Transcriptomic Analysis Identified ARHGAP Family as a Novel Biomarker Associated With Tumor-Promoting Immune Infiltration and Nanomechanical Characteristics in Bladder Cancer

Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common primary malignancy of bone tumors. More and more ARHGAP family genes have been confirmed are to the occurrence, development, and invasion of tumors. However, its significance in osteosarcoma remains unclear. In this study, we aimed to identify the relationship between ARHGAP family genes and prognosis in patients with OS.

METHODS

OS samples were retrieved from the TCGA and GEO databases. We then performed LASSO regression analysis and multivariate COX regression analysis to select ARHGAP family genes to construct a risk prognosis model. We then validated this prognostic model. We utilized ESTIMATE and CIBERSORT algorithms to calculate the stroma and immune scores of samples, as well as the proportions of tumor infiltrating immune cells (TICs). Finally, we conducted in vivo and in vitro experiments to investigate the effect of ARHGAP28 on osteosarcoma.

RESULTS

We selected five genes to construct a risk prognosis model. Patients were divided into high- and low-risk groups and the survival time of the high-risk group was lower than that of the low-risk group. The high-risk group in the prognosis model constructed had relatively poor immune function. GSEA and ssGSEA showed that the low-risk group had abundant immune pathway infiltration. The overexpression of ARHGAP28 can inhibit the proliferation, migration, and invasion of osteosarcoma cells and tumor growth in mice, and IHC showed that overexpression of ARHGAP28 could inhibit the proliferation of tumor cells.

CONCLUSION

We constructed a risk prognostic model based on five ARHGAP family genes, which can predict the overall survival of patients with osteosarcoma, to better assist us in clinical decision-making and individualized treatment.

ARHGAP39 is a prognostic biomarker involved in immune infiltration in breast cancer

BACKGROUND

Current studies on the role of ARHGAP39 mainly focused on its effect on neurodevelopment. However, there are few studies on the comprehensive analysis of ARHGAP39 in breast cancer.

METHODS

ARHGAP39 expression level was analyzed based on the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression Project (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database and validated by qPCR in various cell lines and tumor tissues. The prognostic value was analyzed using Kaplan-Meier curve analysis. CCK-8 and transwell assays were conducted to identify the biological function of ARHGAP39 in tumorigenesis. Signaling pathways related to ARHGAP39 expression were identified by the GO and KEGG enrichment analysis and gene set enrichment analysis (GSEA). The correlations between ARHGAP39 and cancer immune infiltrates were investigated via TIMER, CIBERSORT, ESTIMATE and tumor-immune system interactions database (TISIDB).

RESULTS

ARHGAP39 was overexpressed in breast cancer and associated with poor survival outcomes. In vitro experiments revealed that ARHGAP39 could facilitate the proliferation, migration, and invasion capability of breast cancer cells. GSEA analysis showed that the main enrichment pathways of ARHGAP39 was immunity-related pathways. Considering the immune infiltration level, ARHGAP39 was negatively associated with infiltrating levels of CD8 + T cell and macrophage, and positively associated with CD4 + T cell. Furthermore, ARHGAP39 was significantly negatively correlated with immune score, stromal score, and ESTIMATE score.

CONCLUSIONS

Our findings suggested that ARHGAP39 can be used as a potential therapeutic target and prognostic biomarker in breast cancer. ARHGAP39 was indeed a determinant factor of immune infiltration.

ARHGAP11A Is a Novel Prognostic and Predictive Biomarker Correlated with Immunosuppressive Microenvironment in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor and immune dysfunction is associated with ccRCC poor prognosis. The RhoGTPase-activating proteins (RhoGAPs) family was reported to affect ccRCC development, but its role in immunity and prognosis prediction for ccRCC remain unknown. In the current study, we found ARHGAP11A was the only independent risk factor among 33 RhoGAPs (hazard ratio [HR] 1.949, 95% confidence interval [CI] 1.364-2.785). High ARHGAP11A level was associated with shorter overall survival (OS, HR 2.040, 95% CI 1.646-3.417) and ARHGAP11A is a prognostic biomarker for ccRCC. ARHGAP11A knockdown suppressed renal cell carcinoma (RCC) cell proliferation, colony formation, and migration, suggesting the promoting role of ARHGAP11A on RCC development. Mechanistically, ARHGAP11A might contribute to the suppressive tumor immune microenvironment (TIME). High ARHGAP11A level was correlated with infiltration of immunosuppressive cells (including T helper 2 (Th2) cells, regulatory T (Treg) cells, myeloid derived suppressor cells (MDSC), and M2 macrophage cells), activation of immunosuppressive pathways (IL6-JAK-STAT3 signaling and IFNγ response), and expression of inhibitory immune checkpoints (ICs). ARHGAP11A could promote T cell exhaustion and induce immune escape. ccRCC patients with low ARHGAP11A level were more suitable for immune checkpoint inhibitors (ICIs) therapy, while those with high ARHGAP11A level might benefit from a combination of ARHGAP11A blockade and ICIs. In all, ARHGAP11A might serve as a novel prognostic marker, therapeutic target, and predictor in the clinical response to ICIs therapy for ccRCC.

Netie: inferring the evolution of neoantigen-T cell interactions in tumors

Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8⁺ T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.

