Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis

Identification of key ferroptosis genes in hepatocellular carcinoma and type 2 diabetes mellitus through bioinformatics analysis

Ferroptosis is a programmed cell death mode associated with iron metabolism, with accumulation of intracellular lipid peroxides, which is closely related to the occurrence and development of multiple diseases, including type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC). T2DM is a chronic metabolic disorder characterized by a combination of impaired insulin sensitivity and insufficient insulin production, frequently accompanied by obesity and fatty liver, which increases the risk of developing HCC. To explore the complex interactions between ferritin deposition, T2DM, and HCC, we performed bioinformatics analysis on publicly available gene expression data and identified 23 differentially expressed genes (DEGs) that are commonly expressed in both T2DM and HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these DEGs are primarily enriched in fatty acid metabolism and ferroptosis pathways. The weighted gene co-expression network analysis (WGCNA) identified 6 key genes associated with the pathogenesis of both diseases. Taking the intersection of DEGs and iron deposition-related genes, we identified ACSL4 as a key ferroptosis gene involved in the co-morbidity of T2DM and HCC. To validate the bioinformatics findings, we assessed the expression of ACSL4 using Receiver operating characteristic (ROC) curve analysis, which revealed an Area Under the Curve (AUC) of 0.886 for HCC and 0.745 for T2DM. Additionally, an insulin resistance model was established in HepG2 cells by treatment with 350 µM palmitic acid (PA), resulting in significant changes in cell morphology. Oil Red O staining showed a marked increase in lipid accumulation. RT-PCR analysis further confirmed the significant alteration in ACSL4 gene expression. In conclusion, this study is the first to integrate bioinformatics tools to investigate the potential mechanistic links between iron metabolism and the comorbidity of T2DM and HCC, uncovering a novel pathogenic pathway. These findings provide new directions for drug development and therapeutic strategies in the future.

Unraveling the genetic mysteries of sarcopenia: A bioinformatics approach

Background As life expectancy increases and the global population ages, the incidence of sarcopenia is also increasing, highlighting the need for better diagnosis and treatment methods.ObjectiveTo study the genetic expression of sarcopenia using bioinformatics methods.MethodsA Weighted Gene Coexpression Network Analysis (WGCNA) was conducted to construct coexpression networks, along with protein-protein interaction networks. Diagnostic biomarker potential was evaluated using receiver operating characteristic curves. An analysis of Single-Sample Gene Set Enrichment Analysis (ssGSEA) was performed in order to determine the amount of immune cell infiltration. We analyzed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment using the KEGG.ResultsWGCNA identified modules linked to bone metabolism, ssGSEA showed unique gene enrichment patterns, and 268 genes were found to be differentially expressed in sarcopenia. Fourteen co-expression modules related to bone metabolism were identified, with one showing a strong positive correlation. KEGG pathway analysis indicated downregulation of the renin-angiotensin system and Alzheimer's disease pathways. The differentially expressed genes were primarily involved in adipocyte differentiation.ConclusionThis study analyzes genetic changes and immune cell patterns in sarcopenia, providing insights into its causes and potential diagnostic markers for future research on treatments.

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

Integrated in silico and in vitro exploration of the anti-VEGFR-2 activities of a semisynthetic xanthine alkaloid inhibiting breast cancer

This study presents T-1-NBAB, a new compound derived from the natural xanthine alkaloid theobromine, aimed at inhibiting VEGFR-2, a crucial protein in angiogenesis. T-1-NBAB's potential to interacts with and inhibit the VEGFR-2 was indicated using in silico techniques like molecular docking, MD simulations, MM-GBSA, PLIP, essential dynamics, and bi-dimensional projection experiments. DFT experiments was utilized also to study the structural and electrostatic properties of T-1-NBAB. Computational analysis was performed to predict the ADME-Tox profiles of T-1-NBAB. After semisynthesis, the in vitro results showed that T-1-NBAB effectively inhibits VEGFR-2, with an IC50 of 0.115 μM, compared to sorafenib's 0.0591 μM. In vitro tests also demonstrated significant activity of T-1-NBAB against breast cancer cell lines MCF7 and T47D, with IC50 values of 16.88 μM and 61.17 μM, respectively, and high selectivity. Importantly, T-1-NBAB induced early and late apoptosis in MCF7 cells, indicating its potential as a strong anticancer agent. Additionally, T-1-NBAB reduced the migration and healing abilities of MCF7 cells, suggesting it could be a promising anti-angiogenic agent. Overall, these findings suggest that T-1-NBAB is a promising lead compound for further research as a potential treatment for breast cancer.

