Objective Seed germination is an important process that determines the beginning of the seed plant life cycle. However, the mechanism underlying seed germination in barley remains unclear. To understand the molecular mechanism of the seed germination in barley, WGCNA analysis was used to detect the hub and responsive genes and to reveal the expression of the genes on seed germination. WGCNA is a valuable tool for studying the correlation between genes, identifying modules with high correlation, and identifying Hub genes in different modules. Materials and methods Raw microarray data related to germination stage were obtained from the GEO microarray database for 0h, 3h, 9h, 18h, 33h and 71h after germination stage. Then, weighted gene coexpression network analysis (WGCNA) was utilized for detection of co-expressed network genes. In the present study, a barley cultivar, Mahtab, was utilized to show expression pattern after 9h, 18h and 71h after germination stage. A total number of 4137 differentially expressed genes (DEG) were identified, with some genes showing higher expression in Mahtab and three genes verified by qRT-PCR. Results Most of these DEGs were involved in metabolic processes, cellular processes, glycolysis, and response to stimulus. Hub gene in MEbrown was alpha/beta-Hydrolases superfamily protein, MEpurple was U-box domain-containing protein 4, and MEdarkgrey was B3 domain-containing transcription factor ABI3 which were positively correlated with germinated seeds. The results showed a microarray database and candidate genes for further study of barley at germination stages. In addition, DEGs were divided into three modules by WGCNA. In this study, gene modules associated with seed germination during barley seed germination were identified. Transcription factor and alpha/beta-hydrolase played an important role at germination stage. Also, gene modules and hub genes at 9h, 18h, and 71h after germination were detected. As there is lack of information on seed germination requirements of barley, this research was conducted to study seed germination mechanisms as well as evaluation of hub genes to study molecular mechanism seed germination in barley. Conclusions The results of the present study provide new insights into the molecular mechanism underlying barley seed germination. Based on the network analysis, transcription factors (TFs) and ubiquitin proteins were involved in germination. Most of the genes related to each module were related to proteins involved in carbohydrate metabolism, glycolysis, and protein degradation. Our gene expression results can serve as molecular markers in barley cultivars during seed germination. These findings can be suitable for molecular assisted selection and breeding of fast germinating barley genotypes.

Introduction:  Thermus thermophilus is a thermophilic bacterium known for its resilience in extreme environments. Investigating its transcriptomic responses to environmental stresses can uncover critical adaptive mechanisms. Materials and Methods:  This study analyzed transcriptomic data from 10 microarray datasets, including 63 samples (36 stress-exposed and 27 controls). Stress conditions included copper, cold, zinc, iron, heat, salt, H2O2, tetracycline, diamide, and alkylation. Differentially expressed genes (DEGs) were identified through meta-analysis, followed by Gene Ontology (GO) enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was employed to detect stress-associated gene modules. Machine learning approaches—decision tree, logistic regression, random forest, adaptive boosting, SVM-RFE, and XGBoost—were used to prioritize key genes.  Results: Meta-analysis revealed 54 upregulated and 196 downregulated genes under stress. GO analysis highlighted significant enrichment in ion transport, localization processes, and transmembrane transporter activity. WGCNA identified two stress-related modules, cyan and lightcyan. SVM-RFE and XGBoost outperformed other machine learning models with superior accuracy, precision, recall, and F1-scores. TTHA0798 emerged as a hub gene consistently identified across machine learning and DEG/WGCNA analyses. Conclusions:  This study provides a comprehensive analysis of the stress responses of T. thermophilus, identifying TTHA0798 as a key hub gene. The integration of transcriptomic data, co-expression analysis, and machine learning offers valuable insights into the adaptive mechanisms of this extremophile, paving the way for further functional studies.

Potato is one of the most important sources in the supply of food and industrial raw materials not only in Iran but also in the world which its yield is affected by various pathogens. Among these pathogens, PVY, PVX and nematode are the main factors of yield reduction. The molecular mechanism underlying disease resistance in potato remains largely unknown. In this study, analysis of gene expression profiles from the GEO data of three pathogen infections in potato was performed and morphological traits under four stresses were investigated. For this purpose, 501 common genes with different expression (DEGs) were studied in two experiments. Functional enrichment analysis showed that DEGs are more involved in nitrogen and primary metabolic cycle, GTP binding and GTPase binding, which are continuously up-regulated after inoculation with different pathogens. Based on the analysis of morphological traits under four stresses, PVY and PVX/PVY interaction left a significant difference with other stresses (PVX and nematode) on these traits. Potato microarray data extracted from GEO database were used for weighted gene co-expression network analysis (WGCNA). Based on the results of the network, 2 groups (modules) of genes were obtained whose expression profiles were highly correlated with each other in response to the above-mentioned four stresses. The results of this experiment provide valuable insight into the pathways and genes affected by PVY, PVX, PVX/PVY and potato nematode viruses, which may facilitate to identify genes resistant to many diseases in potato.

Purpose: This study aims to find candidates for testicular spermatozoa retrieval biomarkers among the seminal plasma exLncRNA pairs. Materials and Methods: A set of exLncRNA pairs with the best potential biomarkers was selected and validated in 96 NOA samples. Weighted correlation network analysis (WGCNA) and Least Absolute Shrinkage and Selection Operator were used to identify possible biomarkers for these pairs (LASSO). These pairs' potential biomarkers were identified using receiver operating curves. Confusion matrices and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), FP, false-negative rates (FNR), and F1 scores are calculated. Through F1 scores, we selected the best threshold value. Results: The relative differential expression of each pair in testicular spermatozoa retrieval (+) and testicular spermatozoa retrieval (-) men were validated. The six pairs displayed the best biomarker potential. Among them, CCDC37. DT-LOCI00505685 pair and LOC440934-LOCI01929088 (XR_001745218. 1) pair showed the most significant potential and stability for detecting testicular spermatozoa retrieval in the selected and validated cohort. Conclusion: CCDC37. DT-LOCI00505685 pair and LOC440934-LOCI01929088 (XR_001745218. 1) pair have the potential to become new molecular biomarkers that could help to select clinical strategies for microdissection testicular sperm extraction

Cervical cancer is one of the most common malignancies and one of the main death causes among females all over the world. The discovery of tumor-related genes is crucial for understanding tumor biology and developing preventative and therapeutic strategies. However, genes included in the tumorigenesis of cervical cancer cells are still unclear. Due to its high prevalence and mortality, understating its pathogenesis and biomarker detection is necessary. The purpose of this study was to recognize potential biomarkers related to cervical cancer and analyze their prognostic significance. The present research used the level 3 mRNA expression data and clinical data of cervical cancer from The Cancer Genome Atlas database to identify differentially expressed genes followed by gene ontology. Weighted co-expression Network Analysis was used to construct scale-free gene co-expression networks. In the co-expression network, the hub module and hub genes were identified. The significant modules associated with T, N, M, and FIGO staging in the network were subsequently screened. Next, overlapping genes between significant gene modules and DEGs were screened. CALML5, TERT, PNPLA3, CHRDL1, C7, LEFTY2, and PCP4 were identified as hub genes. Survival analysis was performed to identify the association between these genes and survival using the GEPIA database. Survival analysis showed that PCP4 was slightly less expressed in patients with primary solid tumors than normal, and related to poor prognosis in cervical cancer. These results show that these hub genes, especially PCP4, may be a potential diagnostic biomarker for cervical cancer and provide a new perspective on the pathogenesis of cervical cancer.