Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis

Shared Genetic Features of Psoriasis and Myocardial Infarction: Insights From a Weighted Gene Coexpression Network Analysis

BACKGROUND

Increasing evidence suggests a higher propensity for acute myocardial infarction (MI) in patients with psoriasis. However, the shared mechanisms underlying this comorbidity in these patients remain unclear. This study aimed to explore the shared genetic features of psoriasis and MI and to identify potential biomarkers indicating their coexistence.

METHODS AND RESULTS

Data sets obtained from the gene expression omnibus were examined using a weighted gene coexpression network analysis approach. Hub genes were identified using coexpression modules and validated in other data sets and through in vitro cellular experiments. Bioinformatics tools, including the Human microRNA Disease Database, StarBase, and miRNet databases, were used to construct a ceRNA network and predict potential regulatory mechanisms. By applying weighted gene coexpression network analysis, we identified 2 distinct modules that were significant for both MI and psoriasis. Inflammatory and immune pathways were highlighted by gene ontology enrichment analysis of the overlapping genes. Three pivotal genes-Src homology and collagen 1, disruptor of telomeric silencing 1-like, and feline leukemia virus subgroup C cellular receptor family member 2-were identified as potential biomarkers. We constructed a ceRNA network that suggested the upstream regulatory roles of these genes in the coexistence of psoriasis and MI.

CONCLUSIONS

As potential therapeutic targets, Src homology and collagen 1, feline leukemia virus subgroup C cellular receptor family member 2, and disruptor of telomeric silencing 1-like provide novel insights into the shared genetic features between psoriasis and MI. This study paves the way for future studies focusing on the prevention of MI in patients with psoriasis.

High expression of KIFC1 is a poor prognostic biomarker and correlates with TP53 mutation in lung cancer

The Kinesin Family Member C1 (KIFC1) is highly expressed in a variety of tumors. Since it is linked with tumorigenesis and progression, KIFC1 has emerged as a promising candidate for targeted chemotherapies. Thus, this study aims to find out the association between KIFC1 and lung cancer. The original data were assessed from The Cancer Genome Atlas and Gene Expression Omnibus databases. Compared to normal lung tissues, both mRNA and protein levels of KIFC1 were significantly increased in lung cancer tissues. The upregulation of KIFC1 was significantly correlated with sex, pathological stage, and TMN stage. Survival analysis revealed that increased KIFC1 expression was associated with poor overall survival, first-progression survival and post-progression survival in lung cancer. Based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, we observed that KIFC1 upregulation was linked to enrichment of the cell cycle and TP53 signaling pathway. Additionally, the overexpression of KIFC1 was positively correlated with TP53 mutations in lung cancer. Based on real-world cohort results, western blotting and RT-qPCR showed high-KIFC1 expression in lung cancer, which may be related to the malignancy of lung cancer. Finally, experiments in vitro showed that KIFC1 inhibitor could significantly inhibit the proliferation and invasion of lung cancer cells. In conclusion, KIFC1 is a poor prognostic biomarker, and patients with high-KIFC1 levels may benefit from targeted therapy.

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning

Background

To exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology.

Method

Three HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed.

Result

WGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value.

Conclusion

These five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.

Transcriptome analysis of human hypertrophic cardiomyopathy reveals inhibited cardiac development pathways in children

The epidemiological, etiological, and clinical characteristics vary greatly between pediatric (P-HCM) and adult (A-HCM) hypertrophic cardiomyopathy (HCM) patients, and the understanding of the heterogeneous pathogenesis mechanisms is insufficient to date. In this study, we aimed to comprehensively assess the respective transcriptome signatures and uncover the essential differences in gene expression patterns among A-HCM and P-HCM. The transcriptome data of adults were collected from public data (GSE89714), and novel pediatric data were first obtained by RNA sequencing from 14 P-HCM and 9 infantile donor heart samples. Our study demonstrates the common signatures of myofilament or protein synthesis and calcium ion regulation pathways in HCM. Mitochondrial function is specifically dysregulated in A-HCM, whereas the inhibition of cardiac developing networks typifies P-HCM. These findings not only distinguish the transcriptome characteristics in children and adults with HCM but also reveal the potential mechanism of the higher incidence of septal defects in P-HCM patients.

Identification and validation of functional roles for three MYC-associated genes in hepatocellular carcinoma

BACKGROUND

Aberrations in MYC underlie a large proportion of liver hepatocellular carcinoma (LIHC) cases; however, MYC is difficult to target because of its undruggable structure. We aimed to uncover MYC-associated molecular targets to provide new strategies for LIHC treatment.

METHODS

LIHC transcriptome datasets and clinical information were obtained from The Cancer Genome Atlas. A series of bioinformatics analyses were performed for 370 patients who were stratified based on the median MYC expression level (high-MYC group and low-MYC group). Correlation analysis was performed to determine relationships between the expression of key MYC-associated genes and prognosis, DNA promotor methylation, and immune cell infiltration. Gene ontology and Kyoto Encyclopedia of Genes and Genomes Pathway enrichment analyses were performed to elucidate the functions of these genes in LIHC. Their expression and functions in LIHC were further verified using transgenic mice overexpressing c-Myc under control of the hepatocyte-specific promoter (Alb-Cre).

RESULTS

AURKB, CCNB2, and CDKN3 were overexpressed in LIHC patients with high MYC expression and were associated with poor prognosis. Upregulation of these 3 genes was significantly correlated with hypomethylated promoter status, advanced T stage, metastasis, and immune cell infiltration in LIHC patients. Functional enrichment analyses indicated that these genes participate in the "p53 signaling pathway" and "cell cycle". Furthermore, RT-PCR and IHC analysis revealed that their mRNA and protein expression levels were upregulated in an Alb-Cre;cMYClsl/- mouse model. Drugs that target these 3 MYC-related genes were identified.

CONCLUSION

Taken together, our results identify biomarkers of potential utility for managing liver cancer therapy owing to their significance in tumorigenesis, proliferation, and tumor immunity.

CXADR promote epithelial-mesenchymal transition in endometriosis by modulating AKT/GSK-3β signaling

Endometriosis is a benign high prevalent disease exhibiting malignant features. However, the underlying pathogenesis and key molecules of endometriosis remain unclear. By integrating and analysis of existing expression profile datasets, we identified coxsackie and adenovirus receptor (CXADR), as a novel key gene in endometriosis. Based on the results of immunohistochemistry (IHC), we confirmed significant down-regulation of CXADR in ectopic endometrial tissues obtained from women with endometriosis compared with healthy controls. Further in vitro investigation indicated that CXADR regulated the stability and function of the phosphatases and AKT inhibitors PHLPP2 (pleckstrin homology domain and leucine-rich repeat protein phosphatase 2) and PTEN (phosphatase and tensin homolog). Loss of CXADR led to phosphorylation of AKT and glycogen synthase kinase-3β (GSK-3β), which resulted in stabilization of an epithelial-mesenchymal transition (EMT) factor, SNAIL1 (snail family transcriptional repressor 1). Therefore, EMT processs was induced, and the proliferation, migration and invasion of Ishikawa cells were enhanced. Over-expression of CXADR showed opposite effects. These findings suggest a previously undefined role of AKT/GSK-3β signaling axis in regulating EMT and reveal the involvement of a CXADR-induced EMT, in pathogenic progression of endometriosis.

