BACKGROUND: Colorectal cancer is a complex condition influenced by genetic mutations and environmental factors. Due to its intricate nature, the diagnosis and treatment of this condition require a comprehensive approach that considers individual circumstances. The study aimed to identify genes linked with colorectal cancer and their therapeutic agents from natural bioactive compounds.
METHODS: The significantly prognostic differentially expressed genes (DEGs) were screened out from NCBI Gene Expression Omnibus (GEO) datasets. A protein-protein interaction network was constructed using STRING Database, and key genes were identified using Network Analyzer and CytoNCA plugins within Cytoscape. Further analysis involved functional annotations, and biological pathways analysis, SRC mechanism to uncover the role of SRC in CRC. Additionally, we performed virtual screening and molecular docking, Physiochemical property analysis along with MD simulation study to propose suitable natural compounds for promising therapeutic targets.
RESULTS: The study conducted differential gene expression analysis, identifying 3621 statistically significant genes, with 1467 upregulated and 2154 downregulated. The top ten genes with the highest degree, betweenness centrality, and closeness centrality in the PPI network were selected as key genes. The SRC gene was found to have the highest degree and closeness centrality. Functional annotation and pathway analysis of key genes with a specific focus on the SRC mechanism revealed that the SRC\'s role in activating the RAS-RAF-MEK-ERK and Wnt/β-catenin pathways in CRC cells, promoting proliferation and invasion. Molecular modelling of SRC led to the screening of phyto-compounds from tropical fruits, with Rutinexhibiting a higher docking score compared to FDA-approved anticancer drugs. MD simulations over 100 ns and the post-MD analysis i.e. RMSD, SASA, RMSF, FEL, RG, Hydrogen bond, PCA, and MMPBSA, comprehended the stable and robust interactions of a protein-ligand complex. These findings suggest Rutin\'s potential as a potent natural molecule for treating CRC. The study concludes that SRC plays a pivotal role in CRC, influencing cellular processes critical to cancer development and Rutin has been found to be a promising SRC inhibitor, suggesting a potential alternative therapeutic strategy for CRC. The consistent molecular interactions of Rutin necessitate further validation through wet lab experiments, offering hope for individuals affected by CRC.

BACKGROUND: It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.
METHODS: The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.
RESULTS: The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.
CONCLUSIONS: The implemented workflow could be used for other multifactorial diseases.

Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored.
The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated.
By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels.
Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

Introduction: Photodynamic therapy (PDT) is applied as an efficient method for preventing the progress of cancers. Light and a photosensitive compound which is known as photosensitizer (PS) are the main parts of PDT. In the present study, molecular events after using PDT in the presence of a super lethal dose of a PS were assessed via protein-protein interaction (PPI) analysis. Methods: Data were extracted from Gene Expression Omnibus (GEO). The gene expression profiles of the treated human Sk-Cha1 cells via PDT were compared with the control cells. Expressed change analysis and PPI network analysis were administrated via Cytoscape software v 3.7.2 to find the critical differentially expressed genes (DEGs). Regulatory relationships between the central DEGs were evaluated and the highlighted genes were identified. Results: The significant amounts of gene expression values were grouped and a few DEGs characterized by tremendously expressed values were identified. EGFR, CANX, HSPA5, MYC, JUN, ITGB1, APP, and CDH1 were highlighted as hub-bottleneck DEGs. EGFR, CDH1, and JUN appeared as a set of SEGs, which play a crucial role in response to PDT in the treated Sk-Cha1 cells. Conclusion: In conclusion, regulatory relationships between EGFR, CDH1, and JUN, which have an effect on the regulation of cellular survival, differentiation, and proliferation, were highlighted in the present investigation.

