Gastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer.

Methods

In this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR).

Results

In total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics.

Conclusion

Our study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.

Genomic and transcriptome insight into the structure and immunity role of TRIM proteins in Chinese soft-shelled turtles (Pelodiscus sinensis) after Aeromonas hydrophila infection

BACKGROUND

TRIM proteins, recognized as a class of E3 ubiquitin ligases, are increasingly acknowledged for their antipathogen immune functions in mammals and fish. In the Chinese soft-shelled turtle (Pelodiscus sinensis), a secondary aquatic reptile that occupies a unique evolutionary position, the TRIM gene has rarely been reported.

METHODS AND RESULTS

In the present study, 48 PsTRIM proteins were identified from the genome of Pelodiscus sinensis via Hidden Markov Model (HMM) searches and Signal Transduction ATPases with Numerous Domains (SMART) analysis. These PsTRIMs were found across 43 distinct scaffolds, and phylogenetic analyses classified them into three principal clades. The PsTRIMs feature a conserved assembly of either RING-B-box-coiled-coil (RBCC) or B-box-coiled-coil (BBC) domains at the N-terminus, in addition to eight unique domains at the C-terminus, including the B30.2 domain, 19 of which were identified. Expression profiling revealed ubiquitous expression of the 48 PsTRIMs across various P. sinensis tissues. Notably, seven PsTRIMs exhibited significant differential expression in liver transcriptomes following infection with Aeromonas hydrophila. Weighted gene coexpression network analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis implicated PsTRIM14 and PsTRIM28 as key players in host defense against bacterial invasion. Real-time quantitative PCR results indicated that PsTRIM1, PsTRIM2, PsTRIM14, and PsTRIM28 experienced marked upregulation in P. sinensis livers at 12 h post-infection with A. hydrophila.

CONCLUSIONS

Our study is the first to comprehensively identify and analyze the functions of TRIM genes in P. sinensis, unveiling their considerable diversity and potential roles in modulating immune responses.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Ephrin-Eph receptor tyrosine kinases for potential therapeutics against hepatic pathologies

Hepatic fibrosis is the common pathological change that occurs due to increased synthesis and accumulation of extracellular matrix components. Chronic insult from hepatotoxicants leads to liver cirrhosis, which if not reversed timely using appropriate therapeutics, liver transplantation remains the only effective therapy. Often the disease further progresses into hepatic carcinoma. Although there is an increased advancement in understanding the pathological phenotypes of the disease, additional knowledge of the novel molecular signaling mechanisms involved in the disease progression would enable the development of efficacious therapeutics. Ephrin-Eph molecules belong to the largest family of receptor tyrosine kinases (RTKs) which are identified to play a crucial role in cellular migratory functions, during morphological and developmental stages. Additionally, they contribute to the growth of a multicellular organism as well as in pathological conditions like cancer, and diabetes. A wide spectrum of mechanistic studies has been performed on ephrin-Eph RTKs in various hepatic tissues under both normal and diseased conditions revealing their diverse roles in hepatic pathology. This systematic review summarizes the liver-specific ephrin-Eph RTK signaling mechanisms and recognizes them as druggable targets for mitigating hepatic pathology.

Introducing novel key genes and transcription factors associated with rectal cancer response to chemoradiation through co-expression network analysis

Preoperative radiochemotherapy is a promising therapeutic method for locally advanced rectal cancer patients. However, the response of colorectal cancer (CRC) patients to preoperative radiotherapy varies widely. In this study, we aimed to identify novel biomarkers that could predict the response of colorectal tumors to treatment using a systems biology approach. We applied the Weighted Gene Co-Expression Network Analysis to construct co-expression networks and evaluated the correlation of these networks with radiation using the module-trait relationship. We then identified hub genes and related transcription factors in the selected co-expression module. Our analysis of seven constructed modules revealed that one module, which contained 113 nodes and 6066 edges, had the strongest correlation with radiation effects on CRC (correlation = 0.85; p-value = 6e-7). By analyzing the selected module with the CytoHubba plugin, we identified four hub genes, including ZEB2, JAM2, NDN, and PPAP2A. We also identified seven important transcription factors, including KLF4, SUZ12, TCF4, NANOG, POU5F1, SOX2, and SMARCA4, which may play essential roles in regulating the four hub genes. In summary, our findings suggest that ZEB2, JAM2, NDN, and PPAP2A, along with the seven transcription factors related to these hub genes, may be associated with the response of colorectal tumors to chemoradiotherapy.

Unraveling the Significance of EPH/Ephrin Signaling in Liver Cancer: Insights into Tumor Progression and Therapeutic Implications

Liver cancer is a complex and challenging disease with limited treatment options and dismal prognosis. Understanding the underlying molecular mechanisms driving liver cancer progression and metastasis is crucial for developing effective therapeutic strategies. The EPH/ephrin system, which comprises a family of cell surface receptors and their corresponding ligands, has been implicated in the pathogenesis of HCC. This review paper aims to provide an overview of the current understanding of the role of the EPH/ephrin system in HCC. Specifically, we discuss the dysregulation of EPH/ephrin signaling in HCC and its impact on various cellular processes, including cell proliferation, migration, and invasion. Overall, the EPH/ephrin signaling system emerges as a compelling and multifaceted player in liver cancer biology. Elucidating its precise mechanisms and understanding its implications in disease progression and therapeutic responses may pave the way for novel targeted therapies and personalized treatment approaches for liver cancer patients. Further research is warranted to unravel the full potential of the EPH/ephrin system in liver cancer and its clinical translation.

A novel oncogene trigger transposable element derived-1 promotes oral squamous cell carcinoma progression via evoking immune inhibition

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck squamous cell carcinomas (HNSCC) globally. Its incidence rate is rapidly increasing, and its 5-year survival rate remains at 50%, despite advances in medical science. Trigger transposable element-derived 1 (TIGD1) has been found to be upregulated in various cancer types. However, its biological function in OSCC requires further investigation. We searched the Cancer Genome Atlas database using CIBERSORT and TIMER 2.0 to predict the significance of TIGD1 and evaluate its effect on immune cell infiltration. Gene set enrichment analysis was performed to determine the biological functions of TIGD1. Gain/loss of function techniques were used to explore the biological behavior of TIGD1 in Cal27 and HSC4 cells. Finally, flow cytometry was used to detect dendritic cell markers in an OSCC and dendritic cell co-culture model. Our results show that TIGD1 is upregulated significantly in OSCC and is closely associated with tumor progression and prognosis. TIGD1 functions as an oncogene by increasing cells proliferation, inhibiting apoptosis, promoting cell invasion and migration. TIGD1 is also involved in tumor immune cell infiltration. Its overexpression can inhibit dendritic cell maturation, leading to immune suppression and tumor progression. High TIGD1 expression, which promotes OSCC progression, might be related to decreased dendritic cell maturation and activation. These findings suggest that TIGD1-specific small interfering RNA synthesized in vitro could be a new target for OSCC immunotherapy.