Comprehensive In silico analysis of chaperones identifies CRYAB and P4HA2 as potential therapeutic targets and their small-molecule inhibitors for the treatment of cholangiocarcinoma

Cholangiocarcinoma (CCA) is a subtype of liver cancer with increasing incidence, poor prognosis, and limited treatment modalities. It is, therefore, imperative to identify novel therapeutic targets for better management of the disease. Chaperones are known to be significant regulators of carcinogenesis, however, their role in CCA remains unclear. This study aims to screen chaperones involved in CCA pathogenesis and identify drugs targeting key chaperones to improve the therapeutic response to the disease. To achieve this, first we mined the literature to create an atlas of human chaperone proteins. Next, their expression in CCA was determined by publicly available datasets of patients at mRNA and protein levels. In addition, our analysis involving protein-protein interaction and pathway analysis of eight key dysregulated chaperones revealed that they control crucial cancer-related pathways. Furthermore, topology analysis of the CCA network identified crystallin alpha-B protein (CRYAB) and prolyl-4-hydroxylase subunit 2 (P4HA2) as novel therapeutic targets for the disease. Finally, drug repurposing of 286 clinically approved anti-cancer drugs against these two chaperones performed by molecular docking and molecular dynamics simulations showed that tucatinib and regorafenib had a modulatory effect on them and could be potential inhibitors of CRYAB and P4HA2, respectively. Overall, our study, for the first time, provides insights into the pan-chaperone expression in CCA and explains the pathways that might drive CCA pathogenesis. Further, our identification of potential therapeutic targets and their inhibitors could provide new and complementary approaches to CCA treatment.

Genomic and Transcriptomic Insights into the Evolution and Divergence of MIKC-Type MADS-Box Genes in Carica papaya

MIKC-type MADS-box genes, also known as type II genes, play a crucial role in regulating the formation of floral organs and reproductive development in plants. However, the genome-wide identification and characterization of type II genes as well as a transcriptomic survey of their potential roles in Carica papaya remain unresolved. Here, we identified and characterized 24 type II genes in the C. papaya genome, and investigated their evolutional scenario and potential roles with a widespread expression profile. The type II genes were divided into thirteen subclades, and gene loss events likely occurred in papaya, as evidenced by the contracted member size of most subclades. Gene duplication mainly contributed to MIKC-type gene formation in papaya, and the duplicated gene pairs displayed prevalent expression divergence, implying the evolutionary significance of gene duplication in shaping the diversity of type II genes in papaya. A large-scale transcriptome analysis of 152 samples indicated that different subclasses of these genes showed distinct expression patterns in various tissues, biotic stress response, and abiotic stress response, reflecting their divergent functions. The hub-network of male and female flowers and qRT-PCR suggested that TT16-3 and AGL8 participated in male flower development and seed germination. Overall, this study provides valuable insights into the evolution and functions of MIKC-type genes in C. papaya.

A genome-wide co-expression network analysis revealed ZmNRAMP6-mediated regulatory pathway involved in maize tolerance to lead stress

KEY MESSAGE

A metal transporter ZmNRAMP6 was identified by using a trait-associated co-expression network analysis at a genome-wide level. ZmNRAMP6 confers maize sensitivity to Pb by accumulating it to maize shoots. ZmNRAMP6 knockout detains Pb in roots, activates antioxidant enzymes, and improves Pb tolerance. Lead (Pb) is one of the most toxic heavy metal pollutants, which can penetrate plant cells via root absorption and thus cause irreversible damages to the human body through the food chain. To identify the key gene responsible for Pb tolerance in maize, we performed a trait-associated co-expression network analysis at a genome-wide level, using two maize lines with contrasting Pb tolerances. Finally, ZmNRAMP6 that encodes a metal transporter was identified as the key gene among the Pb tolerance-associated co-expression module. Heterologous expression of ZmNRAMP6 in yeast verified its role in Pb transport. Combined Arabidopsis overexpression and maize mutant analysis suggested that ZmNRAMP6 conferred plant sensitivity to Pb stress by mediating Pb distribution across the roots and shoots. Knockout of ZmNRAMP6 caused Pb retention in the roots and activation of the antioxidant enzyme system, resulting in an increased Pb tolerance in maize. ZmNRAMP6 was likely to transport Pb from the roots to shoots and environment. An integration of yeast one-hybrid and dual-luciferase reporter assay uncovered that ZmNRAMP6 was negatively regulated by a known Pb tolerance-related transcript factor ZmbZIP54. Collectively, knockout of ZmNRAMP6 will aid in the bioremediation of contaminated soil and food safety guarantee of forage and grain corn.

Profiling of N6-methyladenosine methylation in porcine longissimus dorsi muscle and unravelling the hub gene ADIPOQ promotes adipogenesis in an m6A-YTHDF1-dependent manner

BACKGROUND

Intramuscular fat (IMF) content is a critical indicator of pork quality, and abnormal IMF is also relevant to human disease as well as aging. Although N6-methyladenosine (m⁶A) RNA modification was recently found to regulate adipogenesis in porcine intramuscular fat, however, the underlying molecular mechanisms was still unclear.

RESULTS

In this work, we collected 20 longissimus dorsi muscle samples with high (average 3.95%) or low IMF content (average 1.22%) from a unique heterogenous swine population for m⁶A sequencing (m⁶A-seq). We discovered 70 genes show both differential RNA expression and m⁶A modification from high and low IMF group, including ADIPOQ and SFRP1, two hub genes inferred through gene co-expression analysis. Particularly, we observed ADIPOQ, which contains three m⁶A modification sites within 3' untranslated and protein coding region, could promote porcine intramuscular preadipocyte differentiation in an m⁶A-dependent manner. Furthermore, we found the YT521‑B homology domain family protein 1 (YTHDF1) could target and promote ADIPOQ mRNA translation.

CONCLUSIONS

Our study provided a comprehensive profiling of m⁶A methylation in porcine longissimus dorsi muscle and characterized the involvement of m⁶A epigenetic modification in the regulation of ADIPOQ mRNA on IMF deposition through an m⁶A-YTHDF1-dependent manner.

GBP5 exacerbates rosacea-like skin inflammation by skewing macrophage polarization towards M1 phenotype through the NF-κB signalling pathway

BACKGROUND

Rosacea is a chronic inflammatory skin disease with increased macrophage infiltration. However, the molecular mechanism remains unclear.

OBJECTIVES

To determine the significance of macrophage infiltration, and the correlation between Guanylate-binding protein 5 (GBP5) and polarization of macrophages in rosacea-like inflammation.

