Dendritic cell-related hub genes in head-and-neck squamous cell carcinoma: implications for prognosis and immunotherapy.

Background: In the context of head-and-neck squamous cell carcinoma (HNSCC), dendritic cells (DCs) assume pivotal responsibilities, acting as architects of antigen presentation and conductors of immune checkpoint modulation. In this study, we aimed to identify hub genes associated with DCs in HNSCC and explore their prognostic significance and implications for immunotherapy. Methods: Integrated clinical datasets from The Cancer Genome Atlas (TCGA)-HNSCC and GSE65858 cohorts underwent meticulous analysis. Employing weighted gene co-expression network analysis (WGCNA), we delineated candidate genes pertinent to DCs. Through the application of random survival forest and least absolute shrinkage and selection operator (LASSO) Cox's regression, we derived key genes of significance. Lisa (epigenetic Landscape In Silico deletion Analysis and the second descendent of MARGE) highlighted transcription factors, with Dual-luciferase assays confirming their regulatory role. Furthermore, immunotherapeutic sensitivity was assessed utilizing the Tumor Immune Dysfunction and Exclusion online tool. Results: This study illuminated the functional intricacies of HNSCC DC subsets to tailor innovative therapeutic strategies. We leveraged clinical data from the TCGA-HNSCC and GSE65858 cohorts. We subjected the data to advanced analysis, including WGCNA, which revealed 222 DC-related candidate genes. Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox's regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival. Differential gene expression analysis between ACP2 + and ACP2 - DC cells revealed 208 differential expressed genes. Lisa analysis identified the top five significant transcription factors as STAT1, SPI1, SMAD1, CEBPB, and IRF1. The correlation between STAT1 and ACP2 was confirmed through quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Dual-luciferase assays in HEK293T cells. Additionally, TP53 and FAT1 mutations were more common in high-risk DC subgroups. Importantly, the sensitivity to immunotherapy differed among the risk clusters. The low-risk cohorts were anticipated to exhibit favorable responses to immunotherapy, marked by heightened expressions of immune system-related markers. In contrast, the high-risk group displayed augmented proportions of immunosuppressive cells, suggesting a less conducive environment for immunotherapeutic interventions. Conclusions: Our research may yield a robust DC-based prognostic system for HNSCC; this will aid personalized treatment and improve clinical outcomes as the battle against this challenging cancer continues.

TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway.

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

[Identification and functional characterization of candidate genes involved in biosynthesis of ginsenoside Rg_1].

Panax ginseng is a perennial herb with the main active compounds of ginsenosides. Among the reported ginsenosides, ginsenoside Rg_1 not only has a wide range of medicinal functions and abundant content but also is one of the major ginsenoside for the quality evaluation of this herb in the Chinese Pharmacopoeia. The main biosynthesis pathway of ginsenoside Rg_1 in P. ginseng has been clarified, which lays a foundation for the comprehensive and in-depth analysis of the biosynthesis and regulatory mechanism of ginseno-side Rg_1. However, the biosynthesis of ginsenoside Rg_1 is associated with other complex processes involving a variety of regulatory genes and catalyzing enzyme genes, which remain to be studied comprehensively. With the transcriptome data of 344 root samples from 4-year-old P. ginseng plants and their corresponding ginsenoside Rg_1 content obtained in the previous study, this study screened out 217 differentially expressed genes(DEGs) with Rg_1 content changes by DEseq2 analysis in R language. Furthermore, the weighted gene co-expression network analysis(WGCNA) revealed 40 hub genes among the DEGs.Pearsoncorrelation analysis was further perforned to yield 20 candidate genes significantly correlated with ginsenoside Rg_1 content, and these genes were annotated to multiple metabolic processes including primary metabolism and secondary metabolism. Finally, the treatment of P. ginseng adventitious roots with methyl jasmonate indicated that 16 of these genes promoted the biosynthesis of ginsenoside Rg_1 in response to methyl jasmonate induction. Finally, one of the 16 genes was randomly selected to verify the function of the gene by genetic transformation and qRT-PCR and to confirm the rationality of the methodology of this study. The above results lay a foundation for studying the mechanism for regulation on the synthesis of ginsenoside Rg_1 and provide genetic resources for the industrial production of ginsenoside Rg_1.