Identification of key genes in hepatocellular carcinoma associated with exposure to TCDD and α-endosulfan by WGCNA

2,3,7,8-tet-rachlorodibenzo-p-dioxin (TCDD) and α-endosulfan are two typical persistent organic pollutants (POPs), both of which accumulate in the liver and have potential carcinogenic hepatic effects. The underlying molecular mechanisms of pathogenesis of hepatocellular carcinoma (HCC) remain elusive when exposure to POPs. The aim of this study is to explore the key genes involved in HCC when exposure to TCDD and α-endosulfan by weighted gene co-expression network analysis (WGCNA). First, we performed co-expressed analysis on HCC and normal condition, based on WGCNA. In results, seven co-expressed modules were identified from 56 human liver samples, and the brown module correlated with five stages of HCC. Subsequently, we predicted that human five liver diseases were associated with exposure to TCDD and/or α-endosulfan by Nextbio analysis. Functional enrichment analysis showed that the brown module enriched in oxidation-reduction process, DNA replication, oxidoreductase activity and aging, which were the same as the results when exposure to the mixture of TCDD and α-endosulfan. Lastly, based on the protein-protein interaction network, we identified three novel genes including HK2, EXO1 and PFKP as key genes in HCC associated with exposure to TCDD and α-endosulfan mixture. In addition, survival analysis of key genes in Kaplan-Meier plotter demonstrated that aberrant expression levels of all the three key genes were associated with poor prognosis of HCC. Finally, Western blot analysis confirmed that protein expression levels of PFKP and HK2 in the three exposed groups were significantly elevated, while EXO1 were significantly upregulated when exposure to TCDD and α-endosulfan mixture in HepaRG cells. This study provides a new perspective to the understanding of the genetic mechanism of HCC when exposure to POPs.

CsMYC2 is involved in the regulation of phenylpropanoid biosynthesis induced by trypsin in cucumber (Cucumis sativus) during storage

Trypsin has a new activity of scavenging superoxide anion and generating hydrogen peroxide. Trypsin can significantly improve the storage quality of C. sativus. To illustrate the mechanism of trypsin-induced resistance in fruits and vegetables, an integrated analysis of widely targeted metabolomics and transcriptomics was carried out. Transcriptomic results showed that 1068 genes highly related to phenylpropanoid biosynthesis gathered in the brown module were obtained by WGCNA. In KEGG analysis, differentially expressed genes (DEGs) were also highly enriched in EIP (Environmental Information Processing) pathways "Plant hormone signal transduction (map04075)" and "MAPK signaling pathway-plant (map04016)". Next, 87 genes were identified as the leading edge by GSEA analysis. So far, CsMYC2 was highlighted as a key transcription factor that regulates phenylpropanoid biosynthesis identified by GSEA and WGCNA. Furthermore, the major route of biosynthesis of phenylpropanoid compounds including coumarins, lignins, chlorogenic acid, flavonoids, and derivatives regulated by trypsin was also illustrated by both transcriptomic and metabolomic data. Results of O2PLS showed that CsMYC2 was positively correlated with Rosmarinic acid-3-O-glucoside, Epigallocatechin, Quercetin-3-O-sophoroside (Baimaside), and so on. Correlation between CsMYC2, phenylpropanoid related genes, and metabolites in C. sativus was illustrated by co-expression networks. Roles of CsMYC2 were further checked in C. sativus by VIGS. The results of this study might give new insight into the exploration of the postharvest resistance mechanism of C. sativus induced by trypsin and provide useful information for the subsequent mining of resistance genes in C. sativus.

Novel Gene Signatures Promote Epithelial-Mesenchymal Transition (EMT) in Glucose Deprivation-Based Microenvironment to Predict Recurrence-Free Survival in Hepatocellular Carcinoma

Background

Current research studies have suggested that glucose deprivation (GD)-based tumor microenvironment (TME) can promote epithelial-mesenchymal transition (EMT) of tumor cells, leading to tumor invasion and metastasis. However, no one has yet studied detailedly the synthetic studies that include GD features in TME with EMT status. In our research, we comprehensively developed and validated a robust signature regarding GD and EMT status to provide prognostic value for patients with liver cancer.

Methods

GD and EMT status were estimated with transcriptomic profiles based on WGCNA and t-SNE algorithms. Two cohorts of training (TCGA_LIHC) and validation (GSE76427) datasets were analyzed with the Cox regression and logistic regression analyses. We identified a 2-mRNA signature to establish a GD-EMT-based gene risk model for the prediction of HCC relapse.

Results

Patients with significant GD-EMT status were divided into two subgroups: GD^low/EMT^low and GD^high/EMT^high, with the latter having significantly worse recurrence-free survival (P < 0.01). We employed the least absolute shrinkage and selection operator (LASSO) technique as a method for HNF4A and SLC2A4 filtering and constructing a risk score for risk stratification. In the multivariate analysis, this risk score predicted recurrence-free survival (RFS) in both the discovery and validation cohorts and remained valid in patients stratified by TNM stage and age at diagnosis. The nomogram that combines risk score and TNM stage as well as age produces improved performance and net benefits in the analysis of calibration and decision curves in training and validation groups.

Conclusions

The GD-EMT-based signature predictive model may provide a prognosis classifier for HCC patients with a high risk of postoperative recurrence to decrease the relapse rate.

ELAC2 Functions as a Key Gene in the Early Development of Placental Formation Based on WGCNA

The placenta plays a crucial role in mammalian fetal growth. The most important cell type in the placenta is the trophoblast cell. Many genes have been reported to play important functions in the differentiation of early placental trophoblast cells. Weighted gene co-expression network analysis (WGCNA) is a systematic biological method for describing the correlation patterns among genes across microarray samples. We used WGCNA to screen placental trophoblast development-related genes, and through experimental confirmation, we showed that, among these genes, ELAC2 may play an important regulatory role in the early development of mammalian placental formation. ELAC2 regulates early placental trophoblast differentiation by affecting cell migration and cell proliferation. In addition, ELAC2 may be involved in regulating cell migration processes in a manner that affects epithelial mesenchymal transition (EMT).

A 9-gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co-expression and regression analyses

This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co-expression network analysis (WGCNA) and Cox regression. The KIRC-associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co-expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein-protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso-Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT-PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p53 signaling pathway. Twenty-six candidates were subsequently screened out from the green module. Next, a 9-gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer-related signaling pathways were differently activated between two risk score groups. Additionally, under-expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development.

