Role of dysregulated macrophage subpopulation ratios and functional changes in the development of coronary atherosclerosis.

Coronary heart disease is one of the most significant risk factors affecting human health worldwide. Its pathogenesis is intricate, with atherosclerosis being widely regarded as the leading cause. Aberrant lipid metabolism in macrophages is recognized as one of the triggering factors in atherosclerosis development. To investigate the role of macrophages in the formation of coronary artery atherosclerosis, we utilized single-cell data from wild-type mice obtained from the aortic roots and ascending aortas after long-term high-fat diet feeding, as deposited in GSE131776. Seurat software was employed to refine the single-cell data in terms of scale and cell types, facilitating the identification of differentially expressed genes. Through the application of differential expression genes, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses at 0, 8 and 16 weeks, aiming to uncover pathways with the most pronounced functional alterations as the high-fat diet progressed. The AddModuleScore function was employed to score the expression of these pathways across different cell types. Subsequently, macrophages were isolated and further subdivided into subtypes, followed by an investigation into intercellular communication within these subtypes. Subsequent to this, we induced THP-1 cells to generate foam cells, validating critical genes identified in prior studies. The results revealed that macrophages underwent the most substantial functional changes as the high-fat diet progressed. Furthermore, two clusters were identified as potentially playing pivotal roles in macrophage functional regulation during high-fat diet progression. Additionally, macrophage subtypes displayed intricate functionalities, with mutual functional counterbalances observed among these subtypes. The proportions of macrophage subtypes and the modulation of anti-inflammatory and pro-inflammatory functions played significant roles in the development of coronary artery atherosclerosis.

Genome-Scale Investigation of the Regulation of azoR Expression in Escherichia coli Using Computational Analysis and Transposon Mutagenesis.

Bacterial azoreductases are enzymes that catalyze the reduction of ingested or industrial azo dyes. Although azoreductase genes have been well identified and characterized, the regulation of their expression has not been systematically investigated. To determine how different factors affect the expression of azoR, we extracted and analyzed transcriptional data from the Gene Expression Omnibus (GEO) resource, then confirmed computational predictions by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results showed that azoR expression was lower with higher glucose concentration, agitation speed, and incubation temperature, but higher at higher culture densities. Co-expression and clustering analysis indicated ten genes with similar expression patterns to azoR: melA, tpx, yhbW, yciK, fdnG, fpr, nfsA, nfsB, rutF, and chrR (yieF). In parallel, constructing a random transposon library in E. coli K-12 and screening 4320 of its colonies for altered methyl red (MR)-decolorizing activity identified another set of seven genes potentially involved in azoR regulation. Among these genes, arsC, relA, plsY, and trmM were confirmed as potential azoR regulators based on the phenotypic decolorization activity of their transposon mutants, and the expression of arsC and relA was confirmed, by qRT-PCR, to significantly increase in E. coli K-12 in response to different MR concentrations. Finally, the significant decrease in azoR transcription upon transposon insertion in arsC and relA (as compared to its expression in wild-type E. coli) suggests their probable involvement in azoR regulation. In conclusion, combining in silico analysis and random transposon mutagenesis suggested a set of potential regulators of azoR in E. coli.

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms.

BACKGROUND: Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies. METHODS: We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control. RESULTS: After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients. CONCLUSION: Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

Prognostic analysis of uveal melanoma based on the characteristic genes of M2-type macrophages in the tumor microenvironment.

Uveal melanoma arises from stromal melanocytes and is the most prevalent primary intraocular tumor in adults. It poses a significant diagnostic and therapeutic challenge due to its high malignancy and early onset of metastases. In recent years, there has been a growing interest in the role of diverse immune cells in tumor cell development and metastasis. Using The Cancer Genome Atlas and the gene expression omnibus databases, and the CIBERSORT method, we investigated the topography of intra-tumor immune infiltration in uveal melanoma in this research. We evaluated the prognosis of uveal melanoma patients using the M2 macrophage immune cell infiltration score in conjunction with clinical tumor patient data. We built a prognostic model based on the distinctive genes of M2 macrophages and combined it with patients' clinical data in the database; we ran a survival prognostic analysis to authenticate the model's accuracy. The functional study revealed the importance of macrophage-associated genes in the development of uveal melanoma. Moreover, the reliability of our prediction model was verified by combining tumor mutational load, immune checkpoint, and drug sensitivity, respectively. Our study provides a reference for the follow-up study of uveal melanoma.

