Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology

OBJECTIVE

The impact of inflammatory response on tumor development and therapeutic response is of significant importance in clear cell renal cell carcinoma (ccRCC). The customization of specialized prognostication approaches and the exploration of supplementary treatment options hold critical clinical implications in relation to the inflammatory response.

METHODS

In the present study, unsupervised clustering was implemented on TCGA-KIRC tumors using transcriptome profiles of inflammatory response genes, which was then validated in two ccRCC datasets (E-MATB-1980 and ICGC) and two immunotherapy datasets (IMvigor210 and Liu et al.) via SubMap and NTP algorithms. Combining co-expression and LASSO analyses, inflammatory response-based scoring system was defined, which was evaluated in pan-cancer.

RESULTS

Three reproducible inflammatory response subtypes (named IR1, IR2 and IR3) were determined and independently verified, each exhibiting distinct molecular, clinical, and immunological characteristics. Among these subtypes, IR2 had the best OS outcomes, followed by IR3 and IR1. In terms of anti-angiogenic agents, sunitinib may be appropriate for IR1 patients, while axitinib and pazopanib may be suitable for IR2 patients, and sorafenib for IR3 patients. Additionally, IR1 patients might benefit from anti-CTLA4 therapy. A scoring system called IRscore was defined for individual ccRCC patients. Patients with high IRscore presented a lower response rate to anti-PD-L1 therapy and worse prognostic outcomes. Pan-cancer analysis demonstrated the immunological features and prognostic relevance of the IRscore.

CONCLUSION

Altogether, characterization of inflammatory response subtypes and IRscore provides a roadmap for patient risk stratification and personalized treatment decisions, not only in ccRCC, but also in pan-cancer.

Collagen Remodeling along Cancer Progression Providing a Novel Opportunity for Cancer Diagnosis and Treatment

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

M2-Polarization-related CNTNAP1 gene might be a novel immunotherapeutic target and biomarker for clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common malignancies, characterized by high mortality rate in urology. Unfortunately, reliable biomarkers for ccRCC diagnosis and prognosis remain lacking. Contactin Associated Protein 1 (CNTNAP1) has yet to be thoroughly investigated in cancer, especially its relationship with immune infiltration or clinical outcomes of ccRCC. Here, we explored the Cancer Genome Atlas Kidney Clear Cell Carcinoma database (TCGA-KIRC) for prognostic significance, differential expression, and probable mechanism of CNTNAP1. The aberrant CNTNAP1 expression was also validated by international Cancer Genome Consortium (ICGC) and ccRCC clinic samples. We used Database for Annotation, Visualization, and Integrated Discovery (DAVID) to performed the GO & KEGG enrichment. TIMER database was further utilized to assess its correlation with immune infiltration in ccRCC. The the CellMiner database was used to analyse the relationship between CNTNAP1 expression and drug sensitivity. Results showed CNTNAP1 was upregulated in TCGA-KIRC, ICGC and clinic samples. And CNTNAP1 expression was positively related to infiltration levels of cancer-associated fibroblast, regulatory T cells, and Myeloid-derived suppressor cells, while negatively related to eosinophils. Furthermore, we observed CNTNAP1 was appreciably positively associated with alternatively activated macrophage (M2) in ccRCC. Finally, high CNTNAP1 expression was negatively correlated with Nilotinib, Crizotinib, Eribulin mesylate, and Vinorelbine. Collectively, these results strongly suggest that CNTNAP1 might act as an immunotherapeutic target and a promising novel biomarker for ccRCC.