Screening of Diabetic Nephropathy Progression-Related Genes Based on Weighted Gene Co-expression Network Analysis

The purpose of this study is to explore the progression-related genes of diabetic nephropathy (DN) through weighted gene co-expression network analysis (WGCNA). The gene expression dataset GSE14202 was downloaded from the GEO database for differential expression analysis. WGCNA v1.69 was used to perform co-expression analysis on differentially expressed genes. 25 modular genes were selected through WGCNA. The motif enrichment analysis was performed on 25 genes, and 34 motifs were obtained, of which 8 transcription factors (TFs) were differentially expressed. GENIE3 was applied to analyze the expression correlation of 8 differentially expressed TFs and 25 genes. Combined with the predicted TF-target gene relationship, 69 interactions between 8 TFs and 18 genes were obtained. The functional enrichment analysis of 18 genes showed that 7 key genes were obviously enriched in adaptive immune response and were clearly up-regulated in advanced DN patients. The expression of C1S, LAIR1, CD84, SIT1, SASH3, and CD180 in glomerular samples from DN patients was significantly up-regulated in compared with normal samples, and the expression of these genes was negatively correlated with GFR. We observed that in the in vitro cell model of DN, the relative expression levels of 5 key genes (except SASH3) were obviously elevated in the high-glucose group. Five key genes were identified to be related to the progression of DN. The findings of this study may provide new ideas and therapeutic targets for exploring the pathogenesis of DN.

Establishment and validation of a predictive model of preeclampsia based on transcriptional signatures of 43 genes in decidua basalis and peripheral blood

Preeclampsia (PE) has an increasing incidence worldwide, and there is no gold standard for prediction. Recent progress has shown that abnormal decidualization and impaired vascular remodeling are essential to PE pathogenesis. Therefore, it is of great significance to analyze the decidua basalis and blood changes of PE to explore new methods. Here, we performed weighted gene co-expression network analysis based on 9553 differentially expressed genes of decidua basalis data (GSE60438 includes 25 cases of PE and 23 non-cases) from Gene Expression Omnibus to screen relevant module-eigengenes (MEs). Among them, MEblue and MEgrey are the most correlated with PE, which contains 371 core genes. Subsequently, we applied the logistic least absolute shrinkage and selection operator regression, screened 43 genes most relevant to prediction from the intersections of the 371 genes and training set (GSE48424 includes 18 cases of PE and 18 non-cases) genes, and built a predictive model. The specificity and sensitivity are illustrated by receiver operating characteristic curves, and the stability was verified by two validation sets (GSE86200 includes 12 cases of PE and 48 non-cases, and GSE85307 includes 47 cases of PE and 110 non-cases). The results demonstrated that our predictive model shows good predictions, with an area under the curve of 0.991 for the training set, 0.874 and 0.986 for the validation sets. Finally, we found the 43 key marker genes in the model are closely associated with the clinically accepted predictive molecules, including FLT1, PIGF, ENG and VEGF. Therefore, this predictive model provides a potential approach for PE diagnosis and treatment.

Hepatic stellate cell-released CXCL1 aggravates HCC malignant behaviors through the MIR4435-2HG/miR-506-3p/TGFB1 axis

Hepatic stellate cell (HSC) activation is a critical event in the development of hepatic fibrosis and hepatocellular carcinoma (HCC). By the release of soluble cytokines, chemokines, and chemotaxis, HSCs affect HCC cell phenotypes through a complex tumor microenvironment. In this study, weighted gene co-expression network analysis (WGCNA) was used to identify the TGF-β signaling pathway as a key signaling pathway in Hep3B cells cultured in HSC conditioned medium. MIR4435-2HG is a hub lncRNA associated with the TGF-β signaling pathway and HSC activation. HSC-condition medium (CM) culture induced HCC cell malignant behaviors, which were partially reversed by MIR4435-2HG silencing. miR-506-3p directly bound to MIR4435-2HG and the 3'UTR of TGFB1. Similarly, overexpression of miR-506-3p also attenuated HSC-CM-induced malignant behavior of HCC cells. In HSC-CM cultured HCC cells, the effects of MIR4435-2HG knockdown on TGFB1 expression and HCC cell phenotypes were partially reversed by miR-506-3p inhibition. HSCs affected HCC cell phenotypes by releasing CXCL1. In an orthotopic xenotransplanted tumor model of HCC cells plus HSCs in mice, CXCR2 knockdown in HCC cells significantly inhibited tumorigenesis, which was partially reversed by MIR4435-2HG overexpression in HCC cells. In HCC tissue samples, the levels of CXCL1, TGF-β1, and MIR4435-2HG were upregulated, while miR-506-3p expression was downregulated. In conclusion, HSC-released CXCL1 aggravated HCC cell malignant behaviors through the MIR4435-2HG/miR-506-3p/TGFB1 axis. In addition to CXCL1, the MIR4435-2HG/miR-506-3p/TGFB1 axis might also be the underlying target for HCC therapy.

Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.

Transcription Factors and ncRNAs Associated with CYP3A Expression in Human Liver and Small Intestine Assessed with Weighted Gene Co-Expression Network Analysis

CYP3A4, CYP3A5, and CYP3A7, which are located in a multigene locus (CYP3A), play crucial roles in drug metabolism. To understand the highly variable hepatic expression of CYP3As, regulatory network analyses have focused on transcription factors (TFs). Since long non-coding RNAs (lncRNAs) likely contribute to such networks, we assessed the regulatory effects of both TFs and lncRNAs on CYP3A expression in the human liver and small intestine, main organs of CYP3A expression. Using weighted gene co-expression network analysis (WGCNA) of GTEx v8 RNA expression data and multiple stepwise regression analysis, we constructed TF-lncRNA-CYP3A co-expression networks. Multiple lncRNAs and TFs displayed robust associations with CYP3A expression that differed between liver and small intestines (LINC02499, HNF4A-AS1, AC027682.6, LOC102724153, and RP11-503C24.6), indicating that lncRNAs contribute to variance in CYP3A expression in both organs. Of these, HNF4A-AS1 had been experimentally demonstrated to affect CYP3A expression. Incorporating ncRNAs into CYP3A expression regulatory network revealed additional candidate TFs associated with CYP3A expression. These results serve as a guide for experimental studies on lncRNA-TF regulation of CYP3A expression in the liver and small intestines.

Potential core genes associated with COVID-19 identified via weighted gene co-expression network analysis

AIMS

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus belonging to the Coronaviridae family that causes coronavirus disease (COVID-19). This disease rapidly reached pandemic status, presenting a serious threat to global health. However, the detailed molecular mechanism contributing to COVID-19 has not yet been elucidated.

