Protein-protein Interaction Network Analysis Research Articles

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation

ObjectiveAlthough programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. MethodsWe acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. ResultsWe harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. ConclusionInnovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

Exploring the potential mechanisms of Danshen against COVID-19 via network pharmacology analysis and molecular docking

Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein–Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs.

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout

BackgroundGout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis.MethodsThe study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout.ResultsWe identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A.ConclusionAccording to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition.

A molecular subtyping associated with the cGAS-STING pathway provides novel perspectives on the treatment of ulcerative colitis

Ulcerative colitis (UC) is characterized by an abnormal immune response, and the pathogenesis lacks clear understanding. The cGAS-STING pathway is an innate immune signaling pathway that plays a significant role in various pathophysiological processes. However, the role of the cGAS-STING pathway in UC remains largely unclear. In this study, we obtained transcriptome sequencing data from multiple publicly available databases. cGAS-STING related genes were obtained through literature search, and differentially expressed genes (DEGs) were analyzed using R package limma. Hub genes were identified through protein–protein interaction (PPI) network analysis and module construction. The ConsensuClusterPlus package was utilized to identify molecular subtypes based on hub genes. The therapeutic response, immune microenvironment, and biological pathways of subtypes were further investigated. A total of 18 DEGs were found in UC patients. We further identified IFI16, MB21D1 (CGAS), TMEM173 (STING) and TBK1 as the hub genes. These genes are highly expressed in UC. IFI16 exhibited the highest diagnostic value and predictive value for response to anti-TNF therapy. The expression level of IFI16 was higher in non-responders to anti-TNF therapy. Furthermore, a cluster analysis based on genes related to the cGAS-STING pathway revealed that patients with higher gene expression exhibited elevated immune burden and inflammation levels. This study is a pioneering analysis of cGAS-STING pathway-related genes in UC. These findings provide new insights for the diagnosis of UC and the prediction of therapeutic response.

Bioinformatics and experimental approach identify lipocalin 2 as a diagnostic and prognostic indicator for lung adenocarcinoma

Backgroundlipocalin 2 (LCN2) is a secreted glycoprotein that plays key roles in tumorigenesis and progression. Interestingly, LCN2 appears to have a contradictory function in developing lung adenocarcinoma (LUAD). Thus, we intend to explore the role of LCN2 in LUAD through bioinformatics and experimental validation. MethodsLCN2 expression of LUAD was investigated in the TCGA, TIMER and HPA databases. The relationship between LCN2 and prognosis was investigated by KM plotter, TCGA and GEO databases. GO, KEGG and protein-protein interactions network analysis were conducted to investigate the potential mechanism of LCN2. The relevance of LCN2 to cancer-immune infiltrates was investigated in the TCGA and TIMER databases. Quantitative reverse transcription PCR, western blot and enzyme-linked immunosorbent assay were performed to identify the expression level of LCN2 in cells and serum samples. The CCK-8, wound healing and transwell assay were used to confirm the effect of LCN2 on cell proliferation, migration and invasion in LUAD. The receiver operating characteristic curve was utilized to assess the diagnostic efficiency of LCN2 further. ResultsLCN2 expression was significantly upregulated in LUAD (P < 0.05), and was correlated with the clinical stage, tumor size, lymph node metastasis and distant metastasis (P < 0.05). There was a high correlation between high LCN2 and worse prognosis in LUAD. Functional network analysis suggested that LCN2 was associated with multiple signal pathways in cancers, such as JAK-STAT, TNF, NF-κB, HIF-1 and PI3K-Akt signal pathways. In addition, the knockdown of LCN2 significantly inhibited the ability of cell proliferation, migration and invasion. Immune infiltration analysis indicated that LCN2 is associated with multiple immune cell infiltration. Notably, LCN2 demonstrated high diagnostic efficiency for LUAD (AUC = 0.818, P < 0.05), especially for stage III-IV patients could reach 0.895. ConclusionsLCN2 as an oncogenic glycoprotein promotes the cancer progression related to immune infiltrates, which might be a potential diagnostic and prognostic marker in LUAD.

