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Abstract Sepsis-induced myopathy is one of the serious complications of sepsis, which severely affects the respiratory and peripheral motor systems of patients, reduces their quality of life, and jeopardizes their lives, as evidenced by muscle atrophy, loss of strength, and impaired regeneration after injury. The pathogenesis of sepsis-induced myopathy is complex, mainly including cytokine action, enhances free radical production in muscle, increases muscle protein hydrolysis, and decreases skeletal muscle protein synthesis, etc. The above mechanisms have been demonstrated in existing studies. However, it is still unclear how the overall pattern of gene co-expression affects the pathological process of sepsis-induced myopathy. Therefore, we intend to identify hub genes and signaling pathways. Weighted gene co-expression network analysis was our main approach to study gene expression profiles: skeletal muscle transcriptome in ICU patients with sepsis-induced multi-organ failure (GSE13205). After data pre-processing, about 15,181 genes were used to identify 13 co-expression modules. Then, 16 genes (FEM1B, KLHDC3, GPX3, NIFK, GNL2, EBNA1BP2, PES1, FBP2, PFKP, BYSL, HEATR1, WDR75, TBL3, and WDR43) were selected as the hub genes including 3 up-regulated genes and 13 down-regulated genes. Then, Gene Set Enrichment Analysis was performed to show that the hub genes were closely associated with skeletal muscle dysfunction, necrotic and apoptotic skeletal myoblasts, and apoptosis in sepsis-induced myopathy. Overall, 16 candidate biomarkers were certified as reliable features for more in-depth exploration of sepsis-induced myopathy in basic and clinical studies.

Sepsis-induced myopathy is one of the serious complications of sepsis, which severely affects the respiratory and peripheral motor systems of patients, reduces their quality of life, and jeopardizes their lives, as evidenced by muscle atrophy, loss of strength, and impaired regeneration after injury. The pathogenesis of sepsis-induced myopathy is complex, mainly including cytokine action, enhances free radical production in muscle, increases muscle protein hydrolysis, and decreases skeletal muscle protein synthesis, etc. The above mechanisms have been demonstrated in existing studies. However, it is still unclear how the overall pattern of gene co-expression affects the pathological process of sepsis-induced myopathy. Therefore, we intend to identify hub genes and signaling pathways. Weighted gene co-expression network analysis was our main approach to study gene expression profiles: skeletal muscle transcriptome in ICU patients with sepsis-induced multi-organ failure (GSE13205). After data pre-processing, about 15,181 genes were used to identify 13 co-expression modules. Then, 16 genes (FEM1B, KLHDC3, GPX3, NIFK, GNL2, EBNA1BP2, PES1, FBP2, PFKP, BYSL, HEATR1, WDR75, TBL3, and WDR43) were selected as the hub genes including 3 up-regulated genes and 13 down-regulated genes. Then, Gene Set Enrichment Analysis was performed to show that the hub genes were closely associated with skeletal muscle dysfunction, necrotic and apoptotic skeletal myoblasts, and apoptosis in sepsis-induced myopathy. Overall, 16 candidate biomarkers were certified as reliable features for more in-depth exploration of sepsis-induced myopathy in basic and clinical studies. [ABSTRACT FROM AUTHOR]

In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and hard optimization problem. One of the main difficulties in identifying overlapping protein complexes is the accuracy of the partitioning results. In order to accurately identify the overlapping structure of protein complexes, this paper has proposed an overlapping complex detection algorithm termed OCDPSO-Net, which is based on PSO-Net (a well-known modified version of the particle swarm optimization algorithm). The framework of the OCDPSO-Net method consists of three main steps, including an initialization strategy, a movement strategy for each particle, and enhancing search ability in order to expand the solution space. The proposed algorithm has employed the partition density concept for measuring the partitioning quality in PPI network complexes and tried to optimize the value of this quantity by applying the line graph concept of the original graph representing the protein interaction network. The OCDPSO-Net algorithm is applied to a Collins PPI network and the obtained results are compared with different state-of-the-art algorithms in terms of precision (p), recall (R), and F-measure (F.-measure). Experimental results confirm that the proposed algorithm has good clustering performance and has outperformed most of the existing recent overlapping algorithms. [ABSTRACT FROM AUTHOR]