METHODS

Here we tested the hypothesis that Guanylate-binding protein 5 (GBP5) aggravates rosacea-like skin inflammation by promoting the polarization of the M1 macrophages through the NF-κB signalling pathway. We depleted macrophage by injecting clodronate-containing liposomes. We next explored the association between GBP5 and macrophage in rosacea tissue through transcriptome analysis and immunofluorescence analysis. We evaluated the severity of rosacea-like skin inflammation when BALB/c mice were injected with GBP5 siRNA intradermally daily for three consecutive days. At last, to study the causality of knocking down GBP5-blunted M1 macrophage polarization, THP-1 cell was treated with GBP5 siRNA.

RESULTS

Macrophage depletion ameliorated rosacea-like skin inflammation in mice, implying the important role of macrophages in rosacea. Based on the transcriptome analysis, Guanylate-binding protein 5 (GBP5) was identified as hub gene that was associated with macrophage infiltration in rosacea. Next, we found that GBP5 expression was significantly upregulated in rosacea tissues and positively correlated with macrophage infiltration, the immunofluorescence analysis revealed the co-localization between GBP5 and macrophages. In vivo, silencing of GBP5 attenuated rosacea-like skin inflammation in the LL-37-induced mouse model and suppressed the expression of M1 signature genes such as IL-6, iNOS and TNF-a. In vitro, knocking down GBP5 significantly blunted the polarization of the M1 macrophages partly by repressing the activation of the NF-κB signalling pathways.

CONCLUSIONS

Together, our study revealed the important role of macrophages in rosacea and identified GBP5 as a key regulator of rosacea by inducing M1 macrophage polarization via NF-κB signalling pathways.

Characteristic Analysis of Featured Genes Associated with Cholangiocarcinoma Progression

The noninvasive diagnosis of cholangiocarcinoma (CCA) is insufficiently accurate. Therefore, the discovery of new prognostic markers is vital for the understanding of the CCA mechanism and related treatment. The information on CCA patients in The Cancer Genome Atlas database was used for weighted gene co-expression network analysis. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to analyze the modules of interest. By using receiver operating characteristic (ROC) analysis to analyze the Human Protein Atlas (HPA), the featured genes were subsequently verified. In addition, clinical samples and GSE119336 cohort data were also collected for the validation of these hub genes. Using WGCNA, we identified 61 hub genes that regulated the progression and prognosis of CCA. Eight hub genes (VSNL1, TH, PCP4, IGDCC3, RAD51AP2, MUC2, BUB1, and BUB1B) were identified which exhibited significant interactions with the tumorigenic mechanism and prognosis of CCA. In addition, GO and KEGG clarified that the blue and magenta modules were involved with chromosome segregation, mitotic and oocyte meiosis, the cell cycle, and sister chromatid segregation. Four hub genes (VSNL1, PCP4, BUB1, and BUB1B) were also verified as featured genes of progression and prognosis by the GSE119336 cohort data and five human tissue samples.

Mir-421 and mir-550a-1 are potential prognostic markers in esophageal adenocarcinoma

OBJECTIVE

To identify the prognostic indicators of esophageal adenocarcinoma (EAC) for future EAC diagnosis and treatment.

METHODS

The EAC dataset from The Cancer Genome Atlas was screened for differentially expressed microRNAs (miRNAs) and mRNAs associated with EAC. Weighted gene coexpression network analysis was performed to cluster miRNAs or mRNA with similar expression patterns to identify the miRNAs or mRNA that are highly associated with EAC. Prognostic miRNAs for overall survival (OS) were identified using Cox proportional-hazards regression analysis and least absolute shrinkage and selection operator based on survival duration and status. Two types of miRNAs were selected to develop a prognostic signature model for EAC using multiple Cox regression analysis. Furthermore, the signature was validated using internal validation sets 1 and 2. The receiver operating characteristic curve and concordance index were used to evaluate the accuracy of the signature and validation sets. The expression of miR-421, miR-550a-3p, and miR-550a-5p was assessed using quantitative polymerase chain reaction (qPCR). The proliferation, invasion, and migration of EAC cells were assessed using CCK8 and transwell assays. The OS of target mRNAs was assessed using Kaplan-Meier analysis. Functional enrichment analysis of the target mRNAs was performed using Metascape.

RESULTS

The prognostic signature and validation sets comprising mir-421 and mir-550a-1 had favorable predictive power in OS. Compared with the patients with EAC in the high-expression group, those assigned to the low-expression group displayed increased OS according to survival analysis. Differential and qPCR analysis showed that miR-421, miR-550a-3p, and miR-550a-5p were highly expressed in the EAC tissues and cell lines. Moreover, the downregulation of miR-421 and miR-550a-3p with inhibitor markedly suppressed the proliferation, invasion, and migration in OE33 cells compared with the negative control. A total of 20 target mRNAs of three miRNAs were predicted, among which seven target mRNAs-ASAP3, BCL2L2, LMF1, PPM1L, PTPN21, SLC18A2, and NR3C2-had prognostic value; PRKACB, PDCD4, RPS6KA5, and BCL2L2 were enriched in the miRNA cancer pathway.

CONCLUSION

Prognostic indicators of EAC may be useful in future EAC diagnosis and treatment.

LGALS1 was related to the prognosis of clear cell renal cell carcinoma identified by weighted correlation gene network analysis combined with differential gene expression analysis

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We screened out 397 overlapping genes from the four sets of results, and then performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. In addition, the protein-protein interaction (PPI) network of 397 overlapping genes was mapped using the STRING database. We identified ten hub genes (KNG1, TIMP1, ALB, C3, GPC3, VCAN, P4HB, CHGB, LGALS1, EGF) using the CytoHubba plugin of Cytoscape based on the Maximal Clique Centrality (MCC) score. According to Kaplan-Meier survival analysis, higher expression of LGALS1 and TIMP1 was related to poorer overall survival (OS) in patients with ccRCC. Univariate and multivariate Cox proportional hazard analysis showed that the expression of LGALS1 was an independent risk factor for poor prognosis. Moreover, the higher the clinical grade and stage of ccRCC, the higher the expression of LGALS1. LGALS1 may play an important role in developing ccRCC and may be potential a biomarker for prognosis and treatment targets.

1H NMR fecal metabolic phenotyping of periductal fibrosis- and cholangiocarcinoma-specific metabotypes defining perturbation in gut microbial-host co-metabolism

Background

The liver fluke Opisthorchis viverrini (OV), which subsequently inhabits the biliary system and results in periductal fibrosis (PDF), is one of the primarily causes of cholangiocarcinoma (CCA), a bile duct cancer with an exceptionally high incidence in the northeast of Thailand and other Greater Mekong Subregion (GMS) countries. Insights in fecal metabolic changes associated with PDF and CCA are required for further molecular research related to gut health and potential diagnostic biological marker development.

