Angiogenesis Pathway in Kidney Renal Clear Cell Carcinoma and Its Prognostic Value for Cancer Risk Prediction

Bioinformatics screening of prognostic immune-related genes in renal clear cell carcinoma

This study aims to harness bioinformatics to identify prognostic immune-related genes in clear cell renal cell carcinoma (ccRCC), focusing particularly on LILRB3. It evaluates LILRB3's expression in ccRCC, its association with patient prognosis, and its potential as a biomarker for predicting survival, thereby providing a preliminary basis for the diagnosis of ccRCC. Utilizing The Cancer Genome Atlas (TCGA) datasets and an immune gene set, we sought immune-related genes with elevated expression in ccRCC. Seventy-two normal tissue samples and 531 ccRCC samples were analyzed, and differential genes were identified with a screening criterion of fold change (FC) > 2 and P value < 0.01. Survival analysis and receiver operating characteristic (ROC) curve analysis were employed to discover genes of prognostic and diagnostic relevance to ccRCC. Pearson correlation analysis with a cutoff of |r|≥ 0.5, facilitated by cBioPortal, assessed genes co-expressed with LILRB3. The DAVID online tool conducted functional and pathway enrichment analyses for LILRB3-coexpressed genes. The TIMER and TCIA databases were utilized to explore LILRB3's influence on immune infiltration in the tumor microenvironment and its relation to key immunological checkpoints. Screening the TCGA database revealed 3719 up-regulated differential genes in ccRCC, with 355 overlapping immune-related genes. Survival analysis of these 355 genes revealed 100 with significant survival impact. ROC curve analysis pinpointed the top 10 genes, including LILRB3, with the highest diagnostic efficiency. LILRB3 emerged as an independent risk factor from the Cox risk regression model. GO and KEGG analyses linked LILRB3 to various biological processes, including chemokine signaling pathways, immunological response, antigen processing and presentation, inflammatory response, T cell co-stimulation, and signal transduction. LILRB3 significantly affected ccRCC immune infiltration and correlated positively with several immunological checkpoints, such as PD-1, LAG3, IDO1, PD-L1, CTLA4, TIM3, TIGIT, and VISTA. LILRB3 shows higher expression levels in ccRCC than in normal tissues and correlates with poor patient prognosis. Its impactful role in the immune infiltration of the RCC microenvironment suggests that LILRB3 could serve as a novel target for ccRCC treatment and prognosis, underlining its diagnostic and prognostic significance.

An integrated analysis revealing the angiogenic function of TP53I11 in tumor microenvironment

Despite growing evidence suggesting an important contribution of Tumor Protein P53 Inducible Protein 11 (TP53I11) in cancer progression, the role of TP53I11 remains unclear. Our first pan-cancer analysis of TP53I11 showed some tumor tissues displayed reduced TP53I11 expression compared to normal tissues, while others exhibited high TP53I11 expression. Meanwhile, TP53I11 expression carries a particular pan-cancer risk, as high TP53I11 expression levels are detrimental to survival for BRCA, KIRP, MESO, and UVM, but to beneficial survival for KIRC. We demonstrated that TP53I11 expression negatively correlates with DNA methylation in most cancers, and the S14 residue of TP53I11 is phosphorylated in several cancer types. Additionally, TP53I11 was found to be associated with endothelial cells in pan-cancer, and functional enrichment analysis provided strong evidence for its role in tumor angiogenesis. In vitro angiogenesis assays confirmed that TP53I11 can promote angiogenic function of human umbilical vein endothelial cells (HUVECs) in vitro. Mechanistic investigations reveal that TP53I11 is transcriptionally up-regulated by HIF2A under hypoxia.

Identification of novel and potent dual-targeting HDAC1/SPOP inhibitors using structure-based virtual screening, molecular dynamics simulation and evaluation of in vitro and in vivo antitumor activity

Cancer is one of the important factors threatening human health. Hence, it is essential to create novel potent drugs to treat it. Due to the strong correlation among histone deacetylase1 (HDAC1), speckle-type POZ protein (SPOP) and cancers, dual inhibition of HDAC1 and SPOP may be a promising strategy for cancer treatment. In this study, we successfully identified four potential dual-targeting HDAC1/SPOP candidate compounds with structure-based virtual screening. In vitro inhibition experiments confirmed that the four compounds had dual inhibitory effects on HDAC1 and SPOP. Among them, compound HS-2 had a stronger inhibitory effect on HDAC1 and SPOP than the positive controls. Further molecular dynamics simulations indicated that HS-2 could stably bind to HDAC1 and SPOP. In addition, MTT assay indicated that HS-2 inhibited the growth of tumor cells in the micromolar range. In vivo evaluation showed that HS-2 could obviously inhibit the growth of tumor in nude mice without obvious toxicity. These findings suggest that HS-2 is a novel and potent dual-targeting HDAC1/SPOP inhibitor for cancer treatment.