Competing endogenous RNA networks in ovarian cancer: from bench to bedside

Epithelial ovarian cancer is responsible for the majority of ovarian malignancies, and its highly invasive nature and chemoresistant development have been major obstacles to treating patients with mainstream treatments. In recent decades, the significance of microRNAs (miRNAs), circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and competing endogenous RNAs (ceRNAs) has been highlighted in ovarian cancer development. This hidden language between these RNAs has led to the discovery of enormous regulatory networks in ovarian cancer cells that substantially affect gene expression. Aside from providing ample opportunities for targeted therapies, circRNA- and lncRNA-mediated ceRNA network components provide invaluable biomarkers. The current study provides a comprehensive and up-to-date review of the recent findings on the significance of these ceRNA networks in the hallmarks of ovarian cancer oncogenesis, treatment, diagnosis, and prognosis. Also, it provides the authorship with future perspectives in the era of single-cell RNA sequencing and personalized medicine.

Exploitation of Key Regulatory Modules and Genes for High-Salt Adaptation in Schizothoracine by Weighted Gene Co-Expression Network Analysis

Schizothoracine fishes in saltwater lakes of the Tibetan Plateau are important models for studying the evolution and uplift of the Tibetan Plateau. Examining their adaptation to the high-salt environment is interesting. In this study, we first assembled the RNA-Seq data of each tissue of G. przewalskii, G. selincuoensis, and G. namensis from Qinghai Lake, Selincuo Lake, and Namtso Lake, respectively, obtained by the group previously. After obtaining reliable results, the adaptation of the gills, kidneys, and livers of the three species to the high-salinity environment was assessed by weighted gene co-expression network analysis (WGCNA). Using module eigengenes (ME), 21, 22, and 22 gene modules were identified for G. przewalskii, G. selincuoensis, and G. nemesis, respectively. Functional clustering analysis of genes in the significant association module identified several genes associated with osmolarity-regulated potential KEGG pathways in the gills of three species of Schizothoracine fish. Th17 cell differentiation pathway was up-regulated in the gills of all three species; histocompatibility class 2 II antigen and E alpha (h2-ea) were up-regulated genes in this pathway. Functional clustering analysis of genes in apparently related modules in the kidney unveiled several differential KEGG pathways. The pentose phosphate pathway was up-regulated in the three Schizothoracine fishes, and glucose-6-phosphate dehydrogenase (g6pd) was an up-regulated gene in this pathway. In the livers of the three Schizothorax species, the propanoate metabolism pathway was up-regulated, and succinate-CoA ligase GDP-forming subunit beta (suclg2) was an up-regulated gene in this pathway. The above analyses provide reference data for the adaptation of Schizothorax to high-salt environments and lay the foundation for future studies on the adaptive mechanism of Schizothorax in the plateau. These results partly fill the void in the knowledge gap in the survival adaptations of Schizothoracine fishes to highland saline lakes.

Exploring the therapeutic potential of puerarin on intervertebral disc degeneration by regulating apoptosis of nucleus pulposus cells

Intervertebral disc degeneration (IVDD) stands as a prevalent chronic orthopedic ailment, profoundly impacting patients' well-being due to incapacitating low back pain. Studies have highlighted a close correlation between IVDD and the programmed cell death of nucleus pulposus (NP) cells orchestrated by interleukin-1 beta (IL-1β), tumor necrosis factor-alpha (TNF-α), and caspase-3 (CASP3). Puerarin, renowned for its anti-inflammatory attributes and its influence on IL-1β and TNF-α, emerges as a promising candidate for IVDD treatment. However, the precise mechanism by which it regulates apoptosis via these pathways remains ambiguous. This investigation utilizes bioinformatics to unveil the molecular intricacies of puerarin-mediated apoptosis regulation in IVDD, substantiated by preliminary in vitro experiments. Analysis exposes aberrant expression of pivotal apoptosis-associated proteins (IL-1β, TNF-α, CASP3, CASP8, and BCL2) in IVDD patients, with network pharmacology indicating puerarin's potential efficacy in IVDD treatment by modulating apoptosis and cellular senescence pathways. Further experiments elucidate puerarin's capacity to stimulate NP cell proliferation while inhibiting apoptosis, potentially contributing to IVDD mitigation. Western blot and PCR outcomes reveal escalated expression of apoptosis-related proteins (IL-1β, TNF-α, and CASP3) in lipopolysaccharide-treated NPCs, ameliorated by puerarin intervention. Molecular docking simulations demonstrate favorable binding properties of puerarin with apoptotic proteins, while flow cytometry analysis indicates its ability to diminish NPC apoptosis. These discoveries imply that puerarin might alleviate NPC apoptosis by modulating key targets, thereby potentially ameliorating IVDD. In summary, this study unveils the intrinsic mechanism of puerarin in regulating NPC apoptosis to alleviate IVDD, underscoring its therapeutic promise.

Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA

Feather pecking (FP) is a significant welfare concern in poultry, which can result in reduced egg production, deterioration of feather condition, and an increase in mortality rate. This can harm the health of birds and the economic benefits of breeders. FP, as a complex trait, is regulated by multiple factors, and so far, no one has been able to elucidate its exact mechanism. In order to delve deeper into the genetic mechanism of FP, we acquired the expression matrix of dataset GSE36559. We analyzed the gene modules associated with the trait through WGCNA (Weighted correlation network analysis), and then used KEGG and GO to identify the biological pathways enriched by the modules using KEGG and GO. Subsequently, we analyzed the module with the highest correlation (0.99) using three machine learning (ML) algorithms to identify the feature genes that they collectively recognized. In this study, five feature genes, NUFIP2, ST14, OVM, GLULD1, and LOC424943, were identified. Finally, the discriminant value of the feature genes was evaluated by manipulating the receiver operating curve (ROC) in the external dataset GSE10380.

ADAM12 Silencing Mediated by FOXC2 Represses Meningioma Progression Through Inactivating the JAK1/STAT3/VEGFA Pathway

Meningioma is a prevalently intracranial tumor, and the malignant type is aggressive with high recurrence. A Disintegrin and Metalloprotease 12 (ADAM12) is a common oncogene and differentially expressed in meningioma. However, its roles and mechanisms in meningioma development remain obscure. The differentially expressed genes in meningioma were analyzed by GEO (GSE77259 and GSE43290) datasets and weighted gene co-expression network analysis (WGCNA) based on GSE16581. ADAM12 expression was measured via qRT-PCR and western blot. The correlation between ADAM12 and FOXC2 was predicted through JASPER tool and identified via luciferase reporter analysis. Cell proliferation, migration and invasion were investigated using CCK-8, EdU, transwell assays. The JAK1/STAT3/VEGFA signaling was activated by IL-6, and analyzed via western blot. The differentially expressed ADAM12 in meningioma was screened by WGCNA and GEO analyses. ADAM12 silencing repressed meningioma cell proliferation, and decreased migration and invasion. The transcription factor FOXC2 expression was enhanced in meningioma based on GSE77259 and GSE43290 datasets, and positively induced ADAM12 transcription. The JAK1/STAT3/VEGFA signaling was inactivated due to ADAM12 silencing and activated via IL-6. Upregulation of FOXC2 promoted cell proliferation, migration and invasion, and these effects were reversed by silencing ADAM12. ADAM12 knockdown mediated via FOXC2 silencing restrained proliferation, migration and invasion of meningioma cells through inactivating the JAK1/STAT3/VEGFA pathway.

Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes

Cerebral ischemic stroke (CIS) has the characteristics of a high incidence, disability, and mortality rate. Here, we aimed to explore the potential pathogenic mechanisms of ferroptosis-related genes (FRGs) in CIS. Three microarray datasets from the Gene Expression Omnibus (GEO) database were utilized to analyze differentially expressed genes (DEGs) between CIS and normal controls. FRGs were obtained from a literature report and the FerrDb database. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to screen hub genes. The receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic value of key genes in CIS, followed by analysis of immune microenvironment, transcription factor (TF) regulatory network, drug prediction, and molecular docking. In total, 128 CIS samples were divided into 2 subgroups after clustering analysis. Compared with cluster A, 1560 DEGs were identified in cluster B. After the construction of the WGCNA and PPI network, 5 hub genes, including MAPK3, WAS, DNAJC5, PRKCD, and GRB2, were identified for CIS. Interestingly, MAPK3 was a FRG that differentially expressed between cluster A and cluster B. The expression levels of 5 hub genes were all specifically highly in cluster A subtype. It is noted that neutrophils were the most positively correlated with all 5 real hub genes. PRKCD was one of the target genes of FASUDIL. In conclusion, five real hub genes were identified as potential diagnostic markers, which can distinguish the two subtypes well.

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

Purpose

The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci.

Methods

To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA.

Results

Our research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose.