Methods

In this study, nuclear magnetic resonance (NMR) metabolomics was applied for fecal metabolic phenotyping from 55 fecal water samples across different study groups including normal bile duct, PDF and CCA groups.

Results

By using NMR spectroscopy-based metabolomics, fecal metabolic profiles of patients with CCA or PDF and of individuals with normal bile duct have been established with a total of 40 identified metabolites. Further multivariate statistical analysis and hierarchical clustering heat map have demonstrated the PDF- and CCA-specific metabotypes through various altered metabolite groups including amino acids, alcohols, amines, anaerobic glycolytic metabolites, fatty acids, microbial metabolites, sugar, TCA cycle intermediates, tryptophan catabolism substrates, and pyrimidine metabolites. Compared to the normal bile duct group, PDF individuals showed the significantly elevated relative concentrations of fecal ethanol, glycine, tyrosine, and N-acetylglucosamine whereas CCA patients exhibited the remarkable fecal metabolic changes that can be evident through the increased relative concentrations of fecal uracil, succinate, and 5-aminopentanoate. The prominent fecal metabolic alterations between CCA and PDF were displayed by the reduction of relative concentration of methanol observed in CCA. The metabolic alterations associated with PDF and CCA progression have been proposed with the involvement of various metabolic pathways including TCA cycle, ethanol biogenesis, hexamine pathway, methanol biogenesis, pyrimidine metabolism, and lysine metabolism. Among them, ethanol, methanol, and lysine metabolism strongly reflect the association of gut-microbial host metabolic crosstalk in PDF and/or CCA patients.

Conclusion

The PDF- and CCA-associated metabotypes have been investigated displaying their distinct fecal metabolic patterns compared to that of normal bile duct group. Our study also demonstrated that the perturbation in co-metabolism of host and gut bacteria has been involved from the early step since OV infection to CCA tumorigenesis.

Serum ferritin diagnosis and prediction of hepatitis B virus-related acute-on-chronic liver failure

Early diagnosis and prediction of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important to reduce mortality. This study aimed to assess the diagnostic and predictive value of serum ferritin (SF) in HBV-ACLF patients. Clinical data from 1905 hospitalized patients with acute deterioration of HBV-related chronic liver diseases were analyzed to explore the association between SF and ACLF. A co-expression network based on transcriptomics data for 20 HBV-ACLF patients was constructed to investigate biological processes related to ferritin. Of 1270 patients in the derivation group, 440 and 830 were diagnosed with and without ACLF, respectively, based on Chinese Group on the Study of Severe Hepatitis B-ACLF criteria. SF levels showed high diagnostic accuracy (area under the receiver operating characteristic [AUROC]: 0.820) for ACLF at admission. In patients with ACLF, SF was associated with liver and coagulation failure. In patients without ACLF, SF predicted risk for 28-day progression to ACLF (AUROC: 0.808). A validation group of 635 patients confirmed the above results. Moreover, SF was significantly associated with the immune response based on transcriptomics analysis. SF is a potential diagnostic and predictive marker for HBV-ACLF and might play a crucial role in immune disorders in HBV-ACLF.

Identification of therapeutic targets and prognostic biomarkers in cholangiocarcinoma via WGCNA

Background

Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor for which limited treatment methods and prognostic signatures are available. This study aims to identify potential therapeutic targets and prognostic biomarkers for CCA.

Methods

Based on differentially expressed genes (DEGs) identified from The Cancer Genome Atlas (TCGA) data, our study identified key gene modules correlated with CCA patient survival by weighted gene coexpression network analysis (WGCNA). Cox regression analysis identified survival-related genes in the key gene modules. The biological properties of the survival-related genes were evaluated by CCK-8 and transwell assays. Then, these genes were used to construct a prognostic signature that was internally and externally validated. Additionally, by combining clinical characteristics with the gene-based prognostic signature, a nomogram for survival prediction was built.

Results

WGCNA divided the 1531 DEGs into four gene modules, and the yellow gene module was significantly associated with overall survival (OS) and histologic neoplasm grade. Our study identified the lncRNA AGAP2-AS1 and a novel gene, GOLGA7B, that are closely related to survival. GOLGA7B downregulation promoted the invasion, migration and proliferation of CCA cells, but AGAP2-AS1 had the opposite effect. AGAP2-AS1 and GOLGA7B were integrated into a gene-based prognostic signature, and both internal and external validation studies confirmed that this two-gene prognostic signature and nomogram could accurately predict CCA patient prognosis.

Conclusion

AGAP2-AS1 and GOLGA7B are potential therapeutic targets and prognostic biomarkers for CCA.

Weighted gene co-expression network reveals driver genes contributing to phenotypes of anaplastic thyroid carcinoma and immune checkpoint identification for therapeutic targets

Background

Anaplastic thyroid carcinoma (ATC) is a rare but extremely malignant tumor, with a rapid growth rate and early metastasis thus leading to poor survival of patients. The molecular mechanisms underlying these aggressive traits of ATC remain unknown, which impedes the substantial progress in treatment to prolong ATC patient survival.

Methods

We applied weighted gene co-expression network analysis (WGCNA) to identify ATC-specific modules. The Metascape web and R package clusterProfiler were employed to perform enrichment analysis. Combined with differentially expressed gene analysis, we screened out the most potential driver genes and validated them using receiver operator characteristic (ROC) analysis, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting, immunohistochemistry (IHC), and triple immunofluorescence staining.

Results

A gene expression matrix covering 75 normal samples, 83 papillary thyroid carcinoma (PTC), 26 follicular thyroid carcinoma (FTC), 19 poor-differentiated thyroid carcinoma (PDTC), and 41 ATC tissue samples were integrated, based on which we detected three most potential ATC-specific modules and found that hub genes of these modules were enriched in distinct biological signals. Hub genes in the turquoise module were mainly enriched in mitotic cell cycle, tube morphogenesis, and cell differentiation, hub genes in the magenta module were mainly clustered in the extracellular matrix organization, positive regulation of cell motility, and regulation of Wnt signaling pathway, while hub genes in the blue module primarily participated in the inflammatory response, innate immune response, and adaptive immune response. We showed that 9 top genes, 8 transcription factors (TFs), and 4 immune checkpoint genes (ICGs) were differentially expressed in ATC compared to other thyroid samples and had high diagnostic values for ATC, among which, 9 novel ATC-specific genes (ADAM12, RNASE2, CASP5, KIAA1524, E2F7, MYBL1, SRPX2, HAVCR2, and TDO2) were validated with our clinical samples. Furthermore, we illustrated that ADAM12, RNASE2, and HAVCR2 were predominantly present in the cytoplasm.

Conclusion

Our study identified a set of novel ATC-specific genes that were mainly related to cell proliferation, invasion, metastasis, and immunosuppression, which might throw light on molecular mechanisms underlying aggressive phenotypes of ATC and provide promisingly diagnostic biomarkers and therapeutic targets.