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

Background: 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. Methods: 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. Results: 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-1ß 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. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

Transcriptome-Based Weighted Gene Co-Expression Network Analysis Reveals the Photosynthesis Pathway and Hub Genes Involved in Promoting Tiller Growth under Repeated Drought-Rewatering Cycles in Perennial Ryegrass.

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought-rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought-rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought-rewatering (D2R), and one cycle of drought-rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson's correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding.

Mismatch repair protein deficiency and its implications on distant metastasis in colorectal cancer: A comprehensive analysis.

BACKGROUND: While previous studies have indicated variability in distant metastatic potential among different mismatch repair (MMR) states in colorectal cancer (CRC), their findings remain inconclusive, especially considering potential differences across various ethnic backgrounds. Furthermore, the gene regulatory networks and the underlying mechanisms responsible for these variances in metastatic potential across MMR states have yet to be elucidated. METHODS: We collected 2058 consecutive primary CRC samples from the South West of China and assessed the expression of MMR proteins (MLH1, MSH2, MSH6, and PMS2) using immunohistochemistry. To explore the inconsistencies between different MMR statuses and recurrence, we performed a meta-analysis. To delve deeper, we employed Weighted Gene Co-expression Network Analysis (WGCNA), ClueGo, and iRegulon, pinpointing gene expression networks and key regulatory molecules linked to metastasis and recurrence in CRC. Lastly, both univariate and multivariate Cox regression analyses were applied to determine the impact of core regulatory molecules on metastasis. RESULTS: Of the samples, 8.2% displayed deficient MMR (dMMR), with losses of MLH1 and PSM2 observed in 40.8% and 63.9%, respectively. A unique 24.3% isolated loss of PMS2 without concurrent metastasis was identified, a result that diverges from established literature. Additionally, our meta-analysis further solidifies the reduced recurrence likelihood in dMMR CRC samples compared to proficient MMR (pMMR). Two gene expression networks tied to distant metastasis and recurrence were identified, with a majority of metastasis-related genes located on chromosomes 8 and 18. An IRF1 positive feedback loop was discerned in the metastasis-related network, and IRF1 was identified as a predictive marker for both recurrence-free and distant metastasis-free survival across multiple datasets. CONCLUSION: Geographical and ethnic factors might influence peculiarities in MMR protein loss. Our findings also highlight new gene expression networks and crucial regulatory molecules in CRC metastasis, enhancing our comprehension of the mechanisms driving distant metastasis.

CDKN1A regulation on chondrogenic differentiation of human chondrocytes in osteoarthritis through single-cell and bulk sequencing analysis.

Objective: Chondrocyte death is the hallmark of cartilage degeneration during osteoarthritis (OA). However, the specific pathogenesis of cell death in OA chondrocytes has not been elucidated. This study aims to validate the role of CDKN1A, a key programmed cell death (PCD)-related gene, in chondrogenic differentiation using a combination of single-cell and bulk sequencing approaches. Design: OA-related RNA-seq data (GSE114007, GSE55235, GSE152805) were downloaded from Gene Expression Omnibus database. PCD-related genes were obtained from GeneCards database. RNA-seq was performed to annotate the cell types in OA and control samples. Differentially expressed genes (DEGs) among those cell types (scRNA-DEGs) were screened. A nomogram of OA was constructed based on the featured genes, and potential drugs targeting the featured genes were predicted. The presence of key genes was confirmed using Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Western blot (WB), and immunohistochemistry (IHC). Micromass culture and Alcian blue staining were used to determine the effect of CDKN1A on chondrogenesis. Results: Six cell types, namely HomC, HTC, RepC, preFC, FC, and RegC, were annotated in scRNA-seq data. Five featured genes (JUN, CDKN1A, HMGB2, DDIT3, and DDIT4) were screened by multiple biological information analysis methods. TAXOTERE had the highest ability to dock with DDIT3. Functional analysis indicated that CDKN1A was enriched in processes related to collagen catabolism and acts as a positive regulator of autophagy. Additionally, CDKN1A was found to be associated with several KEGG pathways, including those involved in acute myeloid leukemia and autoimmune thyroid disease. CDKN1A was confirmed down-regulated in the joint tissues of OA mouse model and OA model cell. Inhibiting the expression of CDKN1A can significantly suppress the differentiation of OA chondrocytes. Conclusion: Our findings highlight the critical role of CDKN1A in promoting cartilage formation in both in vivo and in vitro and suggest its potential as a therapeutic target for OA treatment.