METHODS

The expression profiles, including the mRNA levels, of samples from patients infected with SARS-CoV-2 along with clinical data were obtained from the GSE152075 dataset in the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules, which were then implemented to evaluate the relationships between fundamental modules and clinical traits. The differentially expressed genes (DEGs), gene ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were evaluated using R software packages.

RESULTS

A total of 377 SARS-CoV-2-infected samples and 54 normal samples with available clinical and genetic data were obtained from the GEO database. There were 1444 DEGs identified between the sample types, which were used to screen out 11 co-expression modules in the WGCNA. Six co-expression modules were significantly associated with three clinical traits (SARS-CoV-2 positivity, age, and sex). Among the DEGs in two modules significantly correlated with SARS-CoV-2 positivity, enrichment was observed in the biological process of viral infection strategies (viral translation) in the GO analysis. The KEGG signalling pathway analysis demonstrated that the DEGs in the two modules were commonly enriched in oxidative phosphorylation, ribosome, and thermogenesis pathways. Moreover, a five-core gene set (RPL35A, RPL7A, RPS15, RPS20, and RPL17) with top connectivity with other genes was identified in the SARS-CoV-2 infection modules, suggesting that these genes may be indispensable in viral transcription after infection.

CONCLUSION

The identified core genes and signalling pathways associated with SARS-CoV-2 infection can significantly supplement the current understanding of COVID-19. The five core genes encoding ribosomal proteins may be indispensable in viral protein biosynthesis after SARS-CoV-2 infection and serve as therapeutic targets for COVID-19 treatment. These findings can be used as a basis for creating a hypothetical model for future experimental studies regarding associations of SARS-CoV-2 infection with ribosomal protein function.

EuRBG10 involved in indole alkaloids biosynthesis in Eucommia ulmoides induced by drought and salt stresses

Alkaloids are natural products with many important medicinal activities. To explore the mechanism of abiotic stress promoting alkaloid biosynthesis in Eucommia ulmoides, transcriptomic analysis and metabonomic analysis were used, virus-induced gene silencing (VIGS) lines of target gene were constructed. The results showed that drought and salt stress caused wilting and blackening of leaves, decreased chlorophyll level, and significantly induced MDA and relative conductivity. To resist the damage of stress to cells, the level of secondary metabolites such as alkaloids increased significantly with the extension of stress time. Transcriptomic results showed that, were. Six alkaloid related genes (AWGs) were gathered in five modules positively correlated with either salt stress or alkaloid contents by WGCNA. Results of GO and KEGG enrichment revealed that biosynthesis of alkaloid, especially indole alkaloid was induced, and degradation of alkaloid was inhibited under salt stress. Combining the results of transcriptome and metabolomics, it was suggested that EuRBG10 promotes the production of indole alkaloids and EuAMO5 inhibits the degradation of alkaloids, which may be the core mechanism of the indole alkaloid biosynthesis pathway (map00901) induced by salt stress. The results of these hub proteins were also consistent with the chordal graph of KEGG enrichment. Hub roles of EuRGB10 was checked in E. ulmoides by VIGS. Our findings provide a preliminary understanding of abiotic stress regulating secondary metabolites such as alkaloids, and propose hub genes that can be used to improve the level of bioactive components in medicinal plant.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Transcriptome analysis of Homo sapiens and Mus musculus reveals mechanisms of CD8+ T cell exhaustion caused by different factors

T cell exhaustion is a state of T cell dysfunction during chronic infection and cancer. Antibody-targeting immune checkpoint inhibitors to reverse T cell exhaustion is a promising approach for cancer immunotherapy. However, molecular mechanisms of T cell exhaustion remain incompletely understood. Here, we performed a transcriptome analysis by integrating seven exhaustion datasets caused by multiple diseases in both humans and mice. In this study, an overlap of 21 upregulated and 37 downregulated genes was identified in human and mouse exhausted CD8+ T cells. These genes were significantly enriched in exhaustion response-related pathways, such as signal transduction, immune system processes, and regulation of cytokine production. Gene expression network analysis revealed that the well-documented exhaustion genes were defined as hub genes in upregulated genes. In addition, a weighted gene co-expression analysis identified 175 overlapping genes that were significantly correlated with the exhaustion trait in both humans and mice. This study found that overlapping six genes were significantly upregulated and highly related to T cell exhaustion. Finally, we revealed that CD200R1 and ADGRG1, less described previously in exhaustion, contributed to T cell exhaustion. Overall, our findings reveal the mechanisms of T cell exhaustion and provide an important reference to the immunology community.

Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma

Objective

To study the differentially expressed genes between multiple myeloma and healthy whole blood samples by bioinformatics analysis, find out the key genes involved in the occurrence, development and prognosis of multiple myeloma, and analyze and predict their functions.

Methods

The gene chip data GSE146649 was downloaded from the GEO expression database. The gene chip data GSE146649 was analyzed by R language to obtain the genes with different expression in multiple myeloma and healthy samples, and the cluster analysis heat map was constructed. At the same time, the protein-protein interaction (PPI) networks of these DEGs were established by STRING and Cytoscape software. The gene co-expression module was constructed by weighted correlation network analysis (WGCNA). The hub genes were identified from key gene and central gene. TCGA database was used to analyze the expression of differentially expressed genes in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.

Results

We identified four genes (TNFSF11, FGF2, SGMS2, IGFBP7) as hub genes of multiple myeloma. Then, TCGA database was used to analyze the survival of TNFSF11, FGF2, SGMS2 and IGFBP7 in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.

Conclusion

The study suggests that TNFSF11, FGF2, SGMS2 and IGFBP7 are important research targets to explore the pathogenesis, diagnosis and treatment of multiple myeloma.

Identification of Hub Genes of Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network in Chinese Population

Purpose: Lung adenocarcinoma is one of the most common malignancies. Though some historic breakthroughs have been made in lung adenocarcinoma, its molecular mechanisms of development remain elusive. The aim of this study was to identify the potential genes associated with the lung adenocarcinoma progression and to provide new ideas for the prognosis evaluation of lung adenocarcinoma.

Methods: The transcriptional profiles of ten pairs of snap-frozen tumor and adjacent normal lung tissues were obtained by performing RNA-seq. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks in order to explore the associations of gene sets with the clinical features and to investigate the functional enrichment analysis of co-expression genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment Analysis (GSEA) analyses were performed using clusterProfiler. The protein-protein network (PPI) was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and hub genes were identified using Cytohubba in Cytoscape. Transcription factor enrichment analysis was performed by the RcisTarget program in R language.