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman's correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

A comprehensive weighted gene co-expression network analysis uncovers potential targets in diabetic kidney disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman’s correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Identification and exploration of novel M2 macrophage-related biomarkers in the development of acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, is a major cause of morbidity and mortality worldwide. Macrophages play a critical role in ventricular remodeling after AMI. The regulatory mechanisms of the AMI progression remain unclear. This study aimed to identify hub regulators of macrophage-related modules and provide translational experiments with potential therapeutic targets.

MATERIALS AND METHODS

The GSE59867 dataset was downloaded from the Gene Expression Omnibus (GEO) database for bioinformatics analysis. The expression patterns of 22 types of immune cells were determined using CIBERSORT. GEO2R was used to identify differentially expressed genes (DEGs) through the limma package. Then, DEGs were clustered into different modules, and relationships between modules and macrophage types were analyzed using weighted gene correlation network analysis (WGCNA). Further functional enrichment analysis was performed using significantly associated modules. The module most significantly associated with M2 macrophages (Mϕ2) was chosen for subsequent analysis. Co-expressed DEGs of AMI were identified in the GSE123342 and GSE97320 datasets and module candidate hub genes. Additionally, hub gene identification was performed in GSE62646 dataset and clinical samples.

RESULTS

A total of 8,760 DEGs were identified and clustered into ten modules using WGCNA analysis. The blue and turquoise modules were significantly related to Mϕ2, and 482 hub genes were discerned from two hub modules that conformed to module membership values > 0.8 and gene significance values > 0.25. Subsequent analysis using a Venn diagram assessed 631 DEGs in GSE123342, 1457 DEGs in GSE97320, and module candidate hub genes for their relationship with Mϕ2 in the progression of AMI. Finally, four hub genes (CSF2RB, colony stimulating factor 2 receptor subunit beta; SIGLEC9, sialic acid-binding immunoglobulin-like lectin 9; LRRC25, leucine-rich repeat containing 25; and CSF3R, colony-stimulating factor-3 receptor) were validated to be differentially expressed and to have high diagnostic value in both GSE62646 and clinical samples.

CONCLUSION

Using comprehensive bioinformatics analysis, we identified four novel genes that may play crucial roles in the pathophysiological mechanism of AMI. This study provides novel insights into the impact of macrophages on the progression of AMI and directions for Mϕ2-targeted molecular therapies for AMI.

Identification of polyunsaturated fatty acids related key modules and genes in metabolic dysfunction-associated fatty liver disease using WGCNA analysis

Polyunsaturated fatty acids (PUFAs) play important roles in the aetiology and pathogenesis of metabolic dysfunction-associated fatty liver disease (MAFLD). However, the underlying molecular mechanisms are not understood. We analysed a public GEO dataset, GSE89632, to identify differentially expressed genes (DEGs) in MAFLD. Weighted gene coexpression network analysis (WGCNA) was used to reveal the core gene regulation network and to explore the PUFA-related hub genes in MAFLD. We experimentally verified these genes by quantitative reverse transcription PCR in high-fat diet (HFD)-fed mice. A total of 286 common DEGs (89 upregulated; 197 downregulated), mostly related to inflammatory and immune responses, were identified. Six modules were constructed using WGCNA, and 2 modules showed significant correlations with PUFAs. After combining these 2 modules with DEGs, the top 10 hub genes were identified. We further established a MAFLD mouse model with liver steatosis, as proved by HE and Oil Red O staining. Of the hub genes, ADAM metallopeptidase with thrombospondin type 1 motif 1 (adamts1) (p = 0.005) and transforming growth factor β3 (tgfβ3) (p < 0.001) showed significantly lower mRNA expression in MAFLD in vivo. adamts1 and tgfβ3 bridged PUFAs and MAFLD, which might be potential causative genes and therapeutic targets of MAFLD.

Maternal NO2 exposure disturbs the long noncoding RNA expression profile in the lungs of offspring in time-series patterns

Gestation is a sensitive window to nitrogen dioxide (NO₂) exposure, which may disturb fetal lung development and lung function later in life. Animal and epidemiological studies indicated that long noncoding RNAs (lncRNAs) participate in abnormal lung development induced by environmental pollutant exposure. In the present study, pregnant C57BL/6J mice were exposed to 2.5 ppm NO₂ (mimicking indoor occupational exposure) or clean air, and lncRNAs expression profiles in the lungs of offspring mice were determined by lncRNA-seq on embryonic day 13.5 (E13.5), E18.5, postnatal day 1 (P1), and P14. The lung histopathology examination of offspring was performed, followed by weighted gene coexpression network analysis (WGCNA), prediction of lncRNAs-target genes, and the biological processes enrichment analysis of lncRNAs. Our results indicated that maternal NO₂ exposure induced hypoalveolarization on P14 and differentially expressed lncRNAs showed a time-series pattern. Following WGCNA and enrichment analysis, 2 modules participated in development-related pathways. Importantly, the expressions of related genes were altered, some of which were confirmed to be related to abnormal vascular development and even lung diseases. The research points out that the maternal NO₂ exposure leads to abnormal lung development in offspring that might be related to altered lncRNAs expression profiles with time-series-pattern.

A metabolic reprogramming-related prognostic risk model for clear cell renal cell carcinoma: From construction to preliminary application

Metabolic reprogramming is one of the characteristics of clear cell renal cell carcinoma (ccRCC). Although some treatments associated with the metabolic reprogramming for ccRCC have been identified, remain still lacking. In this study, we identified the differentially expressed genes (DEGs) associated with clinical traits with a total of 965 samples via DEG analysis and weighted correlation network analysis (WGCNA), screened the prognostic metabolism-related genes, and constructed the risk score prognostic models. We took the intersection of DEGs with significant difference coexpression modules and received two groups of intersection genes that were connected with metabolism via functional enrichment analysis. Then we respectively screened prognostic metabolic-related genes from the genes of the two intersection groups and constructed the risk score prognostic models. Compared with the predicted effect of clinical grade and stage for ccRCC patients, finally, we selected the model constructed with genes of ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, and FBP1. The risk scores of the prognostic model were significantly related to overall survival (OS) and could serve as an independent prognostic factor. The Kaplan-Meier analysis and ROC curves revealed that the model efficiently predicts prognosis in the TCGA-KIRC cohort and the validation cohort. Then we investigated the potential underlying mechanism and sensitive drugs between high- and low-risk groups. The six key genes were significantly linked with worse OS and were downregulated in ccRCC, we confirmed the results in clinical samples. These results demonstrated the efficacy and robustness of the risk score prognostic model, based on the characteristics of metabolic reprogramming in ccRCC, and the key genes used in constructing the model also could develop into targets of molecular therapy for ccRCC.