Proteomics analysis of coronary atherosclerotic heart disease with different Traditional Chinese Medicine syndrome types before and after percutaneous coronary intervention.

To investigate the underlying protein molecular mechanisms of "Qi stagnation and blood stasis syndrome" (QS) and "Qi deficiency and blood stasis syndrome" (QD), as two subtypes of coronary artery disease (CAD) in Traditional Chinese Medicine (TCM), following percutaneous coronary intervention (PCI). In this study, a total of 227 CAD patients with QS and 211 CAD patients with QD were enrolled; all participants underwent PCI. Label-free quantification proteomics were employed to analyze the changes in serum in two subtypes of CAD patients before and 6 months after PCI, aiming to elucidate the intervention mechanism of PCI in treating CAD characterized by two different TCM syndromes. Biochemical analysis revealed significant changes in tumor necrosis factor-α, high density lipoprotein cholesterol, blood stasis clinical symptoms observation, and Gensini levels in both patient groups post-PCI; Proteomic analysis identified 79 and 95 differentially expressed proteins in the QS and QD patient groups, respectively, compared to their control groups. complement C8 alpha chain, complement factor H, apolipoprotein H, apolipoprotein B, plasminogen, carbonic anhydrase 2, and complement factor I were altered in both comparison groups. Furthermore, enrichment analysis demonstrated that cell adhesion and connectivity-related processes underwent changes in QS patients post-PCI, whereas lipid metabolism-related pathways, including the peroxisome proliferator-activated receptor signaling pathway and extracellular matrix receptor interaction, underwent changes in the QD group. The protein-protein interaction network analysis further enriched 52 node proteins, including apolipoprotein B, lipoprotein (a), complement C5, apolipoprotein A4, complement C8 alpha chain, complement C8 beta chain, complement C8 gamma chain, apolipoprotein H, apolipoprotein A-Ⅱ, albumin, complement C4-B, apolipoprotein C3, among others. The functional network of these proteins is posited to contribute to the pathophysiology of CAD characterized by TCM syndromes. The current quantitative proteomic study has preliminarily identified biomarkers of CAD in different TCM subtypes treated with PCI, potentially laying the groundwork for understanding the protein profiles associated with the treatment of various TCM subtypes of CAD.

Human dermal fibroblast-derived secretory proteins for regulating nerve restoration: A bioinformatic approach.

Human dermal fibroblasts secrete diverse proteins that regulate wound repair and tissue regeneration. In this study, dermal fibroblast-conditioned medium (DFCM) proteins potentially regulating nerve restoration were bioinformatically selected among the 337 protein lists identified by quantitative liquid chromatography-tandem mass spectrometry. Using these proteins, protein-protein interaction network analysis was conducted. In addition, the roles of DFCM proteins were reviewed according to their protein classifications. Gene Ontology protein classification categorized these 57 DFCM proteins into various classes, including protein-binding activity modulator (N=11), cytoskeletal protein (N=8), extracellular matrix protein (N=6), metabolite interconversion enzyme (N=5), chaperone (N=4), scaffold/adapter protein (N=4), calcium-binding protein (N=3), cell adhesion molecule (N=2), intercellular signal molecule (N=2), protein modifying enzyme (N=2), transfer/carrier protein (N=2), membrane traffic protein (N=1), translational protein (N=1), and unclassified proteins (N=6). Further protein-protein interaction network analysis of 57 proteins revealed significant interactions among the proteins that varied according to the settings of confidence score. Our bioinformatic analysis demonstrated that DFCM contains many secretory proteins that form significant protein-protein interaction networks crucial for regulating nerve restoration. These findings underscore DFCM proteins' critical roles in various nerve restoration stages during the wound repair process.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

BackgroundTranscriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.MethodsWe first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.ResultsWe identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.ConclusionWe applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Investigation of the Molecular Mechanisms Underlying the Therapeutic Effect of Perilla frutescens L. Essential Oil on Acute Lung Injury Using Gas Chromatography-Mass Spectrometry and Network Pharmacology.