Conclusion

We searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

Identification and validation of IRF6 related to ovarian cancer and biological function and prognostic value

BACKGROUND

Ovarian cancer (OC) is a severe gynecological malignancy with significant diagnostic and therapeutic challenges. The discovery of reliable cancer biomarkers can be used to adjust diagnosis and improve patient care. However, serous OC lacks effective biomarkers. We aimed to identify novel biomarkers for OC and their pathogenic causes.

METHODS

The present study used the differentially expressed genes (DEGs) obtained from the "Limma" package and WGCNA modules for intersection analysis to obtain DEGs in OC. Three hub genes were identified-claudin 3 (CLDN3), interferon regulatory factor 6 (IRF6), and prostasin (PRSS8)-by searching for hub genes through the PPI network and verifying them in GSE14407, GSE18520, GSE66957, and TCGA + GTEx databases. The correlation between IRF6 and the prognosis of OC patients was further confirmed in Kaplan-Miller Plotter. RT-qPCR and IHC confirmed the RNA and protein levels of IRF6 in the OC samples. The effect of IRF6 on OC was explored using transwell invasion and scratch wound assays. Finally, we constructed a ceRNA network of hub genes and used bioinformatics tools to predict drug sensitivity.

RESULTS

The joint analysis results of TCGA, GTEx, and GEO databases indicated that IRF6 RNA and protein levels were significantly upregulated in serous OC and were associated with OS and PFS. Cell function experiments revealed that IRF6 knockdown inhibited SKOV3 cell proliferation, migration and invasion.

CONCLUSION

IRF6 is closely correlated with OC development and progression and could be considered a novel biomarker and therapeutic target for OC patients.

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.

Mechanisms of inhibition of nucleus pulposus cells pyroptosis through SDF1/CXCR4-NFkB-NLRP3 axis in the treatment of intervertebral disc degeneration by Duhuo Jisheng Decoction

Intervertebral disc degeneration (IVDD) is one of the leading causes of lower back pain and the most common health problem in the world. Inflammasomes, which is mainly caused by NLRP3, mediated nucleus pulposus pyroptosis has been discovered to be strongly related to IVDD. In addition, Duhuo Jisheng Decoction (DHJSD) has anti-inflammatory and regulatory effects on NLRP3 inflammasome, but the molecular mechanism of whether DHJSD can regulate pyroptosis through NLRP3 to treat IVDD is unclear. In this study, we used a bioinformatics way to discover the molecular mechanism of DHJSD regulation of pyroptosis in IVDD, and validated our predictions through vitro and vivo experiments. Through bioinformatics, we found that NLRP3, GSDMD, IL-1βand other hub proteins of pyroptosis were highly expressed in IVDD SD rats, and network pharmacology discovered that DHJSD may control cellular senescence, apoptosis, and pyroptosis in order to treat IVDD. Additional findings demonstrated that DHJSD could successfully treat IVDD brought on by imaging and histomorphological analysis. Western blot showed that NLRP3, a key protein of pyroptosis, was elevated in rat degenerated nucleus pulposus tissue and lipopolysaccharide-treated Nucleus pulposus Cells (NPCs), and that DHJSD intervention was effective in reducing LPS-induced inflammatory responses and further suppressing the expression of pyroptosis related proteins to improve IVDD. The specific mechanism is that DHJSD inhibits NPCs pyroptosis via the SDF-1/CXCR4-NF-kB-NLRP3 axis. In conclusion, we revealed the intrinsic mechanism of DHJSD regulation of NPCs pyroptosis to improve IVDD and its intrinsic value for IVDD treatment.

Anti-breast cancer potential of a new xanthine derivative: In silico, antiproliferative, selectivity, VEGFR-2 inhibition, apoptosis induction and migration inhibition studies

BACKGROUND

The overexpression of VEGFR-2 receptors in breast cancer provides a valuable approach to anticancer strategies. Targeting VEGFR-2, a new semisynthetic compound (T-1-MCPAB) has been designed.

METHODS

Computational methods (ADMET, toxicity, DFT, Molecular Docking, Molecular Dynamics Simulations, MM-GBSA, PLIP, and PCAT) were conducted. In addition to the semi-synthesis, in vitro studies (anti-VEGFR-2, anti-proliferative, flow cytometry, and wound scratch assay) were employed.