Identification of MEDAG and SERPINE1 Related to Hypoxia in Abdominal Aortic Aneurysm Based on Weighted Gene Coexpression Network Analysis

Purpose: Abdominal aortic aneurysm (AAA) is a severe cardiovascular disease that often results in high mortality due to sudden rupture. This paper aims to explore potential molecular mechanisms and effective targeted therapies to prevent and delay AAA rupture. Methods: We downloaded two microarray datasets (GSE98278 and GSE17901) from the Gene Expression Omnibus (GEO) database. Differential analysis and single-sample gene set enrichment analysis (ssGSEA) of hypoxia scores were performed on 48 AAA patients in GSE98278. We identified hypoxia- and ruptured AAA-related gene modules using weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package clusterProfiler. For candidate genes, validation was conducted on the mouse dataset GSE17901. Finally, we predicted drug candidates associated with the hub genes using the HERB Chinese medicine database. Results: Eighty-two differentially expressed genes were screened in the ruptured and stable groups; 103 differentially expressed genes were identified between the high- and low-hypoxia groups; and WGCNA identified 58 differentially expressed genes. Finally, nine candidate genes were screened, including two hub genes (MEDAG and SERPINE1). We identified pathways such as cytokine-cytokine receptor interaction and T-helper 1-type immune response involved in AAA hypoxia and rupture. We predicted 93 traditional Chinese medicines (TCMs) associated with MEDAG and SERPINE1. Conclusion: We identified the hypoxic molecules MEDAG and SERPINE1 associated with AAA rupture. Our study provides an additional direction for the association between hypoxia and AAA rupture.

Comprehensive Network-Based Analyses Reveal Novel Renal Function-Related Targets in Acute Kidney Injury

Background: Acute kidney injury (AKI) is a common clinical syndrome with limited methods of treatment and diagnosis. Although several molecules associated with AKI have been discovered, molecular mechanisms underlying AKI still remain unclear. Weighted gene co-expression network analysis (WGCNA) is a novel method to uncover the relationship between co-expression genes and clinical traits at the system level.

Methods: First, by employing WGCNA in transcriptional data on 30 patients with well/poor functioning kidney graft, we identified two co-expression modules that were significantly related to serum creatinine (SCr). Second, based on the modules, potential small molecular compound candidates for developing targeted therapeutics were obtained by connectivity map analysis. Furthermore, multiple validations of expression in space/time were carried out with two classical AKI models in vivo and other five databases of over 152 samples.

Results: Two of the 14 modules were found to be closely correlated with SCr. Function enrichment analysis illustrated that one module was enriched in the immune system, while the other was in the metabolic process. Six key renal function-related genes (RFRGs) were finally obtained. Such genes performed well in cisplatin-induced or cecal ligation and puncture-induced AKI mouse models.

Conclusion: The analysis suggests that WGCNA is a proper method to connect clinical traits with genome data to find novel targets in AKI. The kidney tissue with worse renal function tended to develop a “high immune but low metabolic activity” expression pattern. Also, ACSM2A, GLYAT, CORO1A, DPEP1, ALDH7A1, and EPHX2 are potential targets of molecular diagnosis and treatment in AKI.

High-fat diet aggravates prenatal low-dose DEHP exposure induced spermatogenesis disorder: Characterization of testicular metabolic patterns in mouse offspring

Di-(2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer and has been identified as a male prenatal reproductive toxicant. A high fat diet (HFD) has also been suggested as another potential disruptor of male reproductive function. Despite this potential synergism between DEHP exposure and HFD, little is known about the concomitant effects of prenatal DEHP and a subsequent HFD exposure on male offspring reproductive injury. Here we established a mouse model of prenatal exposure to DEHP (0.2 mg/kg/day) to assess the testicular development and spermatogenesis in offspring subjected to obesogenic diet during the pubertal period. Gross phenotype, hormone profiles and the testicular metabolome were analyzed to determine the underlying mechanism. We found that prenatal exposure to low-dose DEHP resulted in decreased sperm density, decreased testosterone (T) levels, increased luteinizing hormone (LH) levels and testicular germ cell apoptosis. Furthermore, these injury phenotypes were aggravated by pubertal HFD treatment. Testicular riboflavin and biotin metabolites were enriched implying their roles in contributing HFD to exacerbate offspring spermatogenesis disorders due to prenatal low-dose DEHP exposure. Our findings suggest that pubertal HFD exacerbates reproductive dysfunction associated with prenatal exposure to low-dose DEHP in male adult offspring.

Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome

Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.

Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms

Molecular signatures have been excessively reported for diagnosis of many cancers during the last 20 years. However, false-positive signatures are always found using statistical methods or machine learning approaches, and that makes subsequent biological experiments fail. Therefore, signature discovery has gradually become a non-mainstream work in bioinformatics. Actually, there are three critical weaknesses that make the identified signature unreliable. First of all, a signature is wrongly thought to be a gene set, each component of which keeps differential expressions between or among sample groups. Second, there may be many false-positive genes expressed differentially found, even if samples derived from cancer or normal group can be separated in one-dimensional space. Third, cross-platform validation results of a discovered signature are always poor. In order to solve these problems, we propose a new feature selection framework based on ensemble classification to discover signatures for cancer diagnosis. Meanwhile, a procedure for data transform among different expression profiles across different platforms is also designed. Signatures are found on simulation and real data representing different carcinomas across different platforms. Besides, false positives are suppressed. The experimental results demonstrate the effectiveness of our method.

Upregulated of ANXA3, SORL1, and Neutrophils May Be Key Factors in the Progressionof Ankylosing Spondylitis

Introduction

The specific pathogenesis of ankylosing spondylitis (AS) remains unclear, and our study aimed to investigate the possible pathogenesis of AS.

Materials and Methods

Two datasets were downloaded from the GEO database to perform differentially expressed gene analysis, GO enrichment analysis, KEGG pathway analysis, DO enrichment analysis, GSEA analysis of differentially expressed genes, and construction of diagnostic genes using SVM and WGCNA along with Hypoxia-related genes. Also, drug sensitivity analysis was performed on diagnostic genes. To identify the differentially expressed immune genes in the AS and control groups, we analyzed the composition of immune cells between them. Then, we examined differentially expressed genes in three AS interspinous ligament specimens and three Degenerative lumbar spine specimens using high-throughput sequencing while the immune cells were examined using the neutrophil count data from routine blood tests of 1770 HLA-B27-positive samples and 7939 HLA-B27-negative samples. To assess the relationship between ANXA3 and SORL1 and disease activity, we took the neutrophil counts of the first 50 patients with above-average BASDAI scores and the last 50 patients with below-average BASDAI scores for statistical analysis. We used immunohistochemistry to verify the expression of ANXA3 and SORL1 in AS and in controls.

Results

ANXA3 and SORL1 were identified as new diagnostic genes for AS. These two genes showed a significant differential expression between AS and controls, along with showing a significant positive correlation with the neutrophil count. The results of high-throughput sequencing verified that these two gene deletions were indeed differentially expressed in AS versus controls. Data from a total of 9707 routine blood tests showed that the neutrophil count was significantly higher in AS patients than in controls (p < 0.001). Patients with AS with a high BASDAI score had a much higher neutrophil count than those with a low score, and the difference was statistically significant (p < 0.001). The results of immunohistochemistry showed that the expression of ANXA3 and SORL1 in AS was significantly higher than that in the control group.

