BACKGROUND: Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.
METHODS: We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.
RESULTS: WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.
CONCLUSIONS: HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

BACKGROUND: Endoplasmic reticulum stress (ERS) could be a strategy for treating malignant tumors. Moreover, long noncoding RNAs (lncRNAs) can promote tumorigenesis and progression, and forecast the prognosis of cancers. Nevertheless, the prognostic value of ERS-related lncRNAs has not been reported in lung adenocarcinoma (LUAD).
METHODS: The messenger RNA (mRNA), microRNA (miRNA) and lncRNA expression data related to LUAD were obtained in public databases (TCGA and GEO databases). Prognostic ERS-related differentially expressed lncRNAs (ERS-DELs) were obtained and used to build an ERS-related model by Cox regression analysis. Moreover, we further screened independent prognostic elements and built a nomogram. Furthermore, enrichment analysis of genes was conducted to investigate the functions. A lncRNA-miRNA-mRNA network was built to explore mechanism of lncRNAs. Finally, qRT-PCR was utilized to examine the expression levels of lncRNAs.
RESULTS: 30 ERS-DELs were identified, and an ERS-related signature was built based on AF131215.2, LINC00472, LINC01352, RP1-78O14.1, RP11-253E3.3, RP11-98D18.9, and SNHG12. Gene set enrichment analysis indicated that genes in the high-risk group were chiefly focused on the regulation of mRNA binding, and genes in the low-risk group were significantly focused on protein localization to cilia. A lncRNA-miRNA-mRNA network, containing 7 signature lncRNAs, 23 miRNAs, and 128 mRNAs, was also established. Eventually, quantitative real-time polymerase chain reaction was used to confirm that seven prognostic lncRNAs had a consistent expression with the analysis.
CONCLUSIONS: An ERS-related signature containing seven prognostic lncRNAs was built, which offered new thinking concerning the role of ERS-related lncRNAs in LUAD.

Tea, a globally popular beverage, contains various beneficial secondary metabolites. Tea plants (Camellia sinensis) exhibit diverse genetic traits across cultivars, impacting yield, adaptability, morphology, and secondary metabolite composition. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 tea cultivars were assembled, and comparative analysis was conducted on the coding sequences of 13 cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform a cross-cultivar comparison of co-expressed gene clusters and tissue-specific gene expression. We observed that \'Anji Baicha\' possesses the highest number of cultivar-specific genes and the second-highest number of expanded genes. These genes in \'Anji Baicha\' tend to be enriched in functions associated with cold stress response, chloroplast thylakoid structure, and nitrogen metabolism. Notably, we identified three significantly expanded homologous genes in \'Anji Baicha\' encoding the ICE1, SIZ1, and MAPKK2, which are closely associated with the cold sensitivity of \'Anji Baicha\'. Additionally, one significantly expanded homologous gene in \'Anji Baicha\' encoding regulatory factor RIQ may play a crucial role in the abnormal chloroplast structure and absence of thylakoid membranes in \'Anji Baicha\'.

UNASSIGNED: Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood.
UNASSIGNED: Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The \"limma\" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes.
UNASSIGNED: We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD.
UNASSIGNED: We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html.

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene-miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation-related genes were found to overlap between the differentially expressed genes and neuroinflammation-related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene-miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG.

BACKGROUND: Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population.
METHODS: Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital\'s cohort as well as TCGA-KIRC cohort.
RESULTS: VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses.
CONCLUSIONS: In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention.
METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI).
RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores.
CONCLUSIONS: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

UNASSIGNED: The study aimed to investigate the pathogenesis of sepsis-induced cardiomyopathy, a leading cause of mortality in septic patients. Transcriptome data from cecal ligation and puncture-induced septic mice were analyzed at different time points (24, 48, and 72 hours) using GSE171546 data. Through weighted gene co-expression network analysis, time series, and differential expression analyses, key time-series differentially expressed genes were identified. In addition, single-cell sequencing data (GSE207363) were used for both differential and pseudotime analyses to pinpoint differentially expressed genes specific to endothelial cells. The study highlighted Spock2, S100a9, S100a8, and Xdh as differential genes specific to endothelial cells in a time-dependent manner. Immunofluorescence validation confirmed the increased expression of SPOCK2 in the endothelial cells of cecal ligation and puncture-induced septic mice. Furthermore, in vitrostudies showed that deletion of Spock2 significantly increased LPS-induced apoptosis and necrosis in human umbilical vein endothelial cells. In conclusion, SPOCK2 expression was increased in septic cardiac endothelial cells and LPS-induced human umbilical vein endothelial cells and may play a protective role.