The present study aimed to investigate the molecular mechanism through which Perilla essential oil treats acute lung injury (ALI) through network pharmacology, molecular docking, and in vitro assays. Relevant ALI targets of the active ingredients of Perilla essential oil were predicted using the SwissTargetPrediction database and meta TarFisher database. These ALI targets were then screened using GeneCards and DisGeNET, and differentially expressed ALI target genes were identified using the Gene Expression Omnibus (GEO) database. Next, key targets were enriched using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction network analysis was performed to obtain targets with the highest degree values for molecular docking with Perilla essential oil active ingredients. For in vitro experiments, lipopolysaccharide (LPS) was used to induce an ALI inflammation model using RAW264.7 cells. The model cells were then treated with Perilla essential oil to detect the protein expression levels of vascular endothelial factor (NO), tumor necrosis factor (TNF-α), and p65 nuclear transcription factor in them. Sixty-eight key targets of Perilla oil were identified for the treatment of ALI. These targets were found to be involved in biological processes related to peptides, response to lipopolysaccharides, the positive regulation of cytokine production, etc., using GO. The signaling pathways found to be associated with the targets included the AGE-RAGE signaling pathway in diabetic complications, the NF-kappa B signaling pathway, and small cell lung cancer and other inflammatory signaling pathways. The five key targets that showed good binding activity with Perilla oil active ingredients included TNF, RELA, PARP1, PTGS2, and IRAK4. In vitro assays showed that Perilla essential oil could significantly reduce NO and TNF-α levels and inhibit the phosphorylation of nuclear transcription factor P65, thus inhibiting the activation of NF-κB signaling pathway. Conclusion Perilla essential oil can play a role in the treatment of ALI by inhibiting the activation of the NF-κB signaling pathway and preventing an excessive inflammatory response. This study thus provides a reference for the in-depth study of the mechanisms through which Perilla essential oil treats ALI.

Identification of Immune-Related Gene Signature in Schizophrenia.

Schizophrenia (SCZ) is a type of psychiatric disorder characterized by multiple symptoms. Our aim is to decipher the relevant mechanisms of immune-related gene signatures in SCZ. The SCZ dataset and its associated immunoregulatory genes were retrieved using Gene Expression Omnibus (GEO) and single-sample gene set enrichment analysis (ssGSEA). Co-expressed gene modules were determined through weighted gene correlation network analysis (WGCNA). To elucidate the functional characteristics of these clusters, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used. Additionally, gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to identify enriched pathways for the immune subgroups. A protein-protein interaction (PPI) network analysis was performed to identify core genes relevant to SCZ. A significantly higher immune score was observed in SCZ compared to control samples. Seven distinct gene modules were identified, with genes highlighted in green selected for further analysis. Using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, degrees of immune cell adhesion and accumulation related to 22 different immune cell types were calculated. Significantly enriched bioprocesses concerning the immunoregulatory genes with differential expressions included interferon-beta, IgG binding, and response to interferon-gamma, according to GO and KEGG analyses. Eleven hub genes related to immune infiltration emerged as key players among the three top-ranked GO terms. This study underscores the involvement of immunoregulatory reactions in SCZ development. Eleven immune-related genes (IFITM1 (interferon induced transmembrane protein 1), GBP1 (guanylate binding protein 1), BST2 (bone marrow stromal cell antigen 2), IFITM3 (interferon induced transmembrane protein 3), GBP2 (guanylate binding protein 2), CD44 (CD44 molecule), FCER1G (Fc epsilon receptor Ig), HLA-DRA (major histocompatibility complex, class II, DR alpha), FCGR2A (Fc gamma receptor IIa), IFI16 (interferon gamma inducible protein 16), and FCGR3B (Fc gamma receptor IIIb)) were identified as hub genes, representing potential biomarkers and therapeutic targets associated with the immune response in SCZ patients.

Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC. Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC. In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors. The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Bioinformatics‐based identification of hepatocellular carcinoma‐associated hub genes and assessment of the restorative effect of tannic acid in rat liver exposed to monosodium glutamate

AbstractBackgroundHepatocellular carcinoma (HCC) is the most common type of primary liver cancer, occurring mostly in individuals with chronic liver disease, but biomarkers for therapeutic diagnosis and prognosis are lacking. This study aimed to investigate the possible effect of the common food additive monosodium glutamate (MSG) and tannic acid (TA), a phenolic compound, on the key molecular actors responsible for HCC development.MethodsEight HCC‐related public microarray datasets (GSE84005, GSE14520, GSE25097, GSE57958, GSE22058, GSE84402, GSE54238, and GSE36376) were extracted from the gene expression omnibus (GEO) database and analyzed to identify differentially expressed genes (DEGs). To make sense of the identified biological data and to identify hub genes, protein–protein interaction (PPI) network and enrichment analysis were performed. The mRNA expression profiles of the identified hub genes, expression changes in different stages of HCC, and their prognostic significances in HCC were determined using GEPIA, UALCAN, and Kaplan–Meier Plotter databases, respectively. Finally, mRNA expression changes of identified hub genes in the liver tissues of rats treated with MSG and TA were measured by the quantitative real‐time PCR (qPCR) method.ResultsTwo up‐regulated (AURKA and CCNB2) and two down‐regulated (F9 and CYP2E1) genes were identified between the HCC tumor and adjacent non‐tumor liver tissue samples. qPCR results showed that the mRNA expression of up‐regulated DEGs involved in HCC development increased significantly in rat liver tissues exposed to MSG, while this increase was remarkably suppressed by TA treatment. It was observed that the mRNA expressions of down‐regulated DEGs involved in HCC development decreased markedly in the presence of MSG, while this decrease was alleviated with TA.ConclusionOur results provide new insights into pivotal molecular candidates that should be focused on in future in vivo and in vitro HCC research. Moreover, MSG may play a crucial role in HCC development and progression and TA may be used as a favorable restorative agent in HCC.

Clinical efficacy and gene chip expression analysis of Shenzhu Guanxin recipe granules in patients with intermediate coronary lesions.

To evaluate the clinical efficacy and safety of Shenzhu Guanxin recipe granules (, SGR) in treating patients with intermediate coronary lesions (ICL), and to investigate the potential mechanism though a transcriptome sequencing approach. ICL patients with Qi deficiency and phlegm stasis were adopted and randomly assigned to a case group or a control by random number generator in a 1:1 randomization ratio to evaluate the clinical efficacy. There was no significant difference between the two groups in coronary computed tomography angiography related indexes in the two groups before and after intervention. Through the gene chip expression analysis, it is finally concluded that there are 355 differential mRNAs (190 up-regulated genes and 165 down regulated genes) when compared the SGR group and placebo group. Through protein-protein interaction network analysis of differentially expressed genes, 10 hub genes were finally obtained: CACNA2D2, CACNA2D3, DNAJC6, FGF12, SGSM2, CACNA1G, LRP6, KIF25, OXTR, UPB1. SGR combined with Western Medicine can be safely used to treat ICL patients with Qi deficiency and phlegm stasis. The possible mechanism of action and relevant gene loci and pathway were proposed.

Phytol and α-Bisabolol Synergy Induces Autophagy and Apoptosis in A549 Cells and Additional Molecular Insights through Comprehensive Proteome Analysis via Nano LC-MS/MS.

