The role of ARL4C in predicting prognosis and immunotherapy drug susceptibility in pan-cancer analysis

Identification of biomarkers and potential drug targets in osteoarthritis based on bioinformatics analysis and mendelian randomization

Background

Osteoarthritis (OA) can lead to chronic joint pain, and currently there are no methods available for complete cure. Utilizing the Gene Expression Omnibus (GEO) database for bioinformatics analysis combined with Mendelian randomization (MR) has been widely employed for drug repurposing and discovery of novel therapeutic targets. Therefore, our research focus is to identify new diagnostic markers and improved drug target sites.

Methods

Gene expression data from different tissues of synovial membrane, cartilage and subchondral bone were collected through GEO data to screen out differential genes. Two-sample MR Analysis was used to estimate the causal effect of expression quantitative trait loci (eQTL) on OA. Through the intersection of the two, core genes were obtained, which were further screened by bioinformatics analysis for in vitro and in vivo molecular experimental verification. Finally, drug prediction and molecular docking further verified the medicinal value of drug targets.

Results

In the joint analysis utilizing the GEO database and MR approach, five genes exhibited significance across both analytical methods. These genes were subjected to bioinformatics analysis, revealing their close association with immunological functions. Further refinement identified two core genes (ARL4C and GAPDH), whose expression levels were found to decrease in OA pathology and exhibited a protective effect in the MR analysis, thus demonstrating consistent trends. Support from in vitro and in vivo molecular experiments was also obtained, while molecular docking revealed favorable interactions between the drugs and proteins, in line with existing structural data.

Conclusion

This study identified potential diagnostic biomarkers and drug targets for OA through the utilization of the GEO database and MR analysis. The findings suggest that the ARL4C and GAPDH genes may serve as therapeutic targets, offering promise for personalized treatment of OA.

A comprehensive prognostic and immune infiltration analysis of UBA1 in pan-cancer: A computational analysis and in vitro experiments

Ubiquitin like modifier activating enzyme 1 (UBA1) plays an important role in immune regulation and cellular function. However, the functional mechanism and role of UBA1 in pan-cancer have not been fully elucidated and its value in haematological tumours (diffuse large B cell lymphoma (DLBC/DLBCL) and acute myeloid leukaemia (AML/LAML)) has not been explored. We conducted a comprehensive analysis of the functional mechanism and role of UBA1 in pan-cancer using multiple databases, including differential expression analysis, clinical pathological staging analysis, prognosis analysis and immune analysis. Then, we confirmed the function of UBA1 in haematological tumours through cell experiments. The results showed that the expression of UBA1 was significantly increased in most cancers and the differential expression of UBA1 was mainly concentrated in digestive tumours, haematological tumours and brain tumours. Moreover, the high expression of UBA1 had poor prognosis in most tumours, which may be related to its involvement in various cancer-related pathways such as cell cycle, as well as its methylation level, protein phosphorylation level, immune cell infiltration and immune therapy response. Cell experiments have confirmed that UBA1 can significantly regulate the cycle progression and apoptosis of DLBCL cells and AML cells. Therefore, UBA1 may be a potential therapeutic target for haematological tumours. In summary, our study not only comprehensively analysed the functional mechanisms and clinical value of UBA1 in pan-cancer, but also validated for the first time the regulatory role of UBA1 in haematological tumours.

Prognostic stratification of sepsis through DNA damage response based RiskScore system: insights from single-cell RNA-sequencing and transcriptomic profiling

Background

A novel risk scoring system, predicated on DNA damage response (DDR), was developed to enhance prognostic predictions and potentially inform the creation of more effective therapeutic protocols for sepsis.

Methods

To thoroughly delineate the expression profiles of DDR markers within the context of sepsis, an analytical approach utilizing single-cell RNA-sequencing (scRNA-seq) was implemented. Our study utilized single-cell analysis techniques alongside weighted gene co-expression network analysis (WGCNA) to pinpoint the genes that exhibit the most substantial associations with DNA damage response (DDR). Through Cox proportional hazards LASSO regression, we distinguished DDR-associated genes and established a risk model, enabling the stratification of patients into high- and low-risk groups. Subsequently, we carried out an analysis to determine our model's predictive accuracy regarding patient survival. Moreover, we examined the distinct biological characteristics, various signal transduction routes, and immune system responses in sepsis patients, considering different risk categories and outcomes related to survival. Lastly, we conducted experimental validation of the identified genes through in vivo and in vitro assays, employing RT-PCR, ELISA, and flow cytometry.

Results

Both single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses have demonstrated a strong correlation between DNA damage response (DDR) levels and sepsis prognosis. Specific cell subtypes, including monocytes, megakaryocytes, CD4+ T cells, and neutrophils, have shown elevated DDR activity. Cells with increased DDR scores exhibited more robust and numerous interactions with other cell populations. The weighted gene co-expression network analysis (WGCNA) and single-cell analyses revealed 71 DDR-associated genes. We developed a four-gene risk scoring system using ARL4C, CD247, RPL7, and RPL31, identified through univariate COX, LASSO COX regression, and log-rank (Mantel-Cox) tests. Nomograms, calibration plots, and decision curve analyses (DCA) regarding these specific genes have provided significant clinical benefits for individuals diagnosed with sepsis. The study suggested that individuals categorized as lower-risk demonstrated enhanced infiltration of immune cells, upregulated expression of immune regulators, and a more prolific presence of immune-associated functionalities and pathways. RT-qPCR analyses on a sepsis rat model revealed differential gene expression predominantly in the four targeted genes. Furthermore, ARL4C knockdown in sepsis model in vivo and vitro caused increased inflammatory response and a worse prognosis.

Conclusion

The delineated DDR expression landscape offers insights into sepsis pathogenesis, whilst our riskScore model, based on a robust four-gene signature, could underpin personalized sepsis treatment strategies.