Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Comparative transcriptome profile of embryos at different developmental stages derived from somatic cell nuclear transfer (SCNT) and in-vitro fertilization (IVF) in riverine buffalo (Bubalus bubalis).

Somatic cell nuclear transfer (SCNT) is a very important reproductive technology with many diverse applications, such as fast multiplication of elite animals, the production of transgenic animals and embryonic stem (ES) cells. However, low cloning efficiency, a low live birth rate and the abnormally high incidence of abnormalities in the offspring born are attributed to incomplete or aberrant nuclear reprogramming. In SCNT embryos, the aberrant expression pattern of the genes throughout embryonic development is responsible for the incomplete nuclear reprogramming. The present study was carried out to identify the differential gene expression (DEGs) profile and molecular pathways of the SCNT and IVF embryos at different developmental stages (2 cell, 8 cell and blastocyst stages). In the present study, 1164 (2 cell), 1004 (8 cell) and 530 (blastocyst stage) DEGs were identified in the SCNT embryos as compared to IVF embryos. In addition, several genes such as ZEB1, GDF1, HSF5, PDE3B, VIM, TNNC, HSD3B1, TAGLN, ITGA4 and AGMAT were affecting the development of SCNT embryos as compared to IVF embryos. Further, Gene Ontology (GO) and molecular pathways analysis suggested, SCNT embryos exhibit variations compared to their IVF counterparts and affected the development of embryos throughout the different developmental stages. Apart from this, q-PCR analysis of the GDF1, TMEM114, and IGSF22 genes were utilized to validate the RNA-seq data. These findings contribute valuable insights about the different genes and molecular pathways underlying SCNT embryo development and offer crucial information for improving SCNT efficiency.

Physiological and transcriptomic analyses reveal the regulatory mechanisms of Anoectochilus roxburghii in response to high-temperature stress.

BACKGROUND: High temperatures significantly affect the growth, development, and yield of plants. Anoectochilus roxburghii prefers a cool and humid environment, intolerant of high temperatures. It is necessary to enhance the heat tolerance of A. roxburghii and breed heat-tolerant varieties. Therefore, we studied the physiological indexes and transcriptome of A. roxburghii under different times of high-temperature stress treatments. RESULTS: Under high-temperature stress, proline (Pro), H2O2 content increased, then decreased, then increased again, catalase (CAT) activity increased continuously, peroxidase (POD) activity decreased rapidly, then increased, then decreased again, superoxide dismutase (SOD) activity, malondialdehyde (MDA), and soluble sugars (SS) content all decreased, then increased, and chlorophyll and soluble proteins (SP) content increased, then decreased. Transcriptomic investigation indicated that a total of 2740 DEGs were identified and numerous DEGs were notably enriched for "Plant-pathogen interaction" and "Plant hormone signal transduction". We identified a total of 32 genes in these two pathways that may be the key genes for resistance to high-temperature stress in A. roxburghii. CONCLUSIONS: To sum up, the results of this study provide a reference for the molecular regulation of A. roxburghii's tolerance to high temperatures, which is useful for further cultivation of high-temperature-tolerant A. roxburghii varieties.

Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.

This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.

Integrative bioinformatics approach for identifying key genes and potential therapeutic targets in the concurrent manifestation of hypertrophic cardiomyopathy and pulmonary hypertension.

Background: Hypertrophic cardiomyopathy (HCM), identified as a primary cause of sudden cardiac death (SCD), intertwines with pulmonary hypertension (PH) to amplify cardiovascular morbidity. This complex synergy poses significant therapeutic challenges due to the absence of drugs specifically targeting their concurrent manifestation. This study seeks to unravel the molecular intricacies linking HCM and PH, aiming to lay the groundwork for targeted therapeutic interventions. Methods: Through the analysis of gene expression profiles from datasets GSE36961 (HCM) and GSE113439 (PH) within the public data repository of Gene Expression Omnibus (GEO), this research systematically identified differentially expressed genes (DEGs), conducted extensive functional annotations, and constructed detailed protein-protein interaction (PPI) networks to uncover crucial hub genes. Further, co-expression analyses, alongside drug prediction and molecular docking simulations, were employed to pinpoint potential therapeutic agents that could ameliorate the combined pathology of HCM and PH. Results: Our comprehensive analysis unearthed 79 DEGs shared between HCM and PH, highlighting fourteen as pivotal hub genes. Validation across three additional datasets (GSE35229, GSE32453, and GSE53408) from GEO accentuated secreted phosphoprotein 1 (SPP1) as a key gene of interest. Remarkably, the study identified tacrolimus, ponatinib, bosutinib, dasatinib, doxorubicin, and zanubrutinib as promising drugs for addressing the dual challenge of HCM and PH. Conclusions: The findings of this investigation shed light on the genetic underpinnings of HCM and PH's simultaneous occurrence, emphasizing the central role of SPP1 in their pathogenesis. The identification of six candidate drugs offers a hopeful vista for future therapeutic strategies targeting this complex cardiovascular interplay, marking a significant stride towards mitigating the compounded morbidity of HCM and PH. Future mechanistic and clinical studies are warranted for the investigation of this potential target and therapeutics.

Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis.

Background: Myocardial ischemia and hypoxia may result in myocardial cell necrosis, scar formation, and hyperplasia. We aim to explore the differentially expressed genes (DEGs) in ischemic cardiomyopathy (ICM), construct and identify a clinical prognosis model using bioinformatics methods, so as to screen potential biomarkers of ICM to provide a basis for the early diagnosis and treatment of ICM. Methods: Based on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, R language was used to screen DEGs in healthy myocardial (n=5) and ICM myocardial tissues (n=12). DEGs were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI). Receiver operating characteristic (ROC) curves were drawn to verify the target genes. Results: A total of 259 genes with significantly changed fold change (FC) values were obtained through conditional screening, including up-regulated genes and down-regulated genes. The first two hub genes [interleukin-6 (IL-6) and Ras homologous gene family member A (RHOA)] with the largest degree value among the above up-regulated and down-regulated genes were selected and their expression values were combined in the gene chip to draw the ROC curve based on the pROC package of R language. The area under the ROC curve (AUC) values of IL-6 and RHOA were 0.956 and 0.995, respectively. The expression levels of Sqstm1, Nos2, IL-6, RHOA, and Zfp36 genes in the ICM group are lower than those in the blank control group and the difference was statistically significant (P<0.05). RHOA and Stat3 were identified as the key genes controlling the occurrence and development of ICM. Conclusions: ICM is closely related to the changes of extracellular matrix (ECM) and oxidoreductase activity. The IL-6 and RHOA are expected to become potential targets for ICM treatment.

Identification of the Hub Genes Involved in Chikungunya Viral Infection.

Background Chikungunya virus (CHIKV) infection poses a significant global health threat, necessitating a deeper understanding of its molecular mechanisms for effective management and treatment. This study aimed to understand the molecular and genetic mechanisms of CHIKV infection by analyzing microarray expression data. Methodology National Center for Biotechnology Information (NCBI) GEO2R with an adjusted p-value cut-off of <0.05 and |log2FC ≥ 1.5| was used to identify the differentially expressed genes involved in CHIKV infection using microarray data from the Gene Expression Omnibus (GEO) database, followed by enrichment analysis, protein-protein interaction (PPI) network construction, and, finally, hub gene identification. Results Analysis of the microarray dataset revealed 25 highly significant differentially expressed genes (DEGs), including 21 upregulated and four downregulated genes. PPI network analysis elucidated interactions among these DEGs, with hub genes such as ACTB and CTNNB1 exhibiting central roles. Enrichment analysis identified crucial pathways, including leukocyte transendothelial migration, regulation of actin cytoskeleton, and thyroid hormone signaling, implicating their involvement in CHIKV infection. Furthermore, the study highlights potential therapeutic targets such as ACTB and CTNNB1, which showed significant upregulation in infected cells. Conclusions These findings underscore the complex interplay between viral infection and host cellular processes, shedding light on novel avenues for diagnostic marker discovery and advancing antiviral strategies. In this study, we shed light on the molecular and genetic mechanisms of CHIKV infection and the potential role of ACTB and CTNNB1 genes.

The Construction of a Multi-Gene Risk Model for Colon Cancer Prognosis and Drug Treatments Prediction.

