Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

UNASSIGNED: FAR1/FHY3 transcription factors are derived from transposase, which play important roles in light signal transduction, growth and development, and response to stress by regulating downstream gene expression. Although many FAR1/FHY3 members have been identified in various species, the FAR1/FHY3 genes in maize are not well characterized and their function in drought are unknown.
UNASSIGNED: The FAR1/FHY3 family in the maize genome was identified using PlantTFDB, Pfam, Smart, and NCBI-CDD websites. In order to investigate the evolution and functions of FAR1 genes in maize, the information of protein sequences, chromosome localization, subcellular localization, conserved motifs, evolutionary relationships and tissue expression patterns were analyzed by bioinformatics, and the expression patterns under drought stress were detected by quantitative real-time polymerase chain reaction (qRT-PCR).
UNASSIGNED: A total of 24 ZmFAR members in maize genome, which can be divided into five subfamilies, with large differences in protein and gene structures among subfamilies. The promoter regions of ZmFARs contain abundant abiotic stress-responsive and hormone-respovensive cis-elements. Among them, drought-responsive cis-elements are quite abundant. ZmFARs were expressed in all tissues detected, but the expression level varies widely. The expression of ZmFARs were mostly down-regulated in primary roots, seminal roots, lateral roots, and mesocotyls under water deficit. Most ZmFARs were down-regulated in root after PEG-simulated drought stress.
UNASSIGNED: We performed a genome-wide and systematic identification of FAR1/FHY3 genes in maize. And most ZmFARs were down-regulated in root after drought stress. These results indicate that FAR1/FHY3 transcription factors have important roles in drought stress response, which can lay a foundation for further analysis of the functions of ZmFARs in response to drought stress.

The development of osteoarthritis (OA) involves subchondral bone lesions, but the role of osteoblastic autophagy-related genes (ARGs) in osteoarthritis is unclear. Through integrated analysis of single-cell dataset, Bulk RNA dataset, and 367 ARGs extracted from GeneCards, 40 ARGs were found. By employing multiple machine learning algorithms and PPI networks, three key genes (DDIT3, JUN, and VEGFA) were identified. Then the RF model constructed from these genes indicated great potential as a diagnostic tool. Furthermore, the model\'s effectiveness in predicting OA has been confirmed through external validation datasets. Moreover, the expression of ARGs was examined in osteoblasts subject to excessive mechanical stress, human and mouse tissues. Finally, the role of ARGs in OA was confirmed through co-culturing explants and osteoblasts. Thus, osteoblastic ARGs could be crucial in OA development, providing potential diagnostic and treatment strategies.

UNASSIGNED: Erythropoietin-producing human hepatocellular (Eph) receptors stand out as the most expansive group of receptor tyrosine kinases (RTKs). Accumulating evidence suggests that within this expansive family, the EphA subset is implicated in driving cancer cell progression, proliferation, invasion, and metastasis, making it a promising target for anticancer treatment. Nonetheless, the extent of EphA family involvement across diverse cancers, along with its intricate interplay with immunity and the tumor microenvironment (TME), remains to be fully illuminated.
UNASSIGNED: The relationships between EphA gene expression and patient survival, immunological subtypes, and TME characteristics were investigated based on The Cancer Genome Atlas (TCGA) database. The analyses employed various R packages.
UNASSIGNED: A significant difference in expression was identified for most EphA genes when comparing cancer tissues and non-cancer tissues. These genes independently functioned as prognostic factors spanning multiple cancer types. Moreover, a significant correlation surfaced between EphA gene expression and immune subtypes, except for EphA5, EphA6, and EphA8. EphA3 independently influenced the prognosis of papillary renal cell carcinoma (KIRP). This particular gene exhibited links with immune infiltration subtypes and clinicopathologic parameters, holding promise as a valuable biomarker for predicting prognosis and responsiveness to immunotherapy in patients with KIRP.
UNASSIGNED: By meticulously scrutinizing the panorama of EphA genes in a spectrum of cancers, this study supplemented a complete map of the effect of EphA family in Pan-cancer and suggested that EphA family may be a potential target for cancer therapy.