Results: Based on RNA-seq data, 1,545 differentially expressed genes (DEGs) were found. Eight co-expression modules were identified among these DEGs. The blue module exhibited a strong correlation with LUAD, in which ADCY4, RXFP1, AVPR2, CALCRL, ADRB1, RAMP3, RAMP2 and VIPR1 were hub genes. A low expression level of RXFP1, AVPR2, ADRB1 and VIPR1 was detrimental to the survival of LUAD patients. Genes in the blue module enriched in 86 Gene Ontology terms and five KEGG pathways. We also found that transcription factors EGR3 and EXOSC3 were related to the biological function of the blue module. Overall, this study brings a new perspective to the understanding of LUAD and provides possible molecular biomarkers for prognosis evaluation of LUAD.

Identification of key pathways and genes in the progression of silicosis based on WGCNA

Silicosis, induced by inhaling silica particles in workplaces, is one of the most common occupational diseases. The prognosis of silicosis and its consequent fibrosis is extremely poor due to limited treatment modalities and lack of understanding of the disease mechanisms. In this study, a Wistar rat model for silicosis fibrosis was established by intratracheal instillation of silica (0, 50, 100 and 200 mg/mL, 1 mL) with the evidence of Hematoxylin and Eosin (HE) and Masson staining and the expressions of inflammatory and fibrotic proteins of rats' lung tissues. RNA of lung tissues of rats exposed to 200 mg/mL silica particles and normal saline for 14 d and 28 d was extracted and sequenced to detect differentially expressed genes (DEGs) and to identify silicosis fibrosis-associated modules and hub genes by Weighted gene co-expression network analysis (WGCNA). Predictions of gene functions and signaling pathways were conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. In this study, it has been demonstrated the promising role of the Hippo signaling pathway in silicosis fibrosis, which will be conducive to elucidating the specific mechanism of pulmonary fibrosis induced by silica and to determining molecular initiating event (MIE) and adverse outcome pathway (AOP) of silicosis fibrosis.

Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses

Background

Recent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer.

Materials and Methods

The database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, etc.) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors.

Results

In this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor.

Conclusion

A total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

Multispecies comparative analysis reveals transcriptional specificity during Mongolian horse testicular development

Mongolian horses have been bred and used for labor and transport for centuries. Nevertheless, traits of testicular development in Mongolian horses have rarely been studied; particularly, studies regarding the transcriptional regulation characteristics of testicular development are lacking. In this paper, transcription specificity during testicular development in Mongolian horses is highlighted via a multispecies comparative analysis and weighted gene co-expression network analysis (WGCNA). Interestingly, the results showed that most genes were up-regulated in the testes after sexual maturity, which is a phenomenon conserved across species. Moreover, we observed nine key genes involved in regulating Mongolian horse testicular development. Notably, unique transcription signatures of testicular development in Mongolian horses are emphasized, which provides a novel insight into the mechanistic study of their testicular development.

Identification of Immune-Related Hub Genes in Thymoma: Defects in CD247 and Characteristics of Paraneoplastic Syndrome

Background: Thymomas (Ts) and thymic carcinomas (TCs) are rare primary tumors of the mediastinum. Paraneoplastic syndrome (PNS) is an important feature of thymoma, which presents great challenges to clinicians.

Methods: The present study uses the weighted gene co-expression network analysis (WGCNA) to identify possible immunologic mechanisms of thymoma. RNA sequencing data from thymoma samples were downloaded from the TCGA. Core genes were taken from the module that is closely related to the WHO’s stage of classification. Enhanced analysis using the online database “Metascape” and an overall survival (OS) analysis were carried out via the Kaplan–Meier method. The hub genes were obtained from the protein–protein interaction (PPI) network. In addition, we jointly analyzed multiple sets of PNS data related to thymomas from other sources to verify the correlation between thymomas and PNS. The impact of hub genes on the prognosis of PNS was evaluated via the ROC curve, with simultaneous analysis of immune infiltration by CIBERSORT.

Findings: The 14 immune hub genes closely related to thymomas were found to be jointly involved in the T-cell receptor signaling pathway. Compared to the normal thymus and type B1/B2 thymoma, there is a lower number of T-cells in type A/B3 thymoma and thymic carcinoma. The expression of genes related to the T-cell receptor signaling pathway appeared defective. The low expression of CD247 and the decrease in the number of mature T-cells are common features among thymomas, specific pulmonary fibrosis, rheumatoid arthritis, and systemic lupus erythematosus.

Identification of key sex-specific pathways and genes in the subcutaneous adipose tissue from pigs using WGCNA method

Background

Adipose tissues (ATs), including visceral ATs (VATs) and subcutaneous ATs (SATs), are crucial for maintaining energy and metabolic homeostasis. SATs have been found to be closely related to obesity and obesity-induced metabolic disease. Some studies have shown a significant association between subcutaneous fat metabolism and sexes. However, the molecular mechanisms for this association are still unclear. Here, using the pig as a model, we investigated the systematic association between the subcutaneous fat metabolism and sexes, and identified some key sex-specific pathways and genes in the SATs from pigs.

Results

The results revealed that 134 differentially expressed genes (DEGs) were identified in female and male pigs from the obese group. A total of 17 coexpression modules were detected, of which six modules were significantly correlated with the sexes (P < 0.01). Among the significant modules, the greenyellow module (cor = 0.68, P < 9e-06) and green module (cor = 0.49, P < 0.003) were most significantly positively correlated with the male and female, respectively. Functional analysis showed that one GO term and four KEGG pathways were significantly enriched in the greenyellow module while six GO terms and six KEGG pathways were significantly enriched in the green module. Furthermore, a total of five and two key sex-specific genes were identified in the two modules, respectively. Two key sex-specific pathways (Ras-MAPK signaling pathway and type I interferon response) play an important role in the SATs of males and females, respectively.

Conclusions

The present study identified some key sex-specific pathways and genes in the SATs from pigs, which provided some new insights into the molecular mechanism of being involved in fat formation and immunoregulation between pigs of different sexes. These findings may be beneficial to breeding in the pig industry and obesity treatment in medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01054-w.

Differential regulation of intramuscular fat and abdominal fat deposition in chickens

Background

Chicken intramuscular fat (IMF) content is closely related to meat quality and performance, such as tenderness and flavor. Abdominal fat (AF) in chickens is one of the main waste products at slaughter. Excessive AF reduces feed efficiency and carcass quality.

Results

To analyze the differential deposition of IMF and AF in chickens, gene expression profiles in the breast muscle (BM) and AF tissues of 18 animals were analyzed by differential expression analysis and weighted co-expression network analysis. The results showed that IMF deposition in BM was associated with pyruvate and citric acid metabolism through GAPDH, LDHA, GPX1, GBE1, and other genes. In contrast, AF deposition was related to acetyl CoA and glycerol metabolism through FABP1, ELOVL6, SCD, ADIPOQ, and other genes. Carbohydrate metabolism plays an essential role in IMF deposition, and fatty acid and glycerol metabolism regulate AF deposition.