Comprehensive characterization of extracellular matrix-related genes in PAAD identified a novel prognostic panel related to clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro validation

The extracellular matrix (ECM) is a vital component of the tumor microenvironment, which interplays with stromal and tumor cells to stimulate the capacity of cancer cells to proliferate, migrate, invade, and undergo angiogenesis. Nevertheless, the crucial functions of ECM-related genes (ECMGs) in pancreatic adenocarcinoma (PAAD) have not been systematically evaluated. Hence, a comprehensive evaluation of the ECMGs is required in pan-cancer, especially in PAAD. First, a pan-cancer overview of ECMGs was explored through the integration of expression profiles, prognostic values, mutation information, methylation levels, and pathway-regulation relationships. Seven ECMGs (i.e. LAMB3, LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, and COL11A1) were identified to be hub genes of PAAD, which were obviously up-regulated in PAAD and considerably linked to tumor stage as well as prognosis. Subsequently, patients with PAAD were divided into 3 clusters premised on ECMG expression and ECM scores. Cluster 2 was the subtype with the best prognosis accompanied by the lowest ECM scores, further verifying ECM’s significant contribution to the pathophysiological processes of PAAD. Significant differences were observed for oncogene and tumor suppressor gene expression, immune microenvironment, and chemotherapy sensitivity across three ECM subtypes. After applying a variety of bioinformatics methods, a novel and robust ECM-associated mRNA-lncRNA-based prognostic panel (ECM-APP) was developed and validated for accurately predicting clinical outcomes of patients with PAAD. Patients with PAAD were randomly categorized into the train, internal validation, and external validation cohorts; meanwhile, each patient was allocated into high-risk (unfavorable prognosis) and low-risk (favorable prognosis) populations premised on the expression traits of ECM-related mRNAs and lncRNAs. The discrepancy in the tumor mutation burden and immune microenvironment might be responsible for the difference in prognoses across the high-risk and low-risk populations. Overall, our findings identified and validated seven ECMGs remarkably linked to the onset and progression of PAAD. ECM-based molecular classification and prognostic panel aid in the prognostic assessment and personalized intervention of patients with PAAD.

The clinical significance, immune infiltration, and tumor mutational burden of angiogenesis-associated lncRNAs in kidney renal clear cell carcinoma

Background

Poor prognosis of kidney renal clear cell carcinoma (KIRC) is often related to angiogenesis. The lncRNAs that regulate angiogenesis could also affect the prognosis of KIRC. It is meaningful for us to use lncRNAs related to angiogenesis to construct a generic, individualized prognostic signature for patients with KIRC.

Methods

We identified eight angiogenesis-associated genes (AAGs) by differential expression analysis and univariate Cox regression from The Cancer Genome Atlas dataset, including 537 KIRC samples and 72 normal samples. In total, 23 prognostic lncRNAs were screened out after Pearson correlation analysis and univariate Cox regression analysis. Then, we performed least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to establish a four-AAG-related lncRNA prognostic signature.

Results

The risk score was calculated for each KIRC patients by using a four-AAG-related lncRNA prognostic signature. We divided the KIRC patients into high- and low-risk groups by the median of the risk score. It was confirmed that the AAG-related lncRNA prognostic signature has good prognostic value for KIRC patients by time-dependent receiver operating characteristic and Kaplan–Meier survival analysis. We identified 3,399 differentially expressed genes between the high- and low-risk groups and performed their functional enrichment analyses. The AAG-related lncRNA prognostic signature was an independent prognostic predictor for KIRC patients and was used to perform a combined nomogram. We reevaluated them in terms of survival, clinic characteristics, tumor-infiltrating immune cells and tumor mutation burden.

Conclusion

Our research indicates that the AAG-related lncRNA prognostic signature is a promising and potential independent prognostic indicator for KIRC patients. Then, it could offer new insights into the prognosis assessment and potential treatment strategies of KIRC patients.

The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients

Objective: We aimed to investigate the potential role of ERBB signaling pathway–related genes in kidney renal clear cell carcinoma (KIRC) and establish a new predictive risk model using various bioinformatics methods.

Methods: We downloaded the KIRC dataset and clinicopathological information from The Cancer Genome Atlas database. Univariate Cox analysis was used to identify essential genes significantly associated with KIRC progression. Next, we used the STRING website to construct a protein–protein interaction network of ERBB signaling pathway–related molecules. We then used the least the absolute shrinkage and selection operator (LASSO) regression analysis to build a predictive risk model for KIRC patients. Next, we used multiple bioinformatics methods to analyze the copy number variation, single-nucleotide variation, and overall survival of these risk model genes in pan-cancer. At last, we used the Genomics of Drug Sensitivity in Cancer to investigate the correlation between the mRNA expression of genes associated with this risk model gene and drug sensitivity.

Results: Through the LASSO regression analysis, we constructed a novel KIRC prognosis–related risk model using 12 genes: SHC1, GAB1, SOS2, SRC, AKT3, EREG, EIF4EBP1, ERBB3, MAPK3, transforming growth factor-alpha, CDKN1A, and PIK3CD. Based on this risk model, the overall survival rate of KIRC patients in the low-risk group was significantly higher than that in the high-risk group (p = 1.221 × 10⁻¹⁵). Furthermore, this risk model was associated with cancer metastasis, tumor size, node, stage, grade, sex, and fustat in KIRC patients. The receiver operating characteristic curve results showed that the model had better prediction accuracy. Multivariate Cox regression analysis showed that the model’s risk score was an independent risk factor for KIRC. The Human Protein Atlas database was used to validate the protein expression of risk model–associated molecules in tumors and adjacent normal tissues. The validation results were consistent with our previous findings.

Conclusions: We successfully established a prognostic-related risk model for KIRC, which will provide clinicians with a helpful reference for future disease diagnosis and treatment.