Conclusion

Upregulated of ANXA3, SORL1, and neutrophils may be a key factor in the progression of Ankylosing spondylitis.

CHST4 might promote the malignancy of cholangiocarcinoma

Background

Cholangiocarcinoma (CCA) is reported as an aggressive cancer which leads to high mortality and no effective therapeutic target has yet been discovered. Surgical resection is the main method to treat patients with CCA. However, only one-third of CCA patients have the opportunity to accept the operation, leading to poor prognosis for CCA patients. Therefore, it is necessary to search for new therapeutic targets of CCA or core genes involved in the happening and growth of CCA.

Aim

In this study, we utilized bioinformatics technology and accessed to several medical databases trying to find the core genes of CCA for the purpose of intervening CCA through figuring out an effective curative target.

Methods

Firstly, three differentially expressed genes (DEGs) were discovered from GEPIA, and by further observing the distribution and gene expression, CHST4 was obtained as the core gene. Afterwards, correlated genes of CHST4 in CCA were identified using UALCAN to construct a gene expression profile. We obtained PPI network by Search Tool for the Retrieval of Interacting Networks Genes (STRING) and screened core genes using cytoscape software. Functional enrichment analyses were carried out and the expression of CHST in human tissues and tumors was observed. Finally, a CCA model was established for qPCR and staining validation.

Results

Three differentially expressed genes (DEGs), CHST4, MBOAT4 and RP11-525K10.3, were obtained. All were more over-expressed in CCA samples than the normal, among which the change multiple and the gene expression difference of CHST4 was the most obvious. Therefore, CHST4 was selected as the core gene. We can see in our established protein–protein interaction (PPI) network that CHST4 had the highest degree of connectivity, demonstrating its close association with CCA. We found that genes were mainly enriched in CCs in the PPI networks genes which shows functional enrichment analysis results, including golgi lumen, extracellular space and extracellular region. CHST4 was found very specifically expressed in the bile duct and was significantly different from that in normal tissues. The overexpression of CHST4 was further verified in the established animal model of TAA-induced CCA in rats. Quantitative PCR (qPCR) demonstrated that CHST4 was significantly overexpressed in tumor tissues, verifying the role of CHST4 as the core gene of CCA.

Conclusion

CHST4 was increasingly expressed in CCA and CHST4 is worth being studied much further in the intervention of CCA.

Multi-Transcriptomic Analysis and Experimental Validation Implicate a Central Role of STAT3 in Skin Barrier Dysfunction Induced Aggravation of Rosacea

Objective

Methods

Results

Conclusion

Construction of a Redox-Related Prognostic Model with Predictive Value in Survival and Therapeutic Response for Patients with Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) represents the most common histological subtype of lung cancer. Redox plays a significant role in oncogenesis and antitumor immunity. In this study, we aimed to investigate the prognostic redox-associated genes and construct a redox-based prognostic signature for LUAD.

Materials and Methods

A discovery cohort containing 479 LUAD samples from The Cancer Genome Atlas (TCGA) was analyzed. We identified prognostic redox-associated genes by weighted correlation network analysis (WGCNA) and univariate Cox regression analysis to construct a prognostic model via least absolute shrinkage and selection operator (LASSO)-multivariate Cox regression analyses. The performance of the redox-based model was validated in the TCGA cohort and an independent cohort of 456 samples by Cox regression analyses, log-rank test, and receiver operating characteristic (ROC) curves. Correlations of the model with clinicopathological variables and lymphocyte infiltration were assessed. Gene set enrichment analysis (GSEA) was used to clarify the underlying mechanism of the prognostic model. We constructed a nomogram based on the model and created calibration curves to show the accordance between actual survival and predicted survival of the nomogram.

Results

Stepwise analyses identified 6 prognostic redox-associated genes of LUAD and constructed a prognostic model that performed well in both the discovery and validation cohorts. The model was found to be associated with tumor stage, mutation of TP53 and EGFR, and lymphocyte infiltration. The model was mainly involved in the regulation of the cell cycle, DNA replication and repair, NADH metabolism, and the p53 signaling pathway. Calibration curves showed the high predictive accuracy of the nomogram.

Conclusions

This study explored the role of redox-associated genes in LUAD and constructed a prognostic model of LUAD. The signature was also associated with tumor progression and therapeutic response to immunotherapy. These findings contributed to uncovering the underlying mechanism and discovering novel prognostic predictor of LUAD.

Identification of Differentially Expressed and Prognostic lncRNAs for the Construction of ceRNA Networks in Lung Adenocarcinoma

Background

Long noncoding RNAs (lncRNAs) could function as competitive endogenous RNAs (ceRNAs) to competitively adsorb microRNAs (miRNAs), thereby regulating the expression of their target protein-coding mRNAs. In this study, we aim to identify more effective diagnostic and prognostic markers for lung adenocarcinoma (LUAD).

Methods

We obtained differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) for LUAD by using The Cancer Genomes Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) was performed to unveil core gene modules associated with LUAD. The Cox proportional hazards model was performed to determine the prognostic significance of DElncRNAs. The diagnostic and prognostic significance of DElncRNAs was further verified based on the receiver operating characteristic curve (ROC). Cytoscape was used to construct the ceRNA networks comprising the lncRNAs-miRNAs-mRNAs axis based on the correlation obtained from the miRcode, miRDB, and TargetScan.

Results

Compared with normal lung tissues, 2355 DElncRNAs, 820 DEmiRNAs, and 17289 DEmRNAs were identified in LUAD tissues. We generated 8 WGCNA core modules in the lncRNAs coexpression network, 5 modules in the miRNAs, and 12 modules in the mRNAs coexpression network, respectively. One lncRNA module (blue) consisting of 441 lncRNAs, two miRNA modules (blue and turquoise) containing 563 miRNAs, and one mRNA module (turquoise), which consisted of 15162 mRNAs, were mostly significantly related to LUAD status. Furthermore, 67 DEmRNAs were found to be tumor-associated as well as the target genes of the DElncRNAs-DEmiRNAs axis. Survival analyses showed that 6 lncRNAs (LINC01447, WWC2-AS2, OGFRP1, LINC00942, LINC01168, and AC005863.1) were significantly correlated with the prognosis of LUAD patients. Ultimately, the potential ceRNA networks including 6 DElncRNAs, 4 DEmiRNAs, and 22 DEmRNAs were constructed.

Conclusion

Our study indicated that 6 DElncRNAs had the possibilities as diagnostic and prognostic biomarkers for LUAD. The lncRNA-mediated ceRNA networks might provide novel insights into the molecular mechanisms of LUAD progression.