Application of Weighted Gene Co-Expression Network Analysis to Metabolomic Data from an ApoA-I Knockout Mouse Model.

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography-Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets.

A study on the role of Taxifolin in inducing apoptosis of pancreatic cancer cells: screening results using weighted gene co-expression network analysis.

Pancreatic adenocarcinoma (PAAD) is a frequent malignant tumor in the pancreas. The incomplete understanding of cancer etiology and pathogenesis, as well as the limitations in early detection and diagnostic methods, have created an urgent need for the discovery of new therapeutic targets and drugs to control this disease. As a result, the current therapeutic options are limited. In this study, the weighted gene co-expression network analysis (WGCNA) method was employed to identify key genes associated with the progression and prognosis of pancreatic adenocarcinoma (PAAD) patients in the Gene Expression Profiling Interactive Analysis (GEPIA) database. To identify small molecule drugs with potential in the treatment of pancreatic adenocarcinoma (PAAD), we compared key genes to the reference dataset in the CMAP database. First, we analyzed the antitumor properties of small molecule drugs using cell counting kit-8 (CCK-8), AO/EB and Transwell assays. Subsequently, we integrated network pharmacology with molecular docking to explore the potential mechanisms of the identified molecules' anti-tumor effects. Our findings indicated that the progression and prognosis of PAAD patients in pancreatic cancer were associated with 11 genes, namely, DKK1, S100A2, CDA, KRT6A, ITGA3, GPR87, IL20RB, ZBED2, PMEPA1, CST6, and MUC16. These genes were filtered based on their therapeutic potential through comparing them with the reference dataset in the CMAP database. Taxifolin, a natural small molecule drug with the potential for treating PAAD, was screened by comparing it with the reference dataset in the CMAP database. Cell-based experiments have validated the potential of Taxifolin to facilitate apoptosis in pancreatic cancer cells while restraining their invasion and metastasis. This outcome is believed to be achieved via the HIF-1 signaling pathway. In conclusion, this study provided a theoretical basis for screening genes related to the progression of pancreatic cancer and discovered potentially active small molecule drugs. The experimental results confirm that Taxifolin has the ability to promote apoptosis in pancreatic cancer cells.

APOBEC3C is a novel target for the immune treatment of lower-grade gliomas.

