Interleukin 20 receptor subunit beta (IL20RB) predicts poor prognosis and regulates immune cell infiltration in clear cell renal cell carcinoma

Advancements in RNA-Targeted Therapies for Renal Cell Carcinoma

Renal cell carcinoma (RCC) represents a malignant neoplasm characterized by elevated mortality rates, primarily attributed to its resistance to conventional therapeutic interventions. Recent advancements in RNA-targeted therapeutic strategies, such as antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), aptamers, messenger RNAs (mRNAs), microRNAs (miRNAs), short hairpin RNAs (shRNAs), CRISPR/Cas9, and single-guide RNAs (sgRNAs), have shown promising efficacy in preclinical RCC models, owing to their high specificity and effectiveness. This review aims to elucidate the potential of these innovative therapeutic modalities in enhancing the efficacy of targeted therapies, overcoming drug resistance, and improving responses to immunotherapy, while also addressing their roles in RCC progression. Furthermore, the discussion emphasizes the importance of developing personalized medicine strategies to optimize patient outcomes, alongside the potential for advancing delivery mechanisms and integrating these therapeutic approaches with existing treatment modalities to maximize their efficacy.

Investigating causal relationships between plasma proteins and lung adenocarcinoma: result from proteomics and Mendelian randomization study

BACKGROUND

Plasma proteins contribute to the identification, diagnosis, and prognosis of human illnesses, which may be conducive to understanding the molecular mechanism and diagnosis of Lung adenocarcinoma (LUAD).

METHODS

We collected plasma samples from 28 healthy individuals (H) and 56 LUAD patients and analyzed them using LC-MS/MS-based proteomics to determine differential expression plasma proteins (DEPPs). Then, the DEPPs were subjected to a two-sample Mendelian randomization (MR) study based on an "Inverse variance weighted (IVW)" approach to investigate the causal relationships between DEPPs and LUAD. Logistic regression analysis was conducted to develop a diagnostic model for LUAD.

RESULTS

317 plasma proteins were found in proteomics, and 19 DEPPs were identified. The MR study revealed that IL20RB (odds ratio (OR) = 1.600, 95% Confidence Interval (CI) [1.098, 2.331], P = 0.014) and SAA2 (OR = 1.048, 95%CI [1.012, 1.081], P = 0.017) were highly related to LUAD. A diagnostic model was established with IL20RB and SAA2. The AUC of this diagnostic model was 0.858.

CONCLUSION

Plasma IL20RB and SAA2 levels were closely connected with LUAD.

Identification of immune characteristics between different subtypes in kidney renal clear cell carcinoma based on lysosome-related genes to assist immunotherapy

Previous studies have revealed the essential role of lysosomes in human diseases, including cancer. However, there is a lack of in-depth systematic research on its function in kidney renal clear cell carcinoma (KIRC). In this project, we collected the public dataset of KIRC and selected lysosomal genes tightly linked with survival. Cluster analysis uncovered that these genes possess good classification ability and can divide KIRC patients into multiple subtypes with different survival rates. Enrichment analyses revealed that the main biological processes associated with differentially expressed genes (DEGs) in the two representative subpopulations with the largest survival differences (cluster1 and cluster2) were steroid metabolic process, neutrophil extracellular trap formation, and tyrosine metabolism. The immune-related analysis demonstrated notable differences in immune cell infiltration levels between cluster1 and cluster2 subpopulations of KIRC. More specifically, Tfh and TIL were highly infiltrated in the cluster1, and Type II IFN response, mast cells, and basophils were highly infiltrated in the cluster2. The immunotherapy-related analysis demonstrated that cluster1 may be more sensitive to immunotherapy and more likely to benefit from immunotherapy due to its higher immune checkpoint expression, ESTIMATE score, immune score, and higher immunophenoscore (IPS). In addition, gene mutations occurred in the two subtypes, exhibiting similar mutation patterns between the two subtypes. Finally, based on the cMAP database, we identified some small molecules that may target DEGs between the two subtypes, such as epibatidine, mepyramine, and reboxetine. In conclusion, our investigation unearthed that different subtypes of KIRC patients exhibited different survival outcomes and sensitivity to the immune microenvironment, as well as different responses to immunotherapy. These findings may be beneficial for further mechanistic exploration and therapeutic research of KIRC in the future.

Bioinformatics analysis of the role of lysosome-related genes in breast cancer

This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy.

Upregulated X-C motif chemokine ligand 2 (XCL2) is associated with poor prognosis and increased immune infiltration in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most popular malignant carcinomas in the genitourinary system. As a novel tumor-related gene, X-C Motif Chemokine Ligand 2 (XCL2) was up-regulated in ccRCC. The current study aims to reveal the functional activity of XCL2 in ccRCC.

METHODS

The transcriptome profiling, clinical parameters, and simple nucleotide variation profiles of ccRCC samples were obtained from the Cancer Genome Atlas (TCGA) database. The survival analysis, multivariate/univariate Cox analysis, correlation analysis, gene set enrichment analysis (GSEA), and tumor mutation burden (TMB) analysis were performed. Next, immune cell infiltration and immune functions were analyzed. Finally, the functions of XCL2 were investigated in Caki-1 and 786-O cells.

RESULTS

Upregulated XCL2 was associated with worse overall survival of ccRCC and correlated to age, grade, stage, and T stage. Age, grade, and XCL2 were independent prognostic factors. Significant enrichment in apoptosis, DNA replication, and immune response was demonstrated by GSEA. XCL2 was not only tightly associated with immune cell infiltration, but also significantly linked with several immune functions. Moreover, patients, who had higher XCL2 expression, owned higher levels of TMB. Interestingly, XCL2 was positively correlated with common immune checkpoints. In vitro, XCL2 could inhibit apoptosis, and promote proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of Caki-1 and 786-O cells.

CONCLUSIONS

In general, the current study suggested that XCL2 may participate in the progression of ccRCC. Importantly, XCL2 may be a potential new target of immunotherapy.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment. Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA-microRNA-mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center. Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature. Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.