Conclusion

This study elucidated the molecular mechanism governing IMF and AF deposition through crucial genes and signaling pathways and provided a theoretical basis for producing high-quality broilers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08538-0.

Environment-driven shifts in inter-individual variation and phenotypic integration within subnetworks of the mussel transcriptome and proteome

The environment can alter the magnitude of phenotypic variation among individuals, potentially influencing evolutionary trajectories. However, environmental influences on variation are complex and remain understudied. Populations in heterogeneous environments might exhibit more variation, the amount of variation could differ between benign and stressful conditions, and/or variation might manifest in different ways among stages of the gene‐to‐protein expression cascade or among physiological functions. Here, we explore these three issues by quantifying patterns of inter‐individual variation in both transcript and protein expression levels among California mussels, Mytilus californianus Conrad. Mussels were exposed to five ecologically relevant treatments that varied in the mean and interindividual heterogeneity of body temperature. To target a diverse set of physiological functions, we assessed variation within 19 expression subnetworks, including canonical stress‐response pathways and empirically derived coexpression clusters that represent a diffuse set of cellular processes. Variation in expression was particularly pronounced in the treatments with high mean and heterogeneous body temperatures. However, with few exceptions, environment‐dependent shifts of variation in the transcriptome were not reflected in the proteome. A metric of phenotypic integration provided evidence for a greater degree of constraint on relative expression levels (i.e., stronger correlation) within expression subnetworks in benign, homogeneous environments. Our results suggest that environments that are more stressful on average – and which also tend to be more heterogeneous – can relax these expression constraints and reduce phenotypic integration within biochemical subnetworks. Context‐dependent “unmasking” of functional variation may contribute to interindividual differences in physiological phenotype and performance in stressful environments.

Metabolic stimulation-elicited transcriptional responses and biosynthesis of acylated triterpenoids precursors in the medicinal plant Helicteres angustifolia

BACKGROUND

Helicteres angustifolia has long been used in Chinese traditional medicine. It has multiple pharmacological benefits, including anti-inflammatory, anti-viral and anti-tumor effects. Its main active chemicals include betulinic acid, oleanolic acid, helicteric acid, helicterilic acid, and other triterpenoid saponins. It is worth noting that some acylated triterpenoids, such as helicteric acid and helicterilic acid, are characteristic components of Helicteres and are relatively rare among other plants. However, reliance on natural plants as the only sources of these is not enough to meet the market requirement. Therefore, the engineering of its metabolic pathway is of high research value for enhancing the production of secondary metabolites. Unfortunately, there are few studies on the biosynthetic pathways of triterpenoids in H. angustifolia, hindering its further investigation.

RESULTS

Here, the RNAs of different groups treated by metabolic stimulation were sequenced with an Illumina high-throughput sequencing platform, resulting in 121 gigabases of data. A total of 424,824 unigenes were obtained after the trimming and assembly of the raw data, and 22,430 unigenes were determined to be differentially expressed. In addition, three oxidosqualene cyclases (OSCs) and four Cytochrome P450 (CYP450s) were screened, of which one OSC (HaOSC1) and one CYP450 (HaCYPi3) achieved functional verification, suggesting that they could catalyze the production of lupeol and oleanolic acid, respectively.

CONCLUSION

In general, the transcriptomic data of H. angustifolia was first reported and analyzed to study functional genes. Three OSCs, four CYP450s and three acyltransferases were screened out as candidate genes to perform further functional verification, which demonstrated that HaOSC1 and HaCYPi3 encode for lupeol synthase and β-amyrin oxidase, which produce corresponding products of lupeol and oleanolic acid, respectively. Their successful identification revealed pivotal steps in the biosynthesis of acylated triterpenoids precursors, which laid a foundation for further study on acylated triterpenoids. Overall, these results shed light on the regulation of acylated triterpenoids biosynthesis.

Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis

Aim

Methods

Results

Conclusions

Identification of Key Pathways and Genes Related to Immunotherapy Resistance of LUAD Based on WGCNA Analysis

Immunotherapy resistance is a major barrier in the application of immune checkpoint inhibitors (ICI) in lung adenocarcinoma (LUAD) patients. Although recent studies have found several mechanisms and potential genes responsible for immunotherapy resistance, ways to solve this problem are still lacking. Tumor immune dysfunction and exclusion (TIDE) algorithm is a newly developed method to calculate potential regulators and indicators of ICI resistance. In this article, we combined TIDE and weighted gene co-expression network analysis (WGCNA) to screen potential modules and hub genes that are highly associated with immunotherapy resistance using the Cancer Genome Atlas (TCGA) dataset of LUAD patients. We identified 45 gene co-expression modules, and the pink module was most correlated with TIDE score and other immunosuppressive features. After considering the potential factors in immunotherapy resistance, we found that the pink module was also highly related to cancer stemness. Further analysis showed enriched immunosuppressive cells in the extracellular matrix (ECM), immunotherapy resistance indicators, and common cancer-related signaling pathways in the pink module. Seven hub genes in the pink module were shown to be significantly upregulated in tumor tissues compared with normal lung tissue, and were related to poor survival of LUAD patients. Among them, THY1 was the gene most associated with TIDE score, a gene highly related to suppressive immune states, and was shown to be strongly expressed in late-stage patients. Immunohistochemistry (IHC) results demonstrated that THY1 level was higher in the progressive disease (PD) group of LUAD patients receiving a PD-1 monoclonal antibody (mAb) and positively correlated with SOX9. Collectively, we identified that THY1 could be a critical biomarker in predicting ICI efficiency and a potential target for avoiding tumor immunotherapy resistance.

Identification of Molecular Subgroups in Liver Cirrhosis by Gene Expression Profiles

Background: Liver cirrhosis is characterized by high mortality, bringing a serious health and economic burden to the world. The clinical manifestations of liver cirrhosis are complex and heterogeneous. According to subgroup characteristics, identifying cirrhosis has become a challenge. Objectives: The purpose of this study was to evaluate the difference between different subgroups of cirrhosis. The ultimate goal of research on these different phenotypes was to discover groups of patients with unique treatment characteristics, and formulate targeted treatment plans that improve the prognosis of the disease and improve the patients’ quality of life. Methods: We obtained the relevant gene chip by searching the gene expression omnibus (GEO) database. According to the gene expression profile, 79 patients with liver cirrhosis were divided into four subgroups, which showed different expression patterns. Therefore, we used weighted gene coexpression network analysis (WGCNA) to find differences between subgroups. Results: The characteristics of the WGCNA module indicated that subjects in subgroup I might exhibit inflammatory characteristics; subjects in subgroup II might exhibit metabolically active characteristics; arrhythmogenic right ventricular cardiomyopathy and neuroactive ligand-receptive somatic interaction pathways were significantly enriched in subgroup IV. We did not find a significantly upregulated pathway in the third subgroup. Conclusions: In this study, a new type of clinical phenotype classification of liver cirrhosis was derived by consensus clustering. This study found that patients in different subgroups may have unique gene expression patterns. This new classification method helps researchers explore new treatment strategies for cirrhosis based on clinical phenotypic characteristics.