Integrated Analysis of miRNA and mRNA Regulation Network in Hypertension.

Hypertension is the most common chronic disease. Early diagnosis is helpful for early medical intervention. The miRNAs and the messenger RNAs (mRNAs) network may be valuable disease diagnosis markers. We aimed to explore the diagnostic value of the miRNA-mRNA network for hypertension patients. Data of miRNAs and mRNAs expression were obtained from the Gene Expression Omnibus database. The weighted gene co-expression network analysis was performed to screen hypertension-related gene modules, and these genes undergone functional enrichment analysis using "clusterProfiler" package. Differential expression analysis was applied on miRNAs expression profiles using "limma" package. TargetScanHuman and miRDB databases were used to select target mRNAs. Cytoscape software was used to visualize the miRNA-mRNA regulation network. P value < 0.05 was considered statistically significant after t test. There were 123 screened mRNAs which were enriched in 161 Gene ontology (GO) terms and 14 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Thirty-five differentially expressed miRNAs (DEMs) are found in the GSE75670. Totally 36 miRNA-mRNA pairs were obtained after the integrated analysis, and three mRNAs and the hsa-miRNA-5589-5p were identified as key joints. Hub genes, KIAA0513, ARID3A and LRPAP1, and key hsa-miRNA-5589-5p are potential diagnostic biomarkers for hypertension. Our findings are promising in the clinical application, conducive to early detection and prompt intervention of hypertension.

Identification and validation of ferroptosis-related genes for chronic rhinosinusitis with nasal polyps.

PURPOSE: Even though the great progress in the field of chronic rhinosinusitis with nasal polyps (CRSwNP) has been achieved, ferroptosis and its molecular mechanism in CRSwNP remain blank. We are the first to study the relationship between CRSwNP and ferroptosis, aiming to identify ferroptosis-related genes in the process of CRSwNP. METHODS: Using the GEO database and the FerrDb database, significantly differentially expressed ferroptosis-related genes (DEFGs) were selected between CRSwNP-NP and CRSwNP-IT specimens. Then, the protein-protein interaction (PPI) network of ferroptosis-related genes was constructed. Functional enrichment analyses (GSVA, GO, KEGG, and GeneCodis analyses) were introduced in our study. Besides, based on the GSE136825 data set, DEFGs between CRSwNP-NP and CS-IT specimens were also analyzed. Finally, qRT-PCR was performed to validate the selected ferroptosis-related genes with clinical samples. RESULTS: 31 significantly DEFGs were identified between CRSwNP-NP and CRSwNP-IT specimens. Functional enrichment analyses and the analysis of GeneCodis 4 pointed out that DEFGs may potentially be involved in some related KEGG pathways. 8 DEFGs were selected between CRSwNP-NP and CS-IT specimens. The experimental verification indicated that 4 genes (GPX2, CDO1, CAV1, and TP53) were the important DEFGs of CRSwNP. The Venn diagrams proved that CDO1 and GPX2 were considered as the most important DEFGs genes of CRSwNP, especially GPX2. CONCLUSIONS: Though a comprehensive bioinformatics analysis and the experimental verification, CDO1 and GPX2 were considered as the important ferroptosis-related genes of CRSwNP, especially GPX2. However, further molecular biological experiments would be still required to uncover the underlying mechanism between ferroptosis and CRSwNP.

Identification of LIPH as an unfavorable biomarkers correlated with immune suppression or evasion in pancreatic cancer based on RNA-seq.

BACKGROUND: It is widely considered that pancreatic cancer (PC) is an immunosuppressive cancer. Immune-based therapies remain promising therapeutic strategies for PC. Overexpression of lipase H (LIPH) was reported to be related to immunity in cattle and has also been demonstrated to promote tumor progression in several tumors, but its role in pancreatic carcinogenesis remains unclear. Study on LIPH in PC might provide a new insight into the immunosuppression in PC. METHODS: The potential biological and clinical significance of LIPH was evaluated by bioinformatics analysis. We further investigated potential associations between the expression of LIPH and tumor immune infiltration using the CIBERSORT algorithm, the ESTIMAT algorithm, and single sample gene set enrichment analysis (ssGSEA). RESULTS: LIPH was significantly overexpressed in tumor tissues compared with normal tissues. LIPH overexpression correlated with tumor recurrence, advanced histologic grade, and poorer overall survival (OS). Four of the most common somatic mutation, including KRAS, TP53, CDKN2A, and SMAD4, in PC were all correlated with high LIPH expression. And high LIPH expression was significantly correlated with KRAS activation and SMAD4 inactivation. Besides, LIPH expression was involved in various biological pathways such as negative regulation of cell-cell adhesion, actin cytoskeleton, EMT, angiogenesis, and signaling by MST1. And LIPH overexpression caused high infiltration of TAMs, Treg cells, and Th2/Th1, but reduced the infiltration of CD8+ T cells and Th1 cells. CONCLUSIONS: Our findings demonstrated that LIPH correlated with immune suppression or evasion and may function as a novel unfavorable prognostic biomarker in PC.