Integrative analysis of the molecular mechanisms, immunological features and immunotherapy response of ferroptosis regulators across 33 cancer types

Ferroptosis is a recently described mode of cell death caused by the accumulation of intracellular iron and lipid reactive oxygen species (ROS), which play critical roles in tumorigenesis and cancer progression. However, the underlying molecular mechanisms and promising biomarkers of ferroptosis among cancers remain to be elucidated. In this study, 30 ferroptosis regulators in ferroptosis-related signaling pathways were identified and analyzed in 33 cancer types. We found transcriptomic aberrations and evaluated the prognostic value of ferroptosis regulators across 33 cancer types. Then, we predicted and validated potential transcription factors (including E2F7, KLF5 and FOXM1) and therapeutic drugs (such as cyclophosphamide, vinblastine, and gefitinib) that target ferroptosis regulators in cancer. Moreover, we explored the molecular mechanisms of ferroptosis and found that signaling pathways such as the IL-1 and IL-2 pathways are closely associated with ferroptosis. Additionally, we found that ferroptosis regulators have a close relationship with immunity-related parameters, including the immune score, immune cell infiltration level, and immune checkpoint protein level. Finally, we determined a ferroptosis score using GSVA method. We found that the ferroptosis score effectively predicted ferroptotic cell death in tumor samples. And ferroptosis score is served as an independent prognostic indicator for the incidence and recurrence of cancers. More importantly, patients with high ferroptosis scores received greater benefit from immunotherapy. We aslo created an online webserver based on the nomogram prognostic model to predict the survival in immunotherapy cohort. The reason for this outcome is partially the result of patients with a high ferroptosis rate also having high immune scores, HLA-related gene expression and immune checkpoint protein expression, such as PDL2 and TIM3. Moreover, patients with high ferroptosis scores exhibited CD8 T cell and TIL infiltration and immune-related signaling pathway enrichment. In summary, we systematically summarize the molecular characteristics, clinical relevance and immune features of ferroptosis across cancers and show that the ferroptosis score can be used as a prognostic factor and for the evaluation of immunotherapy effects.

A network pharmacology approach to reveal the pharmacological targets and biological mechanism of compound kushen injection for treating pancreatic cancer based on WGCNA and in vitro experiment validation

BACKGROUND

Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated.

METHODS

Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis.

RESULTS

The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis.

CONCLUSIONS

Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI.

Identification and Validation of IFI44 as Key Biomarker in Lupus Nephritis

Lupus nephritis (LN) is a common and severe organ manifestation of systemic lupus erythematosus (SLE) and is a major cause of SLE related deaths. Early diagnosis is essential to improve the prognosis of patients with LN. To screen the potential biomarkers associated with LN, we downloaded the gene expression profile of GSE99967 from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to construct a gene co-expression network and identify gene modules associated with LN. Gene Ontology (GO) analysis was also applied to explore the biological function of genes and identify the key module. Differentially expressed genes (DEGs) were identified and Maximal Clique Centrality (MCC) values were calculated to screen hub genes. Furthermore, we selected promising biomarkers for real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) validation in independent cohorts. Our results indicated that five hub genes, including IFI44, IFIT3, HERC5, RSAD2, and DDX60 play vital roles in the pathogenesis of LN. Importantly, IFI44 may considered as a key biomarker in LN for its diagnostic capabilities, which is also a promising therapeutic target in the future.

Tumor Purity Coexpressed Genes Related to Immune Microenvironment and Clinical Outcomes of Lung Adenocarcinoma

Purpose

Lung cancer tissue includes tumor tissue, stromal cells, immune cells, and epithelial cells. These nontumor cells dilute the tumor purity in lung cancer tissues. Tumor purity plays an essential role in the immune response to lung cancer. At present, the biological processes related to the purity of lung cancer tumors remains unclear.

Methods

We measured tumor purity in 486 lung carcinoma tissues from TCGA-LUAD FPKM by using the "estimate" R package. Lung carcinoma tumor mutation burden was calculated by analyzing TCGA single nucleotide polymorphism data. The immune cell proportion was also evacuated via the CIBERSORT method. Lung carcinoma samples with P < 0.05 were considered significant. Based on the tumor purity and lung carcinoma gene matrix, we performed weighted gene coexpression network analysis (WGCNA), and the tumor purity-related module was identified. Then, we analyzed the functions of the factors involved in the module. We screened the coexpressed factors related to clinical outcome and immunophenotype. Finally, expression levels of these factors were measured at tissue and single-cell levels.

Results

A lung cancer tumor purity correlated coexpression network was determined. Five coexpressed genes (CD4, CD53, EVI2B, PLEK, and SASH3) were identified as tumor purity coexpressed genes that negatively correlated with tumor purity. Because the factors in the coexpression network often participate in similar biological processes, we found that CD4, CD53, EVI2B, PLEK, and SASH3 were most related to positive regulation of cytokine production and interleukin-2 production through functional enrichment. In a clinical phenotype analysis, we found that these five factors can be used as independent prognostic risk factors. We found that these factors were significantly negatively correlated with tumor purity and positively correlated with the immune score in the immunophenotyping analysis. Using GSEA analysis, we found that the antigen processing and presentation pathway were related to the five tumor coexpressed genes mentioned above. SASH3 and CD53 were used to conduct a prognostic model based on the interaction analysis of the Support Vector Machine and the Least Absolute Shrinkage and Selection Operator. SASH3 was verified to be related to CD8A using a single-cell analysis.

Conclusion

Tumor purity-related coexpression factors in the tumor microenvironment have essential clinical, genomic, and biological significance in lung cancer. These coexpression factors (SASH3 and CD53) can be used to classify tumor purity phenotypes and to predict clinical outcomes.

A Deep Look into the Program of Rapid Tumor Growth of Hepatocellular Carcinoma

BACKGROUND AND AIMS

Great efforts have been made towards increasing our understanding of the pathogenesis involved in hepatocellular carcinoma (HCC), but the rapid growth inherent to such tumor development remains to be explored.

METHODS

We identified distinct gene coexpression modes upon liver tumor growth using weighted gene coexpression network analysis. Modeling of tumor growth as signaling activity was employed to understand the main cascades responsible for the growth. Hub genes in the modules were determined, examined in vitro, and further assembled into the growth signature.

RESULTS

We revealed modules related to the different growth states in HCC, especially the fastest growth module, which is preserved among different HCC cohorts. Moreover, signaling flux in the cell cycle pathway was found to act as a driving force for rapid growth. Twenty hub genes in the module were identified and assembled into the growth signature, and two genes (NCAPH, and RAD54L) were tested for their growth potential in vitro. Genetic alteration of the growth signature affected the global gene expression. The activity of the signature was associated with tumor metabolism and immunity in HCC. Finally, the prognosis effect of the growth signature was reproduced in nine cancers.

CONCLUSIONS

These results collectively demonstrate the molecule organization of rapid tumor growth in HCC, which is a highly synergistic process, with implications for the future management of patients.