In clinical practice, colon cancer is a prevalent malignant tumor of the digestive system, characterized by a complex and progressive process involving multiple genes and molecular pathways. Historically, research efforts have primarily focused on investigating individual genes; however, our current study aims to explore the collective impact of multiple genes on colon cancer and to identify potential therapeutic targets associated with these genes. For this research, we acquired the gene expression profiles and RNA sequencing data of colon cancer from TCGA. Subsequently, we conducted differential gene expression analysis using R, followed by GO and KEGG pathway enrichment analyses. To construct a protein-protein interaction (PPI) network, we selected survival-related genes using the log-rank test and single-factor Cox regression analysis. Additionally, we performed LASSO regression analysis, immune infiltration analysis, mutation analysis, and cMAP analysis, as well as an investigation into ferroptosis. Our differential expression and survival analyses identified 47 hub genes, and subsequent LASSO regression analysis refined the focus to 23 key genes. These genes are closely linked to cancer metastasis, proliferation, apoptosis, cell cycle regulation, signal transduction, cancer microenvironment, immunotherapy, and neurodevelopment. Overall, the hub genes discovered in our study are pivotal in colon cancer and are anticipated to serve as important biological markers for the diagnosis and treatment of the disease.

Identification and sex expression profiles of candidate chemosensory genes from Atherigona orientalis via the antennae and leg transcriptome analysis.

Atherigona orientalis Schiner (1868) is an acknowledged agricultural pest owing to its feeding habits and breeding locations. This insect is a tropical and subtropical pest in fruits and vegetables, in which >50 varieties of fruits and vegetables in 26 families, such as Capsicum annuum, Lycopersicon esculentum, and Cucumis melo have been attacked. Moreover, A. orientalis may also develop in rotten crops and feces or insect carcasses, which are also considered one kind of sanitary pest and medical insect. At present, the invasion ranges of A. orientalis are still increasing and more preventive and management measures are to be processed. To gain a better understanding of the molecular mechanisms involved in olfactory reception in A. orientalis, the transcriptome of male and female antennae and legs was systematically analyzed. In total, 131 chemosensory-related genes, including 63 odorant receptors (ORs), 20 gustatory receptors (GRs), 18 ionotropic receptors (IRs), 27 odorant binding proteins (OBPs), 1 chemosensory protein (CSP), and 2 sensory neuron membrane proteins (SNMPs), were identified. The analysis focused on obtaining expression information of candidate olfactory genes at the transcriptomic level by examining the differentially expressed genes (DEGs) in all samples. Totally, 41 DEGs were identified between male antennae (MA) and female antennae (FA), including 32 ORs, 5 OBPs, 1 IR, 2 GRs and 1 SNMP. In MA versus male legs (ML), 78 DEGs were identified (45 ORs, 18 OBPs, 6 GRs, 6 IRs, 1 CSP and 2 SNMPs). In FA and female legs (FL), 96 DEGs were identified (51 ORs, 21 OBPs, 9 GRs, 12 IRs, 1 CSP and 2 SNMPs). For ML and FL, 3 DEGs were identified, including 2 ORs and 1 SNMP. Our results supplement valuable insights for future research on the chemoreception mechanisms in A. orientalis.

Cisplatin and Starvation Differently Sensitize Autophagy in Renal Carcinoma: A Potential Therapeutic Pathway to Target Variegated Drugs Resistant Cancerous Cells.

Cisplatin, a powerful chemotherapy medication, has long been a cornerstone in the fight against cancer due to chemotherapeutic failure. The mechanism of cisplatin resistance/failure is a multifaceted and complex issue that consists mainly of apoptosis inhibition through autophagy sensitization. Currently, researchers are exploring ways to regulate autophagy in order to tip the balance in favor of effective chemotherapy. Based on this notion, the current study primarily identifies the differentially expressed genes (DEGs) in cisplatin-treated autophagic ACHN cells through the Illumina Hi-seq platform. A protein-protein interaction network was constructed using the STRING database and KEGG. GO classifiers were implicated to identify genes and their participating biological pathways. ClueGO, David, and MCODE detected ontological enrichment and sub-networking. The network topology was further examined using 12 different algorithms to identify top-ranked hub genes through the Cytoscape plugin Cytohubba to identify potential targets, which established profound drug efficacy under an autophagic environment. Considerable upregulation of genes related to autophagy and apoptosis suggests that autophagy boosts cisplatin efficacy in malignant ACHN cells with minimal harm to normal HEK-293 growth. Furthermore, the determination of cellular viability and apoptosis by AnnexinV/FITC-PI assay corroborates with in silico data, indicating the reliability of the bioinformatics method followed by qRT-PCR. Altogether, our data provide a clear molecular insight into drug efficacy under starved conditions to improve chemotherapy and will likely prompt more clinical trials on this aspect.

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.

Blue-light irradiation induced partial nitrification.