Primary Breast Angiosarcoma: Comparative Transcriptome Analysis.

Primary breast angiosarcoma, with de novo appearance and not associated with exposure to radiation or lymphedema, is a rare pathology representing less than 0.05% of the neoplasms related to this organ. The pathology is characterized by its aggressiveness, poor prognosis, and difficulties in its differential diagnosis. This article reports the case of a 55-year-old white woman with no family history of cancer, with a rapidly growing mass in the left mammary gland that ulcerates and bleeds. It is confirmed as primary breast angiosarcoma by immunostaining in the tumor tissue for CD31, CD34, and FLI-1. In addition, a sample of neoplastic and healthy tissues is collected from the patient for RNA sequencing; the results are contrasted with a tissue sample from a patient with Luminal A subtype of breast cancer, as well as data from other cases of angiosarcoma available in public databases. These findings revealed a genetic profile associated with the immune and inflammatory response in the patient's sample when compared to available angiosarcoma data; these molecular patterns are consistent with other recent studies. Due to the rarity of the disease, the studies carried out on each patient contribute to the expanding knowledge of the etiology and molecular pathways that are still partially known and continue to be the subject of research. Aside from a comparative transcriptome study, this article aims to provide an update on the state of knowledge about this disease.

Identification of potential plasma biomarkers in early-stage nasopharyngeal carcinoma-derived exosomes based on RNA sequencing.

BACKGROUND: Early diagnosis of nasopharyngeal carcinoma (NPC) is vital to improve the prognosis of these patients. However, early diagnosis of NPC is typically challenging. Therefore, we explored the pathogenetic roles and associated mechanisms of exosomes in plasma of patients with early-stage NPC. METHODS: Exosomes in plasma were extracted by ultra-high-speed centrifugation. Western blot and transmission electron microscopy (TEM) were used to verify the purity of exosomes. The sequencing data (6 plasma samples from healthy volunteers vs. 6 NPC plasma samples) were analyzed by principal component analysis (PCA), DESeq2, gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and TargetScan. The differentially expressed miRNAs (DEmiRNAs) were obtained from the dataset (GSE118720) downloaded from the Gene Expression Omnibus (GEO) repository. Additionally, the datasets downloaded from the GEO database (GSE12452, GSE13597, GSE53819, GSE64634) were used to predict the target genes and functions of hsa-miR-1301-3p. qPCR was applied to verify the differences in the expressions of hsa-miR-1301-3p between 10 normal plasma and 10 NPC plasma samples. RESULTS: Western blot, TEM, and Nanoparticle Tracking Analysis showed adequate purity of the extracted exosomes. RNA-seq analysis revealed 21 upregulated miRNAs, and 10 downregulated miRNAs in plasma exosomes of early-stage NPC patients. GO analysis showed that the target genes of DEmiRNAs were mainly enriched in DNA synthesis and transcription regulation. KEGG analysis revealed that DEmiRNAs were mainly enriched in PI3K-Akt and MAPK signaling pathways. Moreover, the expression of hsa-mir-1301-3p was verified to be significantly upregulated in enlarged samples of plasma exosomes. CONCLUSIONS: We identified several DEmiRNAs extracted from tumor-derived exosomes between normal plasma and early-stage NPC plasma. Bioinformatics analyses indicated that these DEmiRNAs may be related to NPC development. Our study may provide novel insights into underlying biomarkers and mechanisms of plasma exosomes in early-stage NPC.

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis.

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan-cancer validation and time-dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression-related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E-MTAB-4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time-dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan-cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision-making.

Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC. METHODS: We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba. RESULTS: We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network. CONCLUSIONS: Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.