BACKGROUND: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C (A3C) has been identified as a cancer molecular biomarker in the past decade. However, the practical role of A3C in lower-grade gliomas (LGGs) in improving the clinical outcome remains unclear. This study aims to discuss the function of A3C in immunotherapy in LGGs. METHODS: The RNA-Sequencing (RNA-seq) and corresponding clinical data were extracted from UCSC Xena and the results were verified in the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used for screening A3C-related genes. Comprehensive bioinformation analyses were performed and multiple levels of expression, survival rate, and biological functions were assessed to explore the functions of A3C. RESULTS: A3C expression was significantly higher in LGGs than in normal tissues but lower than in glioblastoma (GBM), indicating its role as an independent prognosis predictor for LGGs. Twenty-eight A3C-related genes were found with WGCNA for unsupervised clustering analysis and three modification patterns with different outcomes and immune cell infiltration were identified. A3C and the A3C score were also correlated with immune cell infiltration and the expression of immune checkpoints. In addition, the A3C score was correlated with increased sensitivity to chemotherapy. Single-cell RNA (scRNA) analysis indicated that A3C most probably expresses on immune cells, such as T cells, B cells and macrophage. CONCLUSIONS: A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival.Abbreviation: A3C: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C; LGGs: lower-grade gliomas; CGGA: Chinese Glioma Genome Atlas; WGCNA: Weighted gene co-expression network analysis; scRNA: Single-cell RNA; HGG: higher-grade glioma; OS: overall survival; TME: tumor microenvironment; KM: Kaplan-Meier; PFI: progression-free interval; IDH: isocitrate dehydrogenase; ROC: receiver operating characteristic; GS: gene significance; MM: module membership; TIMER: Tumor IMmune Estimation Resource; GSVA: gene set variation analysis; ssGSEA: single-sample gene-set enrichment analysis; PCA: principal component analysis; AUC: area under ROC curve; HAVCR2: hepatitis A virus cellular receptor 2; PDCD1: programmed cell death 1; PDCD1LG2: PDCD1 ligand 2; PTPRC: protein tyrosine phosphatase receptor type C; ACC: Adrenocortical carcinoma; BLCA: Bladder Urothelial Carcinoma;BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOLCholangiocarcinoma; COADColon adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma.

Constructing a screening model to obtain the functional herbs for the treatment of active ulcerative colitis based on herb-compound-target network and immuno-infiltration analysis.

The therapeutic effect of most traditional Chinese medicines (TCM) on ulcerative colitis is unclear, The objective of this study was to develop a core herbal screening model aimed at facilitating the transition from active ulcerative colitis (UC) to inactive. We obtained the gene expression dataset GSE75214 for UC from the GEO database and analysed the differentially expressed genes (DEGs) between active and inactive groups. Gene modules associated with the active group were screened using WGCNA, and immune-related genes (IRGs) were obtained from the ImmPort database. The TCMSP database was utilized to acquire the herb-molecule-target network and identify the herb-related targets (HRT). We performed intersection operations on HRTs, DEGs, IRGs, and module genes to identify candidate genes and conducted enrichment analyses. Subsequently, three machine learning algorithms (SVM-REF analysis, Random Forest analysis, and LASSO regression analysis) were employed to refine the hubgene from the candidate genes. Based on the hub genes identified in this study, we conducted compound and herb matching and further screened herbs related to abdominal pain and blood in stool using the Symmap database.Besides, the stability between molecules and targets were assessed using molecular docking and molecular dynamic simulation methods. An intersection operation was performed on HRT, DEGs, IRGs, and module genes, leading to the identification of 23 candidate genes. Utilizing three algorithms (RandomForest, SVM-REF, and LASSO) for analyzing the candidate genes and identifying the intersection, we identified five core targets (CXCL2, DUOX2, LYZ, MMP9, and AGT) and 243 associated herbs. Hedysarum Multijugum Maxim. (Huangqi), Sophorae Flavescentis Radix (Kushen), Cotyledon Fimbriata Turcz. (Wasong), and Granati Pericarpium (Shiliupi) were found to be capable of relieving abdominal pain and hematochezia during active UC. Molecular docking demonstrated that the compounds of the four aforementioned herbs showed positive docking activity with their core targets. The results of molecular dynamic simulations indicated that well-docked active molecules had a more stable structure when bound to their target complexes. The study has shed light on the potential of TCMs in treating active UC from an immunomodulatory perspective, consequently, 5 core targets and 4 key herbs has been identified. These findings can provide a theoretical basis for subsequent management and treatment of active UC with TCM, as well as offer original ideas for further research and development of innovative drugs for alleviating UC.

Development and validation of an immune-related gene prognostic index for lung adenocarcinoma.

Background: Lung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD. Methods: Based on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups. Results: The IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS, high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score. Conclusions: IRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model.

Introduction: The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage. Methods: To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools. Results: Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution. Conclusion: Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.

Identification of potential biomarkers associated with CD4+ T cell infiltration in myocardial ischemia-reperfusion injury using bioinformation analysis.