Background: Gallbladder Cancer (GBC) is one of the most common cancers of the biliary tract and the third commonest gastrointestinal (GI) malignancy worldwide. The disease is characterized by the late presentation and poor outcome despite treatment, and hence, newer therapies and targets need to be identified. Methods: The current study investigated various functionally enriched pathways in GBC pathogenesis involving the genes identified through Next Generation Sequencing (NGS) in a hospital-based cohort. The Pathway enrichment analysis and Gene Ontology (GO) were carried out after NGS, followed by the construction of the protein–protein interaction (PPI) network to discover associations among the genes. Results: Of the thirty-three patients with GBC who were screened through next-generation sequencing (NGS), 27somatic mutations were identified. These mutations involved a total of 14 genes. The p53 and KRAS were commonly found to be mutated, while mutations in other genes were seen in one case each, the mean number of mutations were 1.2, and maximum mutation in a single case (eight) was seen in one case. The bioinformatics analysis identified MAP kinase, PI3K-AKT, EGF/EGFR, and Focal Adhesion PI3K-AKT-mTOR signaling pathways and cross-talk between these. Conclusion: The results suggest that the complex crosstalk between the mTOR, MAPK, and multiple interacting cell signaling cascades can promote GBC progression, and hence, mTOR–MAPK targeted treatment will be an attractive option. [ABSTRACT FROM AUTHOR]

Abstract Background Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease Methods Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein–protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored. Results Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan–Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data. Conclusions Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene’s clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.

Abstract Background Lung cancer has been a common malignant tumor with a leading cause of morbidity and mortality, current molecular targets are woefully lacking comparing to the highly progressive cancer. The study is designed to identify new prognostic predictors and potential gene targets based on bioinformatic analysis of Gene Expression Omnibus (GEO) database. Methods Four cDNA expression profiles GSE19188, GSE101929, GSE18842 and GSE33532 were chosen from GEO database to analyze the differently expressed genes (DEGs) between non-small cell lung cancer (NSCLC) and normal lung tissues. After the DEGs functions were analyzed, the protein–protein interaction network (PPI) of DEGs were constructed, and the core gene in the network which has high connectivity degree with other genes was identified. We analyzed the association of the gene with the development of NSCLC as well as its prognosis. Lastly we explored the conceivable signaling mechanism of the gene regulation during the development of NSCLC. Results A total of 92 up regulated and 214 down regulated DEGs were shared in four cDNA expression profiles. Based on their PPI network, TOP2A was connected with most of other genes and was selected for further analysis. Kaplan–Meier overall survival analysis (OS) revealed that TOP2A was associated with worse NSCLC patients survival. And both GEPIA analysis and immunohistochemistry experiment (IHC) confirmed that TOP2A was aberrant gain of expression in cancer comparing to normal tissues. The clinical significance of TOP2A and probable signaling pathways it involved in were further explored, and a positive correlation between TOP2A and TPX2 expression was found in lung cancer tissues. Conclusion Using bioinformatic analysis, we revealed that TOP2A could be adopted as a prognostic indicator of NSCLC and it potentially regulate cancer development through co-work with TPX2. However, more detailed experiments are needed to clarify its drug target role in clinical medical use.

Integrated analysis of accumulated data is an effective way to obtain reliable potential diagnostic molecular of cervical lymph node metastases (LNM) in papillary thyroid carcinoma (PTC). The benefits of prophylactic lymph node dissection (PLND) for these clinically node-negative (cN0) patients remained considerable controversies. Hence, elucidation of the mechanisms of LNM and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis of LNM in PTC patients. Up to date, advanced microarray and bioinformatics analysis have advanced an understanding of the molecular mechanisms of disease occurrence and development, which are necessary to explore genetic changes and identify potential diagnostic biomarkers. In present study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of LNM-related genes. Two publicly available microarray datasets GSE60542 and GSE129562 were available from Gene Expression Omnibus (GEO) database. Differentially expressed genes between clinically node-positive (cN1) and cN0 PTC samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Our results identified 48 differentially expressed genes (DEGs) genetically associated with LNM in PTC patients. Gene ontology (GO) analyses revealed the changes in the modules were mostly enriched in the regulation of MHC class II receptor activity, the immune receptor activity, and the peptide antigen binding. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs displayed the intestinal immune network for IgA production, staphylococcus aureus infection, and cell adhesion molecules (CAMs). To screen core genes related to LNM of PTC from the protein-protein interaction network, top 10 hub genes were identified with highest scores. Our results help us understand the exact mechanisms underlying the metastasis of cervical LNM in PTC tissues and pave an avenue for the progress of precise medicine for individual patients. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current clinical treatment situation is still challenging considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease Methods: Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein–protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function of the gene and signaling mechanism of gene regulating ccRCC development was preliminary explored. Results: Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed with ccRCC tissue microarray which contained 104 local samples and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan–Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data. Conclusions: Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which statistical significantly correlated with patients overall survival, and the gene's clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use. [ABSTRACT FROM AUTHOR]