A content-based literature recommendation system for datasets to improve data reusability - A case study on Gene Expression Omnibus (GEO) datasets.

OBJECTIVE: The centrality of data to biomedical research is difficult to understate, and the same is true for the importance of the biomedical literature in disseminating empirical findings to scientific questions made on such data. But the connections between the literature and related datasets are often weak, hampering the ability of scientists to easily move between existing datasets and existing findings to derive new scientific hypotheses. This work aims to recommend relevant literature articles for datasets with the ultimate goal of increasing the productivity of researchers. Our approach to literature recommendation for datasets is a part of the dataset reusability platform developed at the University Texas Health Science Center at Houston for datasets related to gene expression. This platform incorporates datasets from Gene Expression Omnibus (GEO). An average of 34 datasets were added to GEO daily in the last five years (i.e. 2014 to 2018), demonstrating the need for automatic methods to connect these datasets with relevant literature. The relevant literature for a given dataset may describe that dataset, provide a scientific finding based on that dataset, or even describe prior and related work to the dataset's topic that is of interest to users of the dataset. MATERIALS AND METHODS: We adopt an information retrieval paradigm for literature recommendation. In our experiments, distributional semantic features are created from the title and abstract of MEDLINE articles. Then, related articles are identified for datasets in GEO. We evaluate multiple distributional methods such as TF-IDF, BM25, Latent Semantic Analysis, Latent Dirichlet Allocation, word2vec, and doc2vec. Top similar papers are recommended for each dataset using cosine similarity between the dataset's vector representation and every paper's vector representation. We also propose several novel re-ranking and normalization methods over embeddings to improve the recommendations. RESULTS: The top-performing literature recommendation technique achieved a strict precision at 10 of 0.8333 and a partial precision at 10 of 0.9000 using BM25 based on a manual evaluation of 36 datasets. Evaluation on a larger, automatically-collected benchmark shows small but consistent gains by emphasizing the similarity of dataset and article titles. CONCLUSION: This work is the first step toward developing a literature recommendation tool by recommending relevant literature for datasets. This will hopefully lead to better data reuse experience.

Down-regulation of MARCO associates with tumor progression in hepatocellular carcinoma.

Hepatocellular carcinoma(HCC) is a malignant tumor with high mortality due to lack of early diagnostic methods and effective treatments, and the molecular mechanisms are intricate and remain unclear. In the present study, the role of macrophage receptor with collagenous structure (MARCO) in tumor advancement of HCC was investigated. We examined expression level of MARCO in HCC samples, corresponding adjacent nontumor tissues and six hepatoma cell lines by polymerase chain reaction and immunohistochemistry (IHC). Clinical information of HCC patients was also analyzed. The role of MARCO involved in HCC progression via multiple functional experiments in vitro and in vivo was investigated. Bioinformatics analysis was conducted to further explore biological functions of MARCO. We found MARCO was suggestively down-regulated in HCC and associated with favorable prognosis, and MARCO upregulation oppressed tumor cell migration and invasion. Besides, overexpression of MARCO not only promoted apoptosis of hepatoma cells but also suppressed proliferation in vivo and in vitro. Furthermore, gene set enrichment analysis (GSEA) analysis suggested that MARCO may be related to the P53 signaling pathway, and this prediction was confirmed in this study as well. In sum, our study indicated that MARCO was involved in HCC progression and it can be defined as a novel probable biomarker as well as treatment target for HCC.

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile.

(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

Comprehensive analysis of aberrantly expressed profiles of messenger RNA in alcoholic liver disease.

BACKGROUND: Alcoholic liver disease (ALD) is one of the major cause of morbidity and mortality of clinical liver disease worldwide. Until today, although many general therapies are carried out and several molecular targets have been proposed to act as the potential therapeutic targets, more accurate molecular targets and more effective therapeutic methods remain needed. MATERIAL AND METHODS: In the study, we analyze the differential expression genes (DEGs) between the patients with ALD and healthy controls. Gene Ontology enrichment and KEGG signaling pathway analysis are performed to identify the function of DEGs. Some significant molecules are proposed to act as the potential therapeutic targets for ALD. RNA data of 15 ALD tissues and 7 normal tissues for RNA expression analysis were obtained. DEGs in ALD samples compared with normal tissues identified through the limma R package and subjected to network analysis. RESULTS: As a result, we obtained a total of 274 DEGs that mainly involved in biological processes related to the angiogenesis, stress reaction, synthesis, and metabolism of organic acids. Network analysis obtained several genes with high network degree and fold change. Some significant molecules are proposed to act as the potential therapeutic targets for ALD. CONCLUSIONS: Our research identified some new progression-related genes of alcohol liver diseases, which could be regarded as the new targets for the early diagnosis and therapeutic management in ALD.