Application of weighted gene co-expression network analysis to identify novel key genes in diabetic nephropathy

AIMS/INTRODUCTION

Diabetic nephropathy (DN) is among the leading causes of end-stage renal disease worldwide. DN pathogenesis remains largely unknown. Weighted gene co-expression network analysis is a powerful bioinformatic tool for identifying key genes in diseases.

MATERIALS AND METHODS

The datasets GSE30122, GSE104948, GSE37463 and GSE47185 containing 23 DN and 23 normal glomeruli samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. After data pre-processing, weighted gene co-expression network analysis was carried out to cluster significant modules. Then, Gene Set Enrichment Analysis-based Gene Ontology analysis and visualization of network were carried out to screen the key genes in the most significant modules. The connectivity map analysis was carried out to find the significant chemical compounds. Finally, some key genes were validated in in vivo and in vitro experiments.

RESULTS

A total of 454 upregulated and 392 downregulated genes were identified. A total of 16 modules were clustered, and the most significant modules (green, red and yellow modules) were determined. The green module was associated with extracellular matrix organization, the red module was associated with immunity reaction and the yellow module was associated with kidney development. We found several key genes in these three modules separately, and part of them were validated in vivo and in vitro successfully. We found the top 15 chemical compounds that could perturb the overall expression of key genes in DN.

CONCLUSION

Weighted gene co-expression network analysis was applied to DN expression profiling in combination with connectivity map analysis. Several novel key genes and chemical compounds were screened out, providing new molecular targets for DN.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Background

Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network.

Methods

The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network.

Results

A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes.

Conclusions

In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

Development and Validation of Hub Genes for Adrenal Aldosterone-Producing Adenoma by Integrated Bioinformatics Analysis

OBJECTIVE

To develop and validate hub genes involving in the development and progression of primary aldosteronism (PA) and adrenal aldosterone-producing adenoma (APA).

MATERIALS AND METHODS

A total of four datasets of gene expression profiles related to APA were downloaded from GEO datasets. GSE60042 and GSE8514 were used to identify DEGs. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network module analysis were conducted. GO and KEGG enrichment analysis was performed. GSE10927 and GSE33371 were used for further external validation.

RESULTS

We identified a total of 892 DEGs from GSE60042 and 1167 DEGs from GSE8514. WGCNA analysis demonstrated that the blue module (255 genes) and turquoise module (303 genes) were significantly correlated with APA. PPI networks were then constructed. GO term enrichment analysis suggested that cellular divalent inorganic cation homeostasis, calcium ion homeostasis, collagen-containing extracellular matrix, transport vesicle and metal ion transmembrane transporter activity were the vital annotations. KEGG pathway analysis found that these genes were significantly enriched in neuroactive ligand-receptor interaction, calcium signaling pathway. Finally, we identified a total of 11 candidate genes involving in the development and progression of APA and PA. Besides, two independent datasets (GSE10927 and GSE33371) were used for external validation, and there were seven hub genes successfully verified, including C3, GRM3, AVPR1A, WFS1, PTGFR, NTSR2, and JUN.

CONCLUSION

These newly identified genes could contribute to the understanding of potential mechanism in APA and PA and might be promising targets for the treatment of APA and PA.

Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes

The weighted gene co-expression network analysis (WGCNA) method constructs co-expressed gene modules based on the linear similarity between paired gene expressions. Linear correlations are the main form of similarity between genes, however, nonlinear correlations still existed and had always been ignored. We proposed a modified network analysis method, WGCNA-P + M, which combines Pearson's correlation coefficient and the maximum information coefficient (MIC) as the similarity measures to assess the linear and nonlinear correlations between genes, respectively. Taking two real datasets, GSE44861 and liver hepatocellular carcinoma (TCGA-LIHC), as examples, we compared the gene modules constructed by WGCNA-P + M and WGCNA from four perspectives: the "Usefulness" score, GO enrichment analysis on genes in the gray module, prediction performance of the top hub gene, survival analysis and literature reports on different hub genes. The results showed that the modules obtained by WGCNA-P + M are more biological meaningful, the hub genes obtained from WGCNA-P + M have more potential cancer genes.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

LsrB, the hub of ABC transporters involved in the membrane damage mechanisms of heavy ion irradiation in Escherichia coli

BACKGROUND

Ionizing radiation, especially heavy ion (HI) beams, has been widely used in biology and medicine. However, the mechanism of membrane damage by such radiation remains primarily uncharacterized.

PURPOSE

Transcriptomic profiles of Escherichia coli (E. coli) treated with HI illustrated the response mechanisms of the membrane, mainly ABC transporters, related genes regulated by antibiotics treatment through enrichment analyses of GO and KEGG. The networks of protein-protein interactions indicated that LsrB was the crucial one among the ABC transporters specially regulated by HI through the calculation of plugins MCODE and cytoHubba of Cytoscape. Finally, the expression pattern, GO/KEGG enrichment terms, and the interaction between nine LuxS/AI-2 quorum sensing system members were investigated.

CONCLUSIONS

Above all, results suggested that HI might perform membrane damage through regulated material transport, inhibited LuxS/AI-2 system, finally impeded biofilm formation. This work provides further evidence for the role of ABC transporters, especially LsrB, in membrane damage of E. coli to HI. It will provide new strategies for improving the precise application of HI.

ZEB1 serves an oncogenic role in the tumourigenesis of HCC by promoting cell proliferation, migration, and inhibiting apoptosis via Wnt/β-catenin signaling pathway

Zinc finger E-box-binding homeobox 1 (ZEB1), a functional protein of zinc finger family, was aberrant expressed in many kinds of liver disease including hepatic fibrosis and Hepatitis C virus. Bioinformatics results showed that ZEB1 was abnormally expressed in HCC tissues. However, to date, the potential regulatory role and molecular mechanisms of ZEB1 are still unclear in the occurrence and development of HCC. This study demonstrated that the expression level of ZEB1 was significantly elevated both in liver tissues of HCC patients and cell lines (HepG2 and SMMC-7721 cells). Moreover, ZEB1 could promote the proliferation, migration, and invasion of HCC cells. On the downstream regulation mechanism, ZEB1 could activate the Wnt/β-catenin signaling pathway by upregulating the protein expression levels of β-catenin, c-Myc, and cyclin D1. Novel studies showed that miR-708 particularly targeted ZEB1 3'-UTR regions and inhibited the HCC cell proliferation, migration, and invasion. Furthermore, results of nude mice experiments of HCC model indicated that miR-708 could inhibit tumor growth and xenograft metastasis model was established to validate that miR-708 could inhibit HCC cell metastasis through tail-vein injection in vivo. Together, the study suggested that ZEB1 modulated by miR-708 might be a potential therapeutic target for HCC therapy.