UNASSIGNED: Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient\'s prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.
UNASSIGNED: A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.
UNASSIGNED: A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.
UNASSIGNED: A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

The presence of a poly(A) tail is indispensable for the post-transcriptional regulation of gene expression in cancer. This dynamic and modifiable feature of transcripts is under the control of various nuclear and cytoplasmic proteins. This study aimed to develop a novel cytoplasmic poly(A)-related signature for predicting prognosis, clinical attributes, tumor immune microenvironment (TIME), and treatment response in hepatocellular carcinoma (HCC). Utilizing RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), non-negative matrix factorization (NMF), and principal-component analysis (PCA) were employed to categorize HCC patients into three clusters, thus demonstrating the pivotal prognostic role of cytoplasmic poly(A) tail regulators. Furthermore, machine learning algorithms such as least absolute shrinkage and selection operator (LASSO), survival analysis, and Cox proportional hazards modeling were able to distinguish distinct cytoplasmic poly(A) subtypes. As a result, a 5-gene signature derived from TCGA was developed and validated using International Cancer Genome Consortium (ICGC) HCC datasets. This novel classification based on cytoplasmic poly(A) regulators has the potential to improve prognostic predictions and provide guidance for chemotherapy, immunotherapy, and transarterial chemoembolization (TACE) in HCC.

UNASSIGNED: This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms.
UNASSIGNED: mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software \"limma\" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments.
UNASSIGNED: A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients.
UNASSIGNED: This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.

The development of targeted drugs for the early prevention and management of chronic kidney disease (CKD) is of great importance. However, the success rates and cost-effectiveness of traditional drug development approaches are extremely low. Utilizing large sample genome-wide association study data for drug repurposing has shown promise in many diseases but has not yet been explored in CKD. Herein, we investigated actionable druggable targets to improve renal function using large-scale Mendelian randomization and colocalization analyses. We combined two population-scale independent genetic datasets and validated findings with cell-type-dependent eQTL data of kidney tubular and glomerular samples. We ultimately prioritized two drug targets, opioid receptor-like 1 and F12, with potential genetic support for restoring renal function and subsequent treatment of CKD. Our findings explore the potential pathological mechanisms of CKD, bridge the gap between the molecular mechanisms of pathogenesis and clinical intervention, and provide new strategies in future clinical trials of CKD.

As a research infrastructure with a mission to provide services for bioinformatics, ELIXIR aims to identify and inform its target audiences. Here, we present a survey on a community of researchers studying the environment with omics approaches in Greece, one of the youngest member countries of ELIXIR. Personal interviews followed by quantitative and qualitative analysis were employed to document interactions and practices of the community and to perform a gap analysis for the transition toward multiomics and systems biology. Environmental omics in Greece mostly concerns production of data, in large majority on microbes and non-model organisms. Our survey highlighted (1) the popularity and suitability of targeted hands-on training events; (2) data quality and management issues as important elements for the transition to multiomics, and (3) lack of knowledge and misconceptions regarding interoperability, metadata standards, and pre-registration. The publicly available collected answers represent a valuable resource in view of future strategic planning.

OBJECTIVE: Uveal melanoma is an ocular malignancy whose prognosis severely worsens following metastasis. In order to improve the understanding of molecular physiology of metastatic uveal melanoma, we identified genes and pathways implicated in metastatic vs non-metastatic uveal melanoma.
METHODS: A previously published dataset from Gene Expression Omnibus (GEO) was used to identify differentially expressed genes between metastatic and non-metastatic samples as well as to conduct pathway and perturbagen analyses using Gene Set Enrichment Analysis (GSEA), EnrichR, and iLINCS.
RESULTS: In male metastatic uveal melanoma samples, the gene LOC401052 is significantly down-regulated and FHDC1 is significantly up-regulated compared to non-metastatic male samples. In female samples, no significant differently expressed genes were found. Additionally, we identified many significant up-regulated immune response pathways in male metastatic uveal melanoma, including \"T cell activation in immune response\". In contrast, many top up-regulated female pathways involve iron metabolism, including \"heme biosynthetic process\". iLINCS perturbagen analysis identified that both male and female samples have similar discordant activity with growth factor receptors, but only female samples have discordant activity with progesterone receptor agonists.
CONCLUSIONS: Our results from analyzing genes, pathways, and perturbagens demonstrate differences in metastatic processes between sexes.