The role of ray radiation from the sunlight acting on organisms has long-term been investigated. However, how the light with different wavelengths affects nitrification and the involved nitrifiers are still elusive. Here, we found more than 60 % of differentially expressed genes (DEGs) in nitrifiers were observed under irradiation of blue light with wavelengths of 440-480 nm, which were 13.4 % and 20.3 % under red light and white light irradiation respectively. Blue light was more helpful to achieve partial nitrification rather than white light or red light, where ammonium oxidization by ammonia-oxidizing archaea (AOA) with the increased relative abundance from 8.6 % to 14.2 % played a vital role. This was further evidenced by the enhanced TCA cycle, reactive oxygen species (ROS) scavenge and DNA repair capacity in AOA under blue-light irradiation. In contrast, nitrite-oxidizing bacteria (NOB) was inhibited severely to achieve partial nitrification, and the newly discovered encoded blue light photoreceptor proteins made them more sensitive to blue light and hindered cell activity. Ammonia-oxidizing bacteria (AOB) expressed genes for DNA repair capacity under blue-light irradiation, which ensured their tiny impact by light irradiation. This study provided valuable insights into the photosensitivity mechanism of nitrifiers and shed light on the diverse regulatory by light with different radiation wavelengths in artificial systems, broadening our comprehension of the nitrogen cycle on earth.

Bioinformatics identification of characteristic genes of cervical cancer via an artificial neural network.

BACKGROUND: Artificial neural networks (ANNs) have been extensively used in the field of medicine. The present hypothesis-free study sought to use an ANN to identify the characteristic genes of cervical cancer (CC). METHODS: RNA sequencing profiles were obtained from the GSE7410, GSE9750, GSE63514, and GSE52903 datasets. The differentially expressed genes (DEGs) were identified and compared between the normal and CC tissues. An ANN analysis was conducted to obtain the random-forest tree and to examine differences in gene filtering. A neural network model was established using the characteristic genes of CC, while the verification accuracy of the model was examined by Cox regression. The differences in the immune infiltrating cells between the normal cervical and CC tissues were compared by CIBERSORT (an analytical tool can provide an estimation of the abundances of member cell types in a mixed cell population). RESULTS: Nine genes' characteristics for CC were identified: cyclin-dependent kinase inhibitor 2A (CDKN2A), chromosome 1 open reading frame 112 (C1orf112), helicase, lymphoid-specific (HELLS), mini-chromosome maintenance protein 5 (MCM5), mini-chromosome maintenance protein 2 (MCM2), kinetochore associated 1 (KNTC1), cysteine-rich secretory protein 3 (CRISP3), phytanoyl-CoA 2-hydroxylase interacting protein (PHYHIP), and cornulin (CRNN). CONCLUSIONS: ANN is a robust neural network model that can be used to potentially predict CC based on the gene score. It can provide novel insights into the pathogenesis and molecular mechanisms of CC.

Systems and Computational Screening identifies SRC and NKIRAS2 as Baseline Correlates of Risk (CoR) for Live Attenuated Oral Typhoid Vaccine (TY21a) associated Protection.

AIM: We investigated the molecular underpinnings of variation in immune responses to the live attenuated typhoid vaccine (Ty21a) by analyzing the baseline immunological profile. We utilized gene expression datasets obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE100665) before and after immunization. We then employed two distinct computational approaches to identify potential baseline biomarkers associated with responsiveness to the Ty21a vaccine. MAIN METHODS: The first pipeline (knowledge-based) involved the retrieval of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network construction, and topological network analysis of post-immunization datasets before gauging their pre-vaccination expression levels. The second pipeline utilized an unsupervised machine learning algorithm for data-driven feature selection on pre-immunization datasets. Supervised machine-learning classifiers were employed to computationally validate the identified biomarkers. KEY FINDINGS: Baseline activation of NKIRAS2 (a negative regulator of NF-kB signalling) and SRC (an adaptor for immune receptor activation) was negatively associated with Ty21a vaccine responsiveness, whereas LOC100134365 exhibited a positive association. The Stochastic Gradient Descent (SGD) algorithm accurately distinguished vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1% accuracy for the three identified genes, respectively. SIGNIFICANCE: This dual-pronged novel analytical approach provides a comprehensive comparison between knowledge-based and data-driven methods for the prediction of baseline biomarkers associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the needle further towards the need for development of precise enteric vaccines and on the importance of pre-immunization screening.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer.

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

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.