Multi-platform analysis of methylation-regulated genes in human lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most frequent pathological type of lung cancer that has a poor prognosis and high mortality rate. DNA methylation plays a critical role in various biological processes during development, while dysregulation results in pathological consequences. Thus, this study aimed to identify DNA methylation-regulated genes involved in LUAD occurrence. Initially, 300 downregulated and 168 upregulated mRNA expression levels were identified in two databases: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. In addition, GEO was utilized to detect 243 DNA hyper-methylated sites. Based on our observations, it was possible to correlate downregulation of mRNA expression and DNA hyper-methylation of six genes (ABCA3, COX7A1, HOXA5, SLIT3, SOX17, and SPARCL1). Functional analysis of the six genes indicated that these genes are predominantly enriched in cancer-related pathways and may promote carcinogenesis by regulating epithelialmesenchymal transition processes. In conclusion, our study identified a panel of DNA methylation-regulated genes involved in LUAD and may serve as potential epigenetic markers for this type of carcinoma.

Gene expression metadata analysis reveals molecular mechanisms employed by Phanerochaete chrysosporium during lignin degradation and detoxification of plant extractives.

Lignin, most complex and abundant biopolymer on the earth's surface, attains its stability from intricate polyphenolic units and non-phenolic bonds, making it difficult to depolymerize or separate from other units of biomass. Eccentric lignin degrading ability and availability of annotated genome make Phanerochaete chrysosporium ideal for studying lignin degrading mechanisms. Decoding and understanding the molecular mechanisms underlying the process of lignin degradation will significantly aid the progressing biofuel industries and lead to the production of commercially vital platform chemicals. In this study, we have performed a large-scale metadata analysis to understand the common gene expression patterns of P. chrysosporium during lignin degradation. Gene expression datasets were retrieved from NCBI GEO database and analyzed using GEO2R and Bioconductor packages. Commonly expressed statistically significant genes among different datasets were further considered to understand their involvement in lignin degradation and detoxification mechanisms. We have observed three sets of enzymes commonly expressed during ligninolytic conditions which were later classified into primary ligninolytic, aromatic compound-degrading and other necessary enzymes. Similarly, we have observed three sets of genes coding for detoxification and stress-responsive, phase I and phase II metabolic enzymes. Results obtained in this study indicate the coordinated action of enzymes involved in lignin depolymerization and detoxification-stress responses under ligninolytic conditions. We have developed tentative network of genes and enzymes involved in lignin degradation and detoxification mechanisms by P. chrysosporium based on the literature and results obtained in this study. However, ambiguity raised due to higher expression of several uncharacterized proteins necessitates for further proteomic studies in P. chrysosporium.

Influence of Pterostilbene on Gene Expression in Liver Cancer: An In Silico Analysis.

Background Liver cancer, in particular, is a serious threat to global health and has few viable treatments. One natural molecule that shows potential in cancer therapy is pterostilbene, especially for hepatocellular carcinoma (HCC). The molecular details of pterostilbene's interactions with liver cancer are uncovered in this study using an in silico method. Methodology This study determines the differentially expressed genes (DEGs) in HCC and the way pterostilbene affects them using data from Gene Expression Omnibus (GEO) datasets. To identify the intricate linkages and possible treatment targets, network pharmacology, protein-protein interaction (PPI) analysis, and pathway enrichment investigations were performed. Results The study revealed complex relationships between pterostilbene and liver cancer, identified important DEGs in HCC, and showed enriched pathways. Pterostilbene shows promise as a target for therapeutic approaches in HCC due to its modulation of important signaling pathways. Conclusions This work offers an extensive knowledge of pterostilbene's potential in liver cancer, despite intrinsic computational limitations. In addition to the importance of experimental validation, the pathways and DEGs that have been found provide insightful information for future investigation, highlighting the ongoing research that is necessary to create targeted therapeutics for HCC.

Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach.

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis. METHODS: We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups. RESULTS: Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69). CONCLUSION: The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.