Prognostic and Therapeutic Potential of the OIP5 Network in Papillary Renal Cell Carcinoma

IQGAP3 is relevant to prostate cancer: A detailed presentation of potential pathomechanisms

INTRODUCTION

IQGAP3 possesses oncogenic actions; its impact on prostate cancer (PC) remains unclear.

OBJECTIVE

We will investigate IQGAP3's association with PC progression, key mechanisms, prognosis, and immune evasion.

METHODS

IQGAP3 expression in PC was examined by immunohistochemistry and using multiple datasets. IQGAP3 network was analyzed for pathway alterations and used to construct a multigene signature (SigIQGAP3NW). SigIQGAP3NW was characterized using LNCaP cell-derived castration-resistant PCs (CRPCs), analyzed for prognostic value in 26 human cancer types, and studied for association with immune evasion.

RESULTS

Increases in IQGAP3 expression associated with PC tumorigenesis, tumor grade, metastasis, and p53 mutation. IQGAP3 correlative genes were dominantly involved in mitosis. IQGAP3 correlated with PLK1 and TOP2A expression at Spearman correlation/R = 0.89 (p ≤ 3.069e-169). Both correlations were enriched in advanced PCs and Taxane-treated CRPCs and occurred at high levels (R > 0.8) in multiple cancer types. SigIQGAP3NW effectively predicted cancer recurrence and poor prognosis in independent PC cohorts and across 26 cancer types. SigIQGAP3NW stratified PC recurrence after adjustment for age at diagnosis, grade, stage, and surgical margin. SigIQGAP3NW component genes were upregulated in PC, metastasis, LNCaP cell-produced CRPC, and showed an association with p53 mutation. SigIQGAP3NW correlated with immune cell infiltration, including Treg in PC and other cancers. RELT, a SigIQGAP3NW component gene, was associated with elevations of multiple immune checkpoints and the infiltration of Treg and myeloid-derived suppressor cells in PC and across cancer types. RELT and SigIQGAP3NW predict response to immune checkpoint blockade (ICB) therapy.

CONCLUSIONS

In multiple cancers, IQGAP3 robustly correlates with PLK1 and TOP2A expression, and SigIQGAP3NW and/or RELT effectively predict mortality risk and/or resistance to ICB therapy. PLK1 and TOP2A inhibitors should be investigated for treating cancers with elevated IQGAP3 expression. SigIQGAP3NW and/or RELT can be developed for clinical applications in risk stratification and management of ICB therapy.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Chromatin Separation Regulators Predict the Prognosis and Immune Microenvironment Estimation in Lung Adenocarcinoma

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear.

Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database.

Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk.

Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.