RESULTS

ADME and toxicity profiles of T-1-MCPAB studies indicated its overall drug-likeness showing results much better than Sorafenib. Then, T-1-MCPAB's exact 3D structure, stability, and reactivity were evoked by the DFT calculations. Molecular docking, molecular dynamics simulations, MM-GPSA, PLIP, and PCAT studies denoted the correct binding and inhibiting potential of T-1-MCPAB, towards VEGFR-2 protein. After the semisynthesis, T-1-MCPAB inhibited VEGFR-2 with an IC50 of 0.135 µM, which was comparable to sorafenib's IC50 of 0.0591 µM. T-1-MCPAB also showed a notable performance against MCF7 and T47D breast cancer cell lines with IC50 values of 30.95 µM and 63.64 µM, respectively, and had high selectivity index values of 3.7 and 1.8, respectively. Furthermore, T-1-MCPAB influenced early and late apoptosis and significantly decreased the potential of MCF7 cells to heal and migrate.

CONCLUSION

T-1-MCPAB is a promising VEGFR-2 inhibitor with potential for breast cancer treatment. Further chemical and biological studies are needed to explore its potential as a therapeutic agent.

Putative role of glial cells in treatment resistance depression: An updated critical literation review and evaluation of single-nuclei transcriptomics data

AIMS

Major depressive disorder (MDD) is a prevalent global mental illness with diverse underlying causes. Despite the availability of first-line antidepressants, approximately 10-30 % of MDD patients do not respond to these medications, falling into the category of treatment-resistant depression (TRD). Our study aimed to elucidate the precise molecular mechanisms through which glial cells contribute to depression-like episodes in TRD.

MATERIALS AND METHODS

We conducted a comprehensive literature search using the PubMed and Scopus electronic databases with search terms carefully selected to be specific to our topic. We strictly followed inclusion and exclusion criteria during the article selection process, adhering to PRISMA guidelines. Additionally, we carried out an in-depth analysis of postmortem brain tissue obtained from patients with TRD using single-nucleus transcriptomics (sn-RNAseq).

KEY FINDINGS

Our data confirmed the involvement of multiple glia-specific markers (25 genes) associated with TRD. These differentially expressed genes (DEGs) primarily regulate cytokine signaling, and they are enriched in important pathways such as NFκB and TNF-α. Notably, DEGs showed significant interactions with the transcription factor CREB1. sn-RNAseq analysis confirmed dysregulation of nearly all designated DEGs; however, only Cx30/43, AQP4, S100β, and TNF-αR1 were significantly downregulated in oligodendrocytes (OLGs) of TRD patients. With further exploration, we identified the GLT-1 in OLGs as a hub gene involved in TRD.

SIGNIFICANCE

Our findings suggest that glial dysregulation may hinder the effectiveness of existing therapies for TRD. By targeting specific glial-based genes, we could develop novel interventions with minimal adverse side effects, providing new hope for TRD patients who currently experience limited benefits from invasive treatments.

Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA

Background

Recurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications.

Methods

The GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess the performance of hub genes in the dataset. The expression of hub genes in both the RPL and control group samples was validated using RT-qPCR. The immune cell infiltration level of RPL was assessed using CIBERSORT. Additionally, pan-cancer analysis was conducted using Sangerbox, and small-molecule drug screening was performed using CMap.

Results

A total of 352 DEGs were identified, including 198 up-regulated genes and 154 down-regulated genes. Enrichment analysis indicated that the DEGs were primarily associated with Fc gamma R-mediated phagocytosis, the Fc epsilon RI signaling pathway, and various metabolism-related pathways. The turquoise module, which showed the highest relevance to clinical symptoms based on WGCNA results, contained 104 DEGs. Three hub genes, WBP11, ACTR2, and NCSTN, were identified using machine learning algorithms. ROC curves demonstrated a strong diagnostic value when the three hub genes were combined. RT-qPCR confirmed the low expression of WBP11 and ACTR2 in RPL, whereas NCSTN exhibited high expression. The immune cell infiltration analysis results indicated an imbalance of macrophages in RPL. Meanwhile, these three hub genes exhibited aberrant expression in multiple malignancies and were associated with a poor prognosis. Furthermore, we identified several small-molecule drugs.

Conclusion

This study identifies and validates hub genes in RPL, which may lead to significant advancements in understanding the molecular mechanisms and treatment strategies for this condition.

Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma

Background: Immunity and ferroptosis often play a synergistic role in the progression and treatment of hepatocellular carcinoma (HCC). However, few studies have focused on identifying immune-related ferroptosis gene biomarkers. Methods: We performed weighted gene co-expression network analysis (WGCNA) and random forest to identify prognostic differentially expressed immune-related genes (PR-DE-IRGs) highly related to HCC and characteristic prognostic differentially expressed ferroptosis-related genes (PR-DE-FRGs) respectively to run co-expression analysis for prognostic differentially expressed immune-related ferroptosis characteristic genes (PR-DE-IRFeCGs). Lasso regression finally identified 3 PR-DE-IRFeCGs for us to construct a prognostic predictive model. Differential expression and prognostic analysis based on shared data from multiple sources and experimental means were performed to further verify the 3 modeled genes' biological value in HCC. We ran various performance testing methods to test the model's performance and compare it with other similar signatures. Finally, we integrated composite factors to construct a comprehensive quantitative nomogram for accurate prognostic prediction and evaluated its performance. Results: 17 PR-DE-IRFeCGs were identified based on co-expression analysis between the screened 17 PR-DE-FRGs and 34 PR-DE-IRGs. Multi-source sequencing data, QRT-PCR, immunohistochemical staining and testing methods fully confirmed the upregulation and significant prognostic influence of the three PR-DE-IRFeCGs in HCC. The model performed well in the performance tests of multiple methods based on the 5 cohorts. Furthermore, our model outperformed other related models in various performance tests. The immunotherapy and chemotherapy guiding value of our signature and the comprehensive nomogram's excellent performance have also stood the test. Conclusion: We identified a novel PR-DE-IRFeCGs signature with excellent prognostic prediction and clinical guidance value in HCC.

Epithelial-mesenchymal transition-related gene prognostic index and phenotyping clusters for hepatocellular carcinoma patients

Epithelial-mesenchymal transition (EMT) contributes to high tumor heterogeneity and the immunosuppressive environment of the HCC tumor microenvironment (TME). Here, we developed EMT-related genes phenotyping clusters and systematically evaluated their impact on HCC prognosis, the TME, and drug efficacy prediction. We identified HCC specific EMT-related genes using weighted gene co-expression network analysis (WGCNA). An EMT-related genes prognostic index (EMT-RGPI) capable of effectively predicting HCC prognosis was then constructed. Consensus clustering of 12 HCC specific EMT-related hub genes uncovered two molecular clusters C1 and C2. Cluster C2 preferentially associated with unfavorable prognosis, higher stemness index (mRNAsi) value, elevated immune checkpoint expression, and immune cell infiltration. The TGF-β signaling, EMT, glycolysis, Wnt β-catenin signaling, and angiogenesis were markedly enriched in cluster C2. Moreover, cluster C2 exhibited higher TP53 and RB1 mutation rates. The TME subtypes and tumor immune dysfunction and exclusion (TIDE) score showed that cluster C1 patients responded well to immune checkpoint inhibitors (ICIs). Half-maximal inhibitory concentration (IC50) revealed that cluster C2 patients were more sensitive to chemotherapeutic and antiangiogenic agents. These findings may guide risk stratification and precision therapy for HCC patients.

Identification of Key Biomarkers Associated with Immunogenic Cell Death and Their Regulatory Mechanisms in Severe Acute Pancreatitis Based on WGCNA and Machine Learning

Immunogenic cell death (ICD) is a form of programmed cell death with a strong sense of inflammatory detection, whose powerful situational awareness can cause the reactivation of aberrant immunity. However, the role of ICD in the pathogenesis of severe acute pancreatitis (SAP) has yet to be investigated. This study aims to explore the pivotal genes associated with ICD in SAP and how they relate to immune infiltration and short-chain fatty acids (SCFAs), in order to provide a theoretical foundation for further, in-depth mechanistic studies. We downloaded GSE194331 datasets from the Gene Expression Omnibus (GEO). The use of differentially expressed gene (DEG) analysis; weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to identify a total of three ICD-related hub genes (LY96, BCL2, IFNGR1) in SAP. Furthermore, single sample gene set enrichment analysis (ssGSEA) demonstrated that hub genes are closely associated with the infiltration of specific immune cells, the activation of immune pathways and the metabolism of SCFAs (especially butyrate). These findings were validated through the analysis of gene expression patterns in both clinical patients and rat animal models of SAP. In conclusion, the first concept of ICD in the pathogenesis of SAP was proposed in our study. This has important implications for future investigations into the pro-inflammatory immune mechanisms mediated by damage-associated molecular patterns (DAMPs) in the late stages of SAP.