Non-Small Cell Lung Cancer (NSCLC) is a malignancy with a significant prevalence and aggressive nature, posing a considerable challenge in terms of therapeutic interventions. Autophagy and apoptosis, two intricate cellular processes, are integral to NSCLC pathophysiology, each affecting the other through shared signaling pathways. Phytol (Phy) and α-bisabolol (Bis) have shown promise as potential anticancer agents individually, but their combined effects in NSCLC have not been extensively investigated. The present study was to examine the synergistic impact of Phy and Bis on NSCLC cells, particularly in the context of autophagy modulation, and to elucidate the resulting differential protein expression using LCMS/ MS analysis. The A549 cell lines were subjected to the patented effective concentration of Phy and Bis, and subsequently, the viability of the cells was evaluated utilizing the MTT assay. The present study utilized real-time PCR analysis to assess the expression levels of crucial apoptotic genes, specifically Bcl-2, Bax, and Caspase-9, as well as autophagy-related genes, including Beclin-1, SQSTM1, Ulk1, and LC3B. The confirmation of autophagy marker expression (Beclin-1, LC3B) and the autophagy-regulating protein SQSTM1 was achieved through the utilization of Western blot analysis. Differentially expressed proteins were found using LC-MS/MS analysis. The combination of Phy and Bis demonstrated significant inhibition of NSCLC cell growth, indicating their synergistic effect. Real-time PCR analysis revealed a shift towards apoptosis, with downregulation of Bcl-2 and upregulation of Bax and Caspase-9, suggesting a shift towards apoptosis. Genes associated with autophagy regulation, including Beclin-1, SQSTM1 (p62), Ulk1, and LC3B, showed significant upregulation, indicating potential induction of autophagy. Western blot analysis confirmed increased expression of autophagy markers, such as Beclin-1 and LC3B, while the autophagy-regulating protein SQSTM1 exhibited a significant decrease. LC-MS/MS analysis revealed differential expression of 861 proteins, reflecting the modulation of cellular processes. Protein-protein interaction network analysis highlighted key proteins involved in apoptotic and autophagic pathways, including STOML2, YWHAB, POX2, B2M, CDA, CAPN2, TXN, ECHS1, PEBP1, PFN1, CDC42, TUBB1, HSPB1, PXN, FGF2, and BAG3, emphasizing their crucial roles. Additionally, PANTHER pathway analysis uncovered enriched pathways associated with the differentially expressed proteins, revealing their involvement in a diverse range of biological processes, encompassing cell signaling, metabolism, and cellular stress responses. The combined treatment of Phy and Bis exerts a synergistic inhibitory effect on NSCLC cell growth, mediated through the interplay of apoptosis and autophagy. The differential protein expression observed, along with the identified proteins and enriched pathways, provides valuable insights into the underlying molecular mechanisms. These findings offer a foundation for further exploration of the therapeutic potential of Phy and Bis in the management of NSCLC.

Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors.

Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients. Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein-protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes. We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations. The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.

Spatial transcriptome analysis of amelanotic acral melanoma reveals distant molecular determinants compared to pigmented acral melanoma.

Amelanotic acral melanoma (AAM) is a rare type of acral melanoma associated with poor prognosis. We aimed to investigate the transcriptomic differences between AAM and pigmented acral melanoma (PAM). The differences in spatially resolved transcriptome profiles of 9 AAM patients with 29 regions of interest (ROIs) and 11 PAM patients with 46 ROIs were investigated using S100b and CD3 morphology markers. In S100b-positive tumor cell areas, we detected 11 upregulated differentially expressed genes (DEGs), including chaperone/ubiquitin-associated DEGs, and 82 downregulated DEGs, including human leukocyte antigen, in AAMs compared with PAMs. Protein-protein interaction network and pathway analyses revealed significant enrichment of dysregulated translational and nonsense-mediated decay pathways but significant decreases in antigen processing and presentation, interferon signaling, and melanin biosynthesis pathways in S100b-positive ROIs of AAMs compared with those of PAMs. In tumor-associated immune cell areas, the numbers of CD8 T cells (p = 0.044) and M1 macrophages (p = 0.014) were significantly decreased, whereas those of monocytes (p = 0.045) and endothelial cells (p = 0.04) were increased in AAMs compared with those in PAMs. In conclusion, these findings could widen our understanding of the biological differences between AAMs and PAMs that might result in a different clinical course.