Sentinel Lymph Node Gene Expression Signature Predicts Recurrence-Free Survival in Cutaneous Melanoma

We sought to develop a sentinel lymph node gene expression signature score predictive of disease recurrence in patients with cutaneous melanoma. Gene expression profiling was performed on SLN biopsies using U133A 2.0 Affymetrix gene chips. The top 25 genes associated with recurrence-free survival (RFS) were selected and a penalized regression function was used to select 12 genes with a non-zero coefficient. A proportional hazards regression model was used to evaluate the association between clinical covariates, gene signature score, and RFS. Among the 45 patients evaluated, 23 (51%) had a positive SLN. Twenty-one (46.7%) patients developed disease recurrence. For the top 25 differentially expressed genes (DEG), 12 non-zero penalized coefficients were estimated (CLGN, C1QTNF3, ADORA3, ARHGAP8, DCTN1, ASPSCR1, CHRFAM7A, ZNF223, PDE6G, CXCL3, HEXIM1, HLA-DRB). This 12-gene signature score was significantly associated with RFS (p < 0.0001) and produced a bootstrap C index of 0.888. In univariate analysis, Breslow thickness, presence of primary tumor ulceration, SLN positivity were each significantly associated with RFS. After simultaneously adjusting for these prognostic factors in relation to the gene signature, the 12-gene score remained a significant independent predictor for RFS (p < 0.0001). This SLN 12-gene signature risk score is associated with melanoma recurrence regardless of SLN status and may be used as a prognostic factor for RFS.

Identification of biomarkers for immunotherapy response in prostate cancer and potential drugs to alleviate immunosuppression

Background: Immunotherapy has a significant effect on the treatment of many tumor types. However, prostate cancers generally fail to show significant responses to immunotherapy owing to their immunosuppressive microenvironments. To sustain progress towards more effective immunotherapy for prostate cancer, comprehensive analyses of the genetic characteristics of the immune microenvironment and novel therapeutic strategies are required.

Methods: The transcriptome profiles of patients with prostate cancer were obtained from GEO and processed with the TIDE algorithm to predict their responses to immunotherapy. Next, the significant differentially expressed genes (DEGs) between the responder and non-responder groups were identified and used to compute the co-expression modules by WGCNA. Then, co-expression networks were constructed and survival analysis was applied to hub genes. Finally, drug candidates to alleviate immunosuppression were filtered in prostate cancer using GSEA based on hub genes.

Results: In total, we identified 2758 significant DEGs and constructed 16 co-expression modules, seven of which were significantly correlated with the immune response score. In total, 133 hub genes were identified, of which 13 were significantly associated with prostate cancer prognosis. Co-expression networks of hub genes were constructed with KMT2B at the center. Finally, six candidate drugs for prostate cancer immunotherapy were identified in PC3 and LNCaP cell lines.

Conclusions: We obtained datasets from multiple platforms, performed integrated bioinformatic analysis to identify 133 hub genes and 13 biomarkers of an immunotherapy response, and six candidate drugs were filtered to inhibit the immunosuppressive tumor microenvironment, to ultimately improve patient responses to immunotherapy in prostate cancer.

Prognostic Value of Long Noncoding RNA DLEU2 and Its Relationship with Immune Infiltration in Kidney Renal Clear Cell Carcinoma and Liver Hepatocellular Carcinoma

Background

DLEU2 is a long noncoding RNA considered important in the progression of many cancers. However, correlations between DLEU2 and kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) have rarely been reported.

Methods

We first analysed the expression of DLEU2 across cancers and the correlation between DLEU2 and the clinical features of KIRC and LIHC by using the “ggplot2” package in R and searched the Oncomine database and Timer website platform. We verified the expression of DLEU2 in the GEO dataset (GSE105261 and GSE45267). Receiver operating characteristic (ROC) curves were drawn using the “pROC” and “ggplot2” packages in R, and we constructed a DLEU2-based prognostic nomogram for KIRC and LIHC by using the “survival” and “rms” packages in R. Then, we analysed the correlation between DLEU2 expression and prognosis in R as well as the correlation between DLEU2 and immune cell infiltration in the TIMER database. Finally, we explored the causes of DLEU2 upregulation in the UCSC Xena and UALCAN databases.

Results

We found that DLEU2 was upregulated in many cancers, including KIRC and LIHC. Expression of DLEU2 is associated with tumour stage, grade, lymphatic metastasis, and distant metastasis in KIRC as well as alpha-fetoprotein (AFP), tumour stage, grade, lymphatic metastasis, and distant metastasis in LIHC. DLEU2 is an adverse factor for the prognosis of KIRC and LIHC. In addition, DLEU2 has moderate accuracy in diagnosing KIRC and LIHC and predicting their prognosis. Moreover, we found that expression of DLEU2 correlated positively with immune cell infiltration in KIRC and LIHC, and upregulation of DLEU2 in KIRC and LIHC suggests a poor prognosis based on immune cells analysis. Genetic and epigenetic analyses of DLEU2 indicate that copy number variations (CNVs) and methylation contribute to the upregulation of DLEU2.

Conclusion

The long noncoding RNA DLEU2 has the potential to predict the prognosis and immune infiltration of KIRC and LIHC.