Integrative Methylome and Transcriptome Characterization Identifies SERINC2 as a Tumor-Driven Gene for Papillary Thyroid Carcinoma

Most papillary thyroid carcinomas (PTCs) can be diagnosed preoperatively by routine evaluation, such as thyroid ultrasonography and fine-needle aspiration biopsy. Nevertheless, understanding how to differentiate indolent thyroid tumors from aggressive thyroid cancers remains a challenge, which may cause overtreatment. This study aimed to identify papillary thyroid cancer-specific indicators with whole-genome DNA methylation and gene expression profiles utilizing Infinium Methylation EPIC BeadChip (850k) and RNA arrays. In this paper, we report SERINC2 as a potential tumor-driven indicator in PTC. The up-regulated expression levels of SERINC2 were verified in PTC cell lines via qPCR. Then, cell counting kit 8 (CCK-8), wound healing, and flow cytometric assays were performed to confirm the influence of SERINC2 on proliferation and apoptosis in PTC cell lines after intervention or overexpression. Moreover, the investigation of data from the Cancer Dependency Map (DepMap) provided a potential pathway targeted by SERINC2. The activation of the tryptophan metabolic pathway may reduce the dependency of SERINC2 in thyroid cancers. In conclusion, our results demonstrate the whole-genome DNA methylation and gene expression profiles of papillary thyroid carcinoma, identify SERINC2 as a potential tumor-driven biomarker, and preliminarily verify its function in PTC.

cAMP-Dependent Signaling and Ovarian Cancer

cAMP-dependent pathway is one of the most significant signaling cascades in healthy and neoplastic ovarian cells. Working through its major effector proteins-PKA and EPAC-it regulates gene expression and many cellular functions. PKA promotes the phosphorylation of cAMP response element-binding protein (CREB) which mediates gene transcription, cell migration, mitochondrial homeostasis, cell proliferation, and death. EPAC, on the other hand, is involved in cell adhesion, binding, differentiation, and interaction between cell junctions. Ovarian cancer growth and metabolism largely depend on changes in the signal processing of the cAMP-PKA-CREB axis, often associated with neoplastic transformation, metastasis, proliferation, and inhibition of apoptosis. In addition, the intracellular level of cAMP also determines the course of other pathways including AKT, ERK, MAPK, and mTOR, that are hypo- or hyperactivated among patients with ovarian neoplasm. With this review, we summarize the current findings on cAMP signaling in the ovary and its association with carcinogenesis, multiplication, metastasis, and survival of cancer cells. Additionally, we indicate that targeting particular stages of cAMP-dependent processes might provide promising therapeutic opportunities for the effective management of patients with ovarian cancer.

A scoping review on the significance of programmed death-ligand 1-inhibiting microRNAs in non-small cell lung treatment: A single-cell RNA sequencing-based study

Background

The programmed death-ligand 1 (PD-L1)/PD-1 axis is one of the well-established inhibitory axes in regulating immune responses. Besides the significance of tumor-intrinsic PD-L1 expression in immune evasion, its oncogenic role has been implicated in various malignancies, like non-small cell lung cancer (NSCLC). As small non-coding RNAs, microRNAs (miRs) have pivotal roles in cancer biology. The current study aimed to systematically review the current knowledge about the significance of PD-L1-inhibiting miRs in NSCLC inhibition and their underlying mechanisms.

Materials and methods

We conducted the current scoping review based on the PRISMA-ScR statement. We systematically searched Embase, Scopus, Web of Science, PubMed, Ovid, EBSCO, ProQuest, Cochrane Library, African Index Medicus, and Pascal-Francis up to 4 April 2021. We also performed in silico tumor bulk RNA sequencing and single-cell RNA sequencing to further the current knowledge of the non-coding RNA-mediated tumor-intrinsic PD-L1 regulation and the PD-L1/PD-1 axis in NSCLC.

Results

The ectopic expression of hsa-miR-194-5p, hsa-miR-326, hsa-miR-526b-3p, hsa-miR-34a-5p, hsa-miR-34c-5p, hsa-miR-138-5p, hsa-miR-377-3p, hsa-let-7c-5p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, and hsa-miR-197-3p, as PD-L1-inhibiting miR, inhibits NSCLC development. These PD-L1-inhibiting miRs can substantially regulate the cell cycle, migration, clonogenicity, invasion, apoptosis, tumor chemosensitivity, and host anti-tumoral immune responses. Based on single-cell RNA sequencing results, PD-L1 inhibition might liberate the tumor-infiltrated CD8+ T-cells and dendritic cells (DCs)-mediated anti-tumoral immune responses via disrupting the PD-L1/PD-1 axis.