Background: At present, laboratory blood tests to support major depressive disorder (MDD) diagnosis are not available. This study aimed to screen potential mRNAs for peripheral blood biomarkers and novel pathophysiology of MDD.Methods: The present study utilized public data from two mRNA microarray datasets to analyze the hub genes changes related to MDD. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs) were performed. Finally, some potential mRNA quality biomarkers for hub gene expression in blood were identified.Results: A total of 25 significantly co-upregulated DEGs and 98 co-downregulated DEGs were obtained from two datasets. The pathway enrichment analyses showed that co-upregulated genes were significantly enriched in the regulation of cell-matrix adhesion and mitochondrial membrane permeability which were involved in the apoptotic process. Co-downregulated genes were mainly involved in the neutrophil activation which in turn was involved in the immune response, degranulation and cell-mediated immunity, positive regulation of immune response, the Toll-like receptor signaling pathway, and the NOD-like receptor signaling pathway. From the PPI network, 14 hub genes were obtained. Among them, the subnetworks of PLCG1, BCL2A1, TLR8, FADD, and TLR4 screened out from our study have been shown to play a role in immune and inflammation responses.Discussion: The potential molecular mechanisms that have been identified simultaneously include innate immunity, neuroinflammation, and neurotrophic factors for synapse function and development.

Background: At present, laboratory blood tests to support major depressive disorder (MDD) diagnosis are not available. This study aimed to screen potential mRNAs for peripheral blood biomarkers and novel pathophysiology of MDD. Methods: The present study utilized public data from two mRNA microarray datasets to analyze the hub genes changes related to MDD. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs) were performed. Finally, some potential mRNA quality biomarkers for hub gene expression in blood were identified. Results: A total of 25 significantly co-upregulated DEGs and 98 co-downregulated DEGs were obtained from two datasets. The pathway enrichment analyses showed that co-upregulated genes were significantly enriched in the regulation of cell-matrix adhesion and mitochondrial membrane permeability which were involved in the apoptotic process. Co-downregulated genes were mainly involved in the neutrophil activation which in turn was involved in the immune response, degranulation and cell-mediated immunity, positive regulation of immune response, the Toll-like receptor signaling pathway, and the NOD-like receptor signaling pathway. From the PPI network, 14 hub genes were obtained. Among them, the subnetworks of PLCG1, BCL2A1, TLR8, FADD , and TLR4 s creened out from our study have been shown to play a role in immune and inflammation responses. Discussion: The potential molecular mechanisms that have been identified simultaneously include innate immunity, neuroinflammation, and neurotrophic factors for synapse function and development. [ABSTRACT FROM AUTHOR]

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

Background: Lung cancer has been a common malignant tumor with a leading cause of morbidity and mortality, current molecular targets are woefully lacking comparing to the highly progressive cancer. The study is designed to identify new prognostic predictors and potential gene targets based on bioinformatic analysis of Gene Expression Omnibus (GEO) database. Methods: Four cDNA expression profiles GSE19188, GSE101929, GSE18842 and GSE33532 were chosen from GEO database to analyze the differently expressed genes (DEGs) between non-small cell lung cancer (NSCLC) and normal lung tissues. After the DEGs functions were analyzed, the protein–protein interaction network (PPI) of DEGs were constructed, and the core gene in the network which has high connectivity degree with other genes was identified. We analyzed the association of the gene with the development of NSCLC as well as its prognosis. Lastly we explored the conceivable signaling mechanism of the gene regulation during the development of NSCLC. Results: A total of 92 up regulated and 214 down regulated DEGs were shared in four cDNA expression profiles. Based on their PPI network, TOP2A was connected with most of other genes and was selected for further analysis. Kaplan–Meier overall survival analysis (OS) revealed that TOP2A was associated with worse NSCLC patients survival. And both GEPIA analysis and immunohistochemistry experiment (IHC) confirmed that TOP2A was aberrant gain of expression in cancer comparing to normal tissues. The clinical significance of TOP2A and probable signaling pathways it involved in were further explored, and a positive correlation between TOP2A and TPX2 expression was found in lung cancer tissues. Conclusion: Using bioinformatic analysis, we revealed that TOP2A could be adopted as a prognostic indicator of NSCLC and it potentially regulate cancer development through co-work with TPX2. However, more detailed experiments are needed to clarify its drug target role in clinical medical use. [ABSTRACT FROM AUTHOR]