Identification of Key Genes and Pathways associated with Endometriosis by Weighted Gene Co-expression Network Analysis

Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. Results: We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.

Integrative iTRAQ-based proteomic and transcriptomic analysis reveals the accumulation patterns of key metabolites associated with oil quality during seed ripening of Camellia oleifera

Camellia oleifera (C. oleifera) is one of the four major woody oil-bearing crops in the world and has relatively high ecological, economic, and medicinal value. Its seeds undergo a series of complex physiological and biochemical changes during ripening, which is mainly manifested as the accumulation and transformation of certain metabolites closely related to oil quality, especially flavonoids and fatty acids. To obtain new insights into the underlying molecular mechanisms, a parallel analysis of the transcriptome and proteome profiles of C. oleifera seeds at different maturity levels was conducted using RNA sequencing (RNA-seq) and isobaric tags for relative and absolute quantification (iTRAQ) complemented with gas chromatography-mass spectrometry (GC-MS) data. A total of 16,530 transcripts and 1228 proteins were recognized with significant differential abundances in pairwise comparisons of samples at various developmental stages. Among these, 317 were coexpressed with a poor correlation, and most were involved in metabolic processes, including fatty acid metabolism, α-linolenic acid metabolism, and glutathione metabolism. In addition, the content of total flavonoids decreased gradually with seed maturity, and the levels of fatty acids generally peaked at the fat accumulation stage; these results basically agreed with the regulation patterns of genes or proteins in the corresponding pathways. The expression levels of proteins annotated as upstream candidates of phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS) as well as their cognate transcripts were positively correlated with the variation in the flavonoid content, while shikimate O-hydroxycinnamoyltransferase (HCT)-encoding genes had the opposite pattern. The increase in the abundance of proteins and mRNAs corresponding to alcohol dehydrogenase (ADH) was associated with a reduction in linoleic acid synthesis. Using weighted gene coexpression network analysis (WGCNA), we further identified six unique modules related to flavonoid, oil, and fatty acid anabolism that contained hub genes or proteins similar to transcription factors (TFs), such as MADS intervening keratin-like and C-terminal (MIKC_MADS), type-B authentic response regulator (ARR-B), and basic helix-loop-helix (bHLH). Finally, based on the known metabolic pathways and WGCNA combined with the correlation analysis, five coexpressed transcripts and proteins composed of cinnamyl-alcohol dehydrogenases (CADs), caffeic acid 3-O-methyltransferase (COMT), flavonol synthase (FLS), and 4-coumarate: CoA ligase (4CL) were screened out. With this exploratory multiomics dataset, our results presented a dynamic picture regarding the maturation process of C. oleifera seeds on Hainan Island, not only revealing the temporal specific expression of key candidate genes and proteins but also providing a scientific basis for the genetic improvement of this tree species.

HTR2B and SLC5A3 Are Specific Markers in Age-Related Osteoarthritis and Involved in Apoptosis and Inflammation of Osteoarthritis Synovial Cells

Objective: Osteoarthritis (OA) is a heterogeneous age-related disease, which is badly difficult to cure due to its complex regulatory networks of pathogenesis. This study explored OA-specific genes in synovial tissues and validated their roles on apoptosis and inflammation of OA synovial cells.

Methods: Weighted correlation network analysis (WGCNA) was employed to explore OA-related co-expression modules in the GSE55235 and GSE55457 datasets. Then, this study screened OA-specific genes. After validation of these genes in the GSE12021 and GSE32317 datasets, HTR2B and SLC5A3 were obtained. Their expression was detected in human OA and healthy synovial tissues by RT-qPCR and western blot. OA rat models were constructed by anterior cruciate ligament transection (ACLT) operation. In OA synovial cells, HTR2B and SLC5A3 proteins were examined via western blot. After transfection with sh-HTR2B or sh-SLC5A3, apoptosis and inflammation of OA synovial cells were investigated by flow cytometry and western blot.

Results: A total of 17 OA-specific DEGs were identified, which were significantly enriched in inflammation pathways. Among them, HTR2B and SLC5A3 were highly expressed in end-than early-stage OA. Their up-regulation was validated in human OA synovial tissues and ACLT-induced OA synovial cells. Knockdown of HTR2B and SLC5A3 restrained apoptosis and increased TGF-β and IL-4 expression as well as reduced TNF-α and IL-1β expression in OA synovial cells.

Conclusion: Collectively, this study identified two OA-specific markers HTR2B and SLC5A3 and their knockdown ameliorated apoptosis and inflammation of OA synovial cells.

Potential prognostic biomarkers related to immunity in clear cell renal cell carcinoma using bioinformatic strategy

The clear cell renal cell carcinoma (ccRCC) is the main pathological subtype of renal cell carcinoma. Immune system evasion, one hallmark of cancer, contributes to cancer cells in escaping from the attack of immune cells. In order to identify potential prognostic biomarkers in ccRCC patients and immune cells fraction, we collected and downloaded profiles from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. We obtained 2 modules significantly associated with tumor stage and immune cells; functional enrichment analysis showed that genes in the module ‘yellow’ were significantly enriched in proteins targeting to membrane and ribosome, as well as the oxidative phosphorylation pathway, while genes in the module ‘green’ mainly participate in molecular functions associated with immunity like activation of T cells. Four LncRNAs (LINC00472, AL590094.1, AL365203.3, and AC147651.3) and RPL27A and RPL22L1 in the module ‘yellow’ and two lncRNAs (LINC00426 and AC129507.2) and five protein-coding genes (CSF1, NOD2, ITGAE, CD7, and PDCD1) in the module ‘green’ represented independent prognostic values in patients with ccRCC. Expression of LINC0042, NOD2, CD7, and PDCD1 were significantly correlated with ratio of immune cells (like T cells CD8 and resting mast cells). LINC00426, with significant correlation with immune cell fraction, shows potential prognostic value in ccRCC patients. Our findings provide a strategy in exploring biomarkers with prognostic significance and significant association with the fraction of immune cells.