Conclusion

Given the promising preclinical results of these PD-L1-inhibiting miRs in inhibiting NSCLC development, their ectopic expression might improve NSCLC patients' prognosis; however, further studies are needed to translate this approach into clinical practice.

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation

BACKGROUND

Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA).

RESULTS

In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response.

CONCLUSION

We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.

KIF11: A potential prognostic biomarker for predicting bone metastasis‑free survival of prostate cancer

Most prostate cancer (PCa) cases remain indolent with a relatively good prognosis. However, bone metastasis of PCa can quickly worsen prognoses and lead to mortality. Metastasis-free survival (MFS), a strong surrogate for overall survival, is widely used in PCa prognosis research. The present study identified molecules that affect bone MFS in PCa, with clinical validation. Three datasets (GSE32269, GSE74367 and GSE77930) were downloaded from the Gene Expression Omnibus database. Hub genes most relevant to clinical traits (bone metastasis-associated morbidity) were identified by weighted gene co-expression network analysis (WGCNA) and subjected to logistic regression analysis. Patient samples were obtained between January 2014 and December 2016, with a clinically annotated follow-up in December 2021. Clinical data and follow-up information for 60 patients with PCa were used in MFS analysis. Tumor samples were retrieved, and immunohistochemistry was performed to detect vascular endothelial growth factor (VEGF). The prognostic potential of the two molecules was assessed using Cox proportional hazards regression analysis. A total of 16 gene modules were obtained via WGCNA, and the tan module, containing 147 genes, was most closely linked to bone metastasis. In total, 877 differentially expressed genes (DEGs) were detected. The DEG-tan module intersection yielded seven hub genes [BUB1, kinesin family member (KIF)2C, RACGAP1, CENPE, KIF11, TTK and KIF20A]. Using univariate and multivariate logistic regression analyses for independent risk factors of bone metastasis, KIF11 and VEGF were found to be significantly associated with a higher T stage, prostate-specific antigen level and Gleason score. In addition, KIF11 and VEGF expression levels were positively correlated (P<0.001). Using univariate Cox analysis, KIF11 and VEGF were found to exhibit a significant association with poor MFS (P<0.05). However, only KIF11 was significantly associated with MFS upon multivariate analysis (P=0.007; hazard ratio, 2.776; 95% confidence interval, 1.315-5.859). Markers of bone metastasis in PCa were identified. Overall, KIF11 is an independent indicator that can predict bone metastasis for patients with PCa, which could be used to guide clinical practice.

Revealing oxidative stress-related genes in osteoporosis and advanced structural biological study for novel natural material discovery regarding MAPKAPK2

OBJECTIVES

This study aimed to find novel oxidative stress (OS)-related biomarkers of osteoporosis (OP), together with targeting the macromolecule Mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2) protein to further discover potential novel materials based on an advanced structural biology approach.

METHODS

Gene expression profiles of GSE35958 were obtained from the Gene Expression Omnibus (GEO) database, which were included for weighted gene co-expression network analysis (WGCNA) and differential analysis to identify the most correlated module, to identify OS-related hub genes in the progression of OP. Functional annotations were also analyzed on the interested module to get a comprehensive understanding of these genes. Then, a series of advanced structural biology methods, including high-throughput screening, pharmacological characteristic prediction, precise molecular docking, molecular dynamics simulation, etc., was implemented to discover novel natural inhibitor materials against the MAPKAPK2 protein.

RESULTS

The brown module containing 720 genes was identified as the interested module, and a group set of genes was determined as the hub OS-related genes, including PPP1R15A, CYB5R3, BCL2L1, ABCD1, MAPKAPK2, HSP90AB1, CSF1, RELA, P4HB, AKT1, HSP90B1, and CTNNB1. Functional analysis demonstrated that these genes were primarily enriched in response to chemical stress and several OS-related functions. Then, Novel Materials Discovery demonstrated that two compounds, ZINC000014951634 and ZINC000040976869, were found binding to MAPKAPK2 with a favorable interaction energy together with a high binding affinity, relatively low hepatoxicity and carcinogenicity, high aqueous solubility and intestinal absorption levels, etc., indicating that the two compounds were ideal potential inhibitor materials targeting MAPKAPK2.

CONCLUSION

This study found a group set of OS-related biomarkers of OP, providing further insights for OS functions in the development of OP. This study then focused on one of the macromolecules, MAPKAPK2, to further discover potential novel materials, which was of great significance in guiding the screening of MAPKAPK2 potential materials.