This study aimed to identify genes related to the degree of immune cell infiltration in head and neck squamous cell carcinoma (HNSCC), explore their new biological functions, and evaluate their diagnostic and prognostic value in HNSCC.
Transcriptomic data from The Cancer Genome Atlas (TCGA) HNSCC dataset was used to screen differentially expressed genes between tumors and normal tissues, followed by weighted correlation network analysis (WGCNA) to identify immune-related modules. Differential gene expression, immune cell infiltration, and survival analyses were performed to screen key genes. The expression of these key genes was validated in Oncomine and gene expression omnibus (GEO) datasets and by immunohistochemistry (IHC).
1869 and 1578 genes were significantly upregulated and downregulated in HNSCC. WGCNA showed that the brown module was associated with the most significant number of immune-related genes. PPI network analysis demonstrated that PPL, SCEL, KRT4, KRT24, KRT78, KRT13, SPRR3, TGM3, CRCT1, and CRNN were key components in the brown module. Furthermore, the expression levels of KRT4, KRT78, KRT13, and SPRR3 in HNSCC correlated with infiltration levels of CD8+ T cells and macrophages. Survival analyses revealed that the expression of KRT78, KRT13, and SPRR3 in HNSCC correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78 (p < .001), and SPRR3 (p = .022) protein expression levels in HNSCC were significantly lower than in normal tissues. Analysis of GSE65858 and GSE41613 datasets showed that a worse OS was associated with low expression of KRT78 (p = .0086, and p = .005) and SPRR3 (p = .017, and p = .02).
Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.

Lung cancer is a severe health problem that affects more men than women around the world. The goal of this study was to identify the biomarker hub genes for lung cancer in order to ascertain the biological pathway and protein- protein interaction networks. The microarray datasets GSE80796, GSE68571, GSE118370 and GSE43458 were retrieved from the GEO database and were analysed using GEO2R. STRING, Cytoscape, and cytoHubba were used to construct the PPI network and hub genes. GEPIA was used to obtain the overall survival and expression level in LUAD/LUSC and normal tissue. The MTT assay was used to examine antiproliferative activity. PI staining was used to determine the cell cycle arrest. qPCR was used to analyse gene expressions. The datasets revealed a total of 401 common DEGs, with 258 up-regulated genes and 143 down-regulated genes. Further, in-vitro study of gallic acid cytotoxic effect in human lung cancer cell line A549 indicated that gallic acid dramatically suppressed cell growth in A549 cells. Gallic acid also, significantly promoted programmed cell death by halting cells in the G0/G1 phase of the cell cycle. Taken together, our study indicated that gallic acid is a promising natural STAT1 inhibitor as it hindered lung cancer progression by inducing cell cycle arrest and apoptosis which can be employed to increase the therapeutic efficacy of existing lung cancer treatments and to improve overall patient survival.Communicated by Ramaswamy H. Sarma.

We aimed to explore mechanisms of development and progression of polycystic ovary syndrome (PCOS).
The microRNA expression microarray GSE37914 and gene expression profiles GSE43264 and GSE98421 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEmiRNAs) and genes (DEGs) were screened using Limma package. Then, the DEGs and DEmiRNAs were combined to use for the subsequent analysis, including the functional enrichment analysis, protein-protein interaction (PPI) network and module analysis, drug-gene interaction network analysis, and DEmiRNAs-DEGs interactive network construction.
A total of 26 DEmiRNAs and 80 DEGs were screened. The PPI network contained 68 nodes and 259 interactions. A significant clustering module with 8 nodes and 25 interactions was obtained. Three PCOS-related overlapping pathways were obtained based on PPI-degree top10 and module genes, including prion diseases, Staphylococcus aureus infection, and Chagas disease (American trypanosomiasis). A total of 44 drug-gene interaction pairs were obtained, which included 2 up-regulated genes (LDLR and VCAM1), 4 down-regulated genes (C1QA, C1QB, IL6 and ACAN) and 26 small molecules drugs. A total of 52 nodes and 57 interactions were obtained in the DEmiRNA-DEGs regulatory network, LDLR was regulated by miR-152-3p, miR-1207-5p, miR-378a-5p and miR-150-5p.
Our research has identified several key genes and pathways related to PCOS. These results can improve our understanding of PCOS and provide new basis for drug target research.

Host genetic factors have been shown to play an important role in SARS-CoV-2 infection and the course of Covid-19 disease. The genetic contributions of common variants influencing Covid-19 susceptibility and severity have been extensively studied in diverse populations. However, the studies of rare genetic defects arising from inborn errors of immunity (IEI) are relatively few, especially in the Chinese population. To fill this gap, we used a deeply sequenced dataset of nearly 500 patients, all of Chinese descent, to investigate putative functional rare variants. Specifically, we annotated rare variants in our call set and selected likely deleterious missense (LDM) and high-confidence predicted loss-of-function (HC-pLoF) variants. Further, we analyzed LDM and HC-pLoF variants between non-severe and severe Covid-19 patients by (a) performing gene- and pathway-level association analyses, (b) testing the number of mutations in previously reported genes mapped from LDM and HC-pLoF variants, and (c) uncovering candidate genes via protein-protein interaction (PPI) network analysis of Covid-19-related genes and genes defined from LDM and HC-pLoF variants. From our analyses, we found that (a) pathways Tuberculosis (hsa:05152), Primary Immunodeficiency (hsa:05340), and Influenza A (hsa:05164) showed significant enrichment in severe patients compared to the non-severe ones, (b) HC-pLoF mutations were enriched in Covid-19-related genes in severe patients, and (c) several candidate genes, such as IL12RB1, TBK1, TLR3, and IFNGR2, are uncovered by PPI network analysis and worth further investigation. These regions generally play an essential role in regulating antiviral innate immunity responses to foreign pathogens and in responding to many inflammatory diseases. We believe that our identified candidate genes/pathways can be potentially used as Covid-19 diagnostic markers and help distinguish patients at higher risk.