Background: Myocardial ischemia-reperfusion injury (MIRI) is often part of clinical events such as cardiac arrest, resuscitation, and reperfusion after coronary artery occlusion. Recently, more and more studies have shown that the immune microenvironment is an integral part of ischemia-reperfusion injury (IRI), and CD4+ T-cell infiltration plays an important role, but there are no relevant molecular targets for clinical diagnosis and treatment. Methods: The transcriptome data and matched group information were retrieved from the Gene Expression Omnibus (GEO) database. The ImmuCellAI-mouse (Immune Cell Abundance Identifier for mouse) algorithm was used to calculate each symbol's CD4+ T cell infiltration score. The time period with the greatest change in the degree of CD4+ T cell infiltration [ischemia-reperfusion 6 hours (IR6h)-ischemia-reperfusion 24 hours (IR24h)] was selected for the next analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to screen out CD4+ T cell-related genes and from which the gene CLEC5A was screened for the highest correlation with CD4+ T cell infiltration. The potential regulatory mechanism of CD4+ T cells in MIRI was discussed through various enrichment analysis. Finally, we analyzed the expression and molecular function (MF) of CLEC5A and its related genes in MIRI. Results: A total of 406 CD4+ T cell-related genes were obtained by intersecting the results of WGCNA and differential expression analysis. Functional enrichment analysis indicated that the CD4+ T cell-related genes were mainly involved in chemokine signaling pathway and cell cycle. By constructing a protein-protein interaction (PPI) network, a total of 12 hub genes were identified as candidate genes for further analysis. Through the correlation analysis between the 12 candidate genes found in the PPI network and CD4+ T cell infiltration fraction, we determined the core gene CLEC5A. Finally, a gene interaction network was constructed to decipher the biological functions of CLEC5A using GeneMANIA. Conclusions: In this study, RNA sequencing (RNA-Seq) data at different time points after reperfusion were subjected to a series of bioinformatics methods such as PPI network, WGCNA module, etc., and CLEC5A, a pivotal gene associated with CD4+ T-cells, was found, which may serve as a new target for diagnosis or treatment.

Bioinformatics and functional experiments reveal that MRC2 inhibits atrial fibrillation via the PPAR signaling pathway.

Background: Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires improved clinical markers to increase diagnostic accuracy and provide insight into its pathogenesis. Although some biomarkers are available, new ones need to be discovered to better capture the complex physiology of AF. However, their limitations are still not fully addressed. Bioinformatics and functional studies can help find new clinical markers and improve the understanding of AF, meeting the need for early diagnosis and individualized treatment. Methods: To identify AF-related differentially expressed genes (DEGs), We applied the messenger RNA (mRNA) expression profile retrieved in Series Matrix File format from the GSE143924 microarray dataset obtained from the Gene Expression Omnibus (GEO) database, and then used weighted gene co-expression network analysis (WGCNA) to identify the overlapping genes. These genes were analyzed by enrichment analysis, expression analysis and others to obtain the hub gene. Additionally, the potential signaling pathway of hub gene in AF was explored and verified by functional experiments, like quantitative real-time polymerase chain reaction (qRT-PCR), cell counting kit-8 (CCK-8), flow cytometry, and Western blotting (WB) assay. Results: From the GSE143924 data (410 DEGs) and tan module (57 genes), 10 overlapping genes were identified. A central down-regulated gene in AF, MRC2, was identified through bioinformatics analysis. based on these results, it was hypothesized that the PPAR signaling pathway is related to the mechanism of action of MRC2 in AF. Moreover, over-MRC2 markedly reduced the growth speed of angiotensin II (Ang II)-induced human cardiac fibroblasts (HCFs) and increased apoptosis. Conversely, knockdown of MRC2 promoted HCFs cell proliferation number. Additionally, MRC2 over-expression increased the protein expression level of PPARα, PPARγ, CPT-1, and SIRT3 in Ang II-induced HCFs. Conclusions: While meeting the need for new biomarkers in the diagnosis and prognosis of AF, this study reveals the inherent limitations of current biomarkers. We identified MRC2 as a key player as an inhibitory gene in AF, highlighting its role in suppressing AF progression through the PPAR signaling pathway. MRC2 may not only serve as a diagnostic indicator, but also as a promising therapeutic target for patients with AF, which is expected to be applied in clinical practice and open up new avenues for individualized interventions.