Deciphering FFAR4 Mediated Functional Pathways and Potential Drug Targets in Diabetes Mellitus: An Integrated Protein Interaction Network Analysis and Kinetic Simulation

Deciphering FFAR4 Mediated Functional Pathways and Potential Drug Targets in Diabetes Mellitus: An Integrated Protein Interaction Network Analysis and Kinetic Simulation

Uncovering Druggable Targets in Aortic Dissection: An Association Study Integrating Mendelian Randomization, pQTL, and Protein-Protein Interaction Network.

Aortic dissection (AD) is a life-threatening acute aortic syndrome. There are limitations and challenges in the discovery and application of biomarkers and drug targets for AD. Mendelian randomization (MR) analysis is a reliable analytical method to identify effective therapeutic targets. We aimed to identify novel therapeutic targets for AD and investigate their potential side-effects based on MR analysis. Data from protein quantitative trait loci (pQTLs) were used for MR analyses to identify potential therapeutic targets. We probed druggable proteins involved in the pathogenesis of aortic dissection from deCODE. In this study, a two-sample MR analysis was conducted, with druggable proteins as the exposure factor and data on genome-wide association studies (GWAS) of AD as the outcome. After conducting a two-sample MR, summary data-based Mendelian randomization (SMR) analysis and colocalization analysis were performed. A protein-protein interaction (PPI) network was also constructed to delve into the interactions between identified proteins. After MR analysis and the Steiger test, we identified five proteins as potential therapeutic targets for AD. SMR analysis and colocalization analysis also confirmed our findings. Finally, we identified ASPN (OR = 1.36, 95% CI: 1.20, 1.54, p = 4.22 × 10-5) and SPOCK2 (OR = 0.57, 95% CI: 0.41, 0.78, p = 4.52 × 10-4) as the core therapeutic targets. Through PPI network analysis, we identified six druggable targets, enabling the subsequent identification of six promising drugs from DrugBank for treating AD. This discovery of specific proteins as novel therapeutic targets represents a significant advancement in AD treatment. These findings provide more effective treatment options for AD.

Gene expression and immune infiltration analysis comparing lesioned and preserved subchondral bone in osteoarthritis.

Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein-protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand-receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1β were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

The Therapeutic Target of IBD and the Mechanism of Dipyridamole in Treating IBD Explored by Geo Gene Chips, Network Pharmacology, and Molecular Docking.

Inflammatory Bowel Disease (IBD) is a refractory disease with repeated attacks, and there is no accurate treatment target at present. Dipyridamole, a phosphodiesterase (PDE) inhibitor, has been proven to be an effective treatment for IBD in a pilot study. This study explored the therapeutic target of IBD and the pharmacological mechanism of dipyridamole for the treatment of IBD. The candidate targets of dipyridamole were obtained by searching the pharmMapper online server and Swiss Target Prediction Database. The IBD-related targets were selected from four GEO chips and three databases, including Genecards, DisGeNET, and TTD database. A protein-protein interaction (PPI) network was constructed, and the core targets were identified according to the topological structure. KEGG and GO enrichment analysis and BioGPS location were performed. Finally, molecular docking was used to verify dipyridamole and the hub targets. We obtained 112 up-regulated genes and 157 down-regulated genes, as well as 105 composite targets of Dipyridamole-IBD. Through the PPI network analysis, we obtained the 7 hub targets, including SRC, EGFR, MAPK1, MAPK14, MAPK8, PTPN11, and LCK. The BioGPS showed that these genes were highly expressed in the immune system, digestive system, and endocrine system. In addition, the 7 hub targets had good intermolecular interactions with dipyridamole. The therapeutic effect of dipyridamole on IBD may involve immune system activation and regulation of inflammatory reactions involved in the regulation of extracellular matrix, perinuclear region of cytoplasm, protein kinase binding, and positive regulation of programmed cell death through cancer pathway (proteoglycans in cancer), lipid metabolism, Ras signaling pathway, MAPK signaling pathway, PI3K-AKT signaling pathway, Th17 cell differentiation, and other cellular and innate immune signaling pathways. This study predicted the therapeutic target of IBD and the molecular mechanism of dipyridamole in treating IBD, providing a new direction for the treatment of IBD and a theoretical basis for further research.