BACKGROUND: Accurate diagnosis of major depressive disorder (MDD) remains difficult, and one of the key challenges in diagnosing MDD is the lack of reliable diagnostic biomarkers. The objective of this study was to explore gene networks and identify potential biomarkers for MDD.
METHODS: In the present study, we performed a comprehensive analysis of the mRNA expression profiles using blood samples of four patients with MDD and four controls by RNA sequencing. Differentially expressed genes (DEGs) were screened, and functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. All DEGs were inputted to the STRING database to build a PPI network, and the top 10 hub genes were screened using the cytoHubba plugin of the Cytoscape software. The relative expression of 10 key genes was identified by quantitative real-time polymerase chain reaction (qRT-PCR) of blood samples from 50 MDD patients and 50 controls. Plasma levels of SQSTM1 and TNFα were measured using an enzyme-linked immunosorbent assay in blood samples of 44 MDD patients and 44 controls. A sucrose preference test was used to evaluate depression-like behavior in chronic unpredictable mild stress (CUMS) model rats. Immunofluorescence assay and western blotting were performed to study the expression of proteins in the brain samples of CUMS model rats.
RESULTS: We identified 247 DEGs that were closely associated with MDD. Gene ontology analyses suggested that the DEGs were mainly enriched in negative regulation of transcription by RNA polymerase II promoter, cytoplasm, and protein binding. Moreover, Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that the DEGs were significantly enriched in the MAPK signaling pathway. Ten hub genes were screened through the PPI network, and qRT-PCR assay revealed that one and six genes were downregulated and upregulated, respectively; however, SMARCA2, PPP3CB, and RAB5C were not detected. Pathway enrichment analysis for the 10 genes showed that the mTOR signaling pathway was also enriched. A strong positive correlation was observed between SQSTM1 and TNFα protein levels in patients with MDD. LC3 II and SQSTM1 protein levels were increased in the CUMS rat model; however, p-mTOR protein levels were decreased. The sucrose preference values decreased in the CUMS rat model.
CONCLUSIONS: We identified 247 DEGs and constructed an MDD-specific network; thereafter, 10 hub genes were selected for further analysis. Our results provide novel insights into the pathogenesis of MDD. Moreover, SQSTM1, which is related to autophagy and inflammatory reactions, may play a key role in MDD. SQSTM1 may be used as a promising therapeutic target in MDD; additionally, more molecular mechanisms have been suggested that should be focused on in future in vivo and in vitro studies.

Wound repair is a multistep process which involves coordination of multiple molecular players from different cell types and pathways. Though the cellular processes that are taking place in order to repair damage is already known, molecular players involved in crucial pathways are still scarce. In this regard, the present study intends to uncover crucial players that are involved in the central repair events through proteomics approach which included 2-D GE and LC-MS/MS using Caenorhabditis elegans wound model. Initial gel-based 2-D GE and following protein-protein interaction (PPI) network analyses revealed active role of calcium signaling, acetylcholine transport and serotonergic neurotransmitter pathways. Further, gel-free LC-MS/MS and following PPI network analyses revealed the incidence of actin nucleation at the initial hours immediately after injury. Further by visualizing the PPI network and the interacting players, pink-1, a mitochondrial Serine/threonine-protein kinase which is known to regulate mitochondrial dynamics, was found to be the central player in facilitating the mitochondrial fission and its role was further verified using qPCR analysis and pink-1 transgenic worms. Overall, the study delivers new insights from crucial regulatory pathways and central players involved in wound repair using high throughput proteomic approaches and the mass spectrometry Data (PXD024629/PXD024744) are available via ProteomeXchange. SIGNIFICANCE.