Exploring the genetic landscape of nitrogen uptake in durum wheat: genome-wide characterization and expression profiling of NPF and NRT2 gene families.

Nitrate uptake by plants primarily relies on two gene families: Nitrate transporter 1/peptide transporter (NPF) and Nitrate transporter 2 (NRT2). Here, we extensively characterized the NPF and NRT2 families in the durum wheat genome, revealing 211 NPF and 20 NRT2 genes. The two families share many Cis Regulatory Elements (CREs) and Transcription Factor binding sites, highlighting a partially overlapping regulatory system and suggesting a coordinated response for nitrate transport and utilization. Analyzing RNA-seq data from 9 tissues and 20 cultivars, we explored expression profiles and co-expression relationships of both gene families. We observed a strong correlation between nucleotide variation and gene expression within the NRT2 gene family, implicating a shared selection mechanism operating on both coding and regulatory regions. Furthermore, NPF genes showed highly tissue-specific expression profiles, while NRT2s were mainly divided in two co-expression modules, one expressed in roots (NAR2/NRT3 dependent) and the other induced in anthers and/ovaries during maturation. Our evidences confirmed that the majority of these genes were retained after small-scale duplication events, suggesting a neo- or sub-functionalization of many NPFs and NRT2s. Altogether, these findings indicate that the expansion of these gene families in durum wheat could provide valuable genetic variability useful to identify NUE-related and candidate genes for future breeding programs in the context of low-impact and sustainable agriculture.

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response.

Background: Cancer-associated fibroblasts (CAFs) are notably involved in colorectal cancer (CRC) tumorigenesis, progression, and treatment failure. In this article, we report the in silico development of a CAF-related prognostic signature for CRC. Methods: We separately downloaded CRC transcription data from The Cancer Genome Atlas and the Gene Expression Omnibus database. Deconvolution algorithms, including Estimating the Proportions of Immune and Cancer Cells and the Microenvironment Cell Population-counter, were used to calculate CAF abundance, while the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression algorithm was used to calculate the stromal score. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator algorithm were used to identify CAF-related genes and prognostic signatures. Results: We identified a three-gene, prognostic, CAF-related signature and defined risk groups based on the Riskscores. Multidimensional validations were applied to evaluate the robustness of the signature and its correlation with clinical parameters. We utilized Tumor Immune Dysfunction and Exclusion (TIDE) and oncoPredict algorithms to predict therapy responses and found that patients in low-risk groups are more sensitive to immunotherapy and chemotherapy drugs such as 5-fluorouracil and oxaliplatin. Finally, we used the Cancer Cell Line Encyclopedia and Human Protein Atlas databases to evaluate the mRNA and protein levels encoded by the signature genes. Conclusions: This novel CAF-related three-gene signature is expected to become a potential prognostic biomarker in CRC and predict chemotherapy and immunotherapy responses. It may be of considerable value for studying the tumor microenvironment in CRC.

Gene modules and genes associated with postoperative atrial fibrillation: weighted gene co-expression network analysis and circRNA-miRNA-mRNA regulatory network analysis.