Comparative Transcriptome Analysis Reveals the Genes and Pathways Related to Wheat Root Hair Length.

Tube-like outgrowths from root epidermal cells, known as root hairs, enhance water and nutrient absorption, facilitate microbial interactions, and contribute to plant anchorage by expanding the root surface area. Genetically regulated and strongly influenced by environmental conditions, longer root hairs generally enhance water and nutrient absorption, correlating with increased stress resistance. Wheat, a globally predominant crop pivotal for human nutrition, necessitates the identification of long root hair genotypes and their regulatory genes to enhance nutrient capture and yield potential. This study focused on 261 wheat samples of diverse genotypes during germination, revealing noticeable disparities in the length of the root hair among the genotypes. Notably, two long root hair genotypes (W106 and W136) and two short root hair genotypes (W90 and W100) were identified. Transcriptome sequencing resulted in the development of 12 root cDNA libraries, unveiling 1180 shared differentially expressed genes (DEGs). Further analyses, including GO function annotation, KEGG enrichment, MapMan metabolic pathway analysis, and protein-protein interaction (PPI) network prediction, underscored the upregulation of root hair length regulatory genes in the long root hair genotypes. These included genes are associated with GA and BA hormone signaling pathways, FRS/FRF and bHLH transcription factors, phenylpropanoid, lignin, lignan secondary metabolic pathways, the peroxidase gene for maintaining ROS steady state, and the ankyrin gene with diverse biological functions. This study contributes valuable insights into modulating the length of wheat root hair and identifies candidate genes for the genetic improvement of wheat root traits.

Comparative transcriptome profiling of contrasting finger millet (Eleusine coracana (L.) Gaertn) genotypes under heat stress.

BACKGROUND: Eleusine coracana (L.) Gaertn is a crucial C4 species renowned for its stress robustness and nutritional significance. Because of its adaptability traits, finger millet (ragi) is a storehouse of critical genomic resources for crop improvement. However, more knowledge about this crop's molecular responses to heat stress needs to be gained. METHODS AND RESULTS: In the present study, a comparative RNA sequencing analysis was done in the leaf tissue of the finger millet, between the heat-sensitive (KJNS-46) and heat-tolerant (PES-110) cultivars of Ragi, in response to high temperatures. On average, each sample generated about 24 million reads. Interestingly, a comparison of transcriptomic profiling identified 684 transcripts which were significantly differentially expressed genes (DEGs) examined between the heat-stressed samples of both genotypes. The heat-induced change in the transcriptome was confirmed by qRT-PCR using a set of randomly selected genes. Pathway analysis and functional annotation analysis revealed the activation of various genes involved in response to stress specifically heat, oxidation-reduction process, water deprivation, and changes in heat shock protein (HSP) and transcription factors, calcium signaling, and kinase signaling. The basal regulatory genes, such as bZIP, were involved in response to heat stress, indicating that heat stress activates genes involved in housekeeping or related to basal regulatory processes. A substantial percentage of the DEGs belonged to proteins of unknown functions (PUFs), i.e., not yet characterized. CONCLUSION: These findings highlight the importance of candidate genes, such as HSPs and pathways that can confer tolerance towards heat stress in ragi. These results will provide valuable information to improve the heat tolerance in heat-susceptible agronomically important varieties of ragi and other crops.

Deciphering molecular basis of pesticide-induced recurrent pregnancy loss: insights from transcriptomics analysis.

Recent studies have revealed a notable connection between pesticide exposure and Recurrent Pregnancy Loss (RPL), yet the precise molecular underpinning of this toxicity remains elusive. Through the alignment of Differentially Expressed Genes (DEGs) of healthy and RPL patients with the target genes of 9 pesticide components, we identified a set of 12 genes responsible for RPL etiology. Interestingly, biological process showed that besides RPL, those 12 genes also associated with preeclampsia and cardiovascular disease. Enrichment analysis showed the engagement of these genes associated with essential roles in the molecular transport of small molecules, as well as the aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, mineral absorption, ion homeostasis, and ion transport by P-type ATPases. Notably, the crosstalk targets between pesticide components played crucial roles in influencing RPL results, suggesting a role in attenuating pesticide agents that contribute to RPL. It is important to note that non-significant concentration of the pesticide components observed in both control and RPL samples should not prematurely undermine the potential for pesticides to induce RPL in humans. This study emphasizes the complexity of pesticide induced RPL and highlights avenues for further research and precautionary measures.