Background: Atrial fibrillation (AF) is the most common complication in patients undergoing cardiac surgery. However, the pathogenesis of postoperative AF (POAF) is elusive, and research related to this topic is sparse. Our study aimed to identify key gene modules and genes and to conduct a circular RNA (circRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory network analysis of POAF on the basis of bioinformatic analysis. Methods: The GSE143924 and GSE97455 data sets from the Gene Expression Omnibus (GEO) database were analyzed. Weighted gene co-expression network analysis (WGCNA) was used to identify the key gene modules and genes related to POAF. A circRNA-miRNA-mRNA regulatory network was also built according to differential expression analysis. Functional enrichment analysis was further performed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Results: WGCNA identified 2 key gene modules and 44 key genes that were significantly related to POAF. Functional enrichment analysis of these key genes implicated the following important biological processes (BPs): endosomal transport, protein kinase B signaling, and transcription regulation. The circRNA-miRNA-mRNA regulatory network suggested that KLF10 may take critical part in POAF. Moreover, 2 novel circRNAs, hsa_circRNA_001654 and hsa_circRNA_005899, and 2 miRNAs, hsa-miR-19b-3p and hsa-miR-30a-5p, which related with KLF10, were involved in the network. Conclusions: Our study provides foundational expression profiles following POAF based on WGCNA. The circRNA-miRNA-mRNA network offers insights into the BPs and underlying mechanisms of POAF.

MYEF2: an immune infiltration-related prognostic factor in IDH-wild-type glioblastoma.

Glioblastoma (GBM) is the most malignant and prevalent primary brain tumor. In this study, weighted gene coexpression network analysis (WGCNA) was performed to analyze RNA binding protein (RBP) expression data from The Cancer Genome Atlas (TCGA) for the IDH-wild type GBM cohort. The CIBERSORT algorithm quantified the cellular composition of immune cells and was used to identify key modules associated with CD8+ T cell infiltration. Coexpression networks analysis and protein-protein interaction (PPI) network analysis was used to filter out central RBP genes. Eleven RBP genes, including MYEF2, MAPT, NOVA1, MAP2, TUBB2B, CDH10, TTYH1, PTPRZ1, SOX2, NOVA2 and SCG3, were identified as candidate CD8+ T cell infiltration-associated central genes. A Cox proportional hazards regression model and Kaplan-Meier analysis were applied to identify candidate biomarkers. MYEF2 was selected as a prognostic biomarker based on the results of prognostic analysis. Flow Cytometric Analysis indicated that MYEF2 expression was negatively correlated with dysfunctional CD8+ T cell markers. Kaplan-Meier survival analysis (based on IHC staining) revealed that GBM patients with elevated MYEF2 expression have a better prognosis. Knockdown of MYEF2 in GBM cells via in vitro assays was observed to promote cell proliferation and migration. Our study suggests that MYEF2 expression negatively correlates with T cell exhaustion and tumor progression, rendering it a potentially valuable prognostic biomarker for GBM.

Transcriptomic and genetic approaches reveal that the pipecolate biosynthesis pathway simultaneously regulates tomato fruit ripening and quality.

Pipecolic acid (Pip) and N-hydroxypipecolic acid (NHP) have been found to accumulate during the ripening of multiple types of fruits; however, the function and mechanism of pipecolate pathway in fruits remain unclear. Here study was conducted on fruits produced by the model plant tomato, wherein the NHP biosynthesis-related genes, Slald1 and Slfmo1, were mutated. The results showed that the fruits of both the Slald1 and the Slfmo1 mutants exhibited a delayed onset of ripening, decreased fruit size, nutrition and flavor. Exogenous treatment with Pip and NHP promoted fruit ripening and improved fruit quality. Transcriptomic analysis combined with weighted gene co-expression network analysis revealed that the genes involved in the biosynthesis of amino acids, carbon metabolism, photosynthesis, starch and sucrose metabolism, flavonoid biosynthesis, and plant hormone signal transduction were affected by SlFMO1 gene mutation. Transcription factor prediction analysis revealed that the NAC and AP2/ERF-ERF family members are notably involved in the regulation pathway. Overall, our results suggest that the pipecolate biosynthesis pathway is involved in the simultaneous regulation of fruit ripening and quality and indicate that a regulatory mechanism at the transcriptional level exists. However, possible roles of endogenously synthesized Pip and NHP in these processes remain to be determined. The biosynthesis pathway genes SlALD1 and SlFMO1 may be potential breeding targets for promoting fruit ripening and improving fruit quality with concomitant yield increases.