Identification of an independent immune-genes prognostic index for renal cell carcinoma

Construction of a novel immune response prediction signature to predict the efficacy of immune checkpoint inhibitors in clear cell renal cell carcinoma patients

Background

Immune checkpoint inhibitor (ICI) treatment has enhanced survival outcomes in clear cell renal cell carcinoma (ccRCC) patients. Nevertheless, the effectiveness of immunotherapy in ccRCC patients is restricted and we intended to develop and characterize an immune response prediction signature (IRPS) to forecast the efficacy of immunotherapy.

Methods

RNA-seq expression profile and clinicopathologic characteristics of 539 kidney cancer and 72 patients with normal specimens, were downloaded from the Cancer Genome Atlas (TCGA) database, while the Gene Expression Omnibus (GEO) database was used as the validation set, which included 24 ccRCC samples. Utilization of the TCGA data and immune genes databases (ImmPort and the InnateDB), we explored through Weighted Gene Co-expression Network Analysis (WGCNA), along with Least Absolute Shrinkage and Selection Operator method (LASSO), and constructed an IRPS for kidney cancer patients. GSEA and CIBERSORT were performed to declare the molecular and immunologic mechanism underlying the predictive value of IRPS. The Human Protein Atlas (HPA) was deployed to verify the protein expressions of IRPS genes. Tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS) were computed to determine the risk of immune escape and value the discrimination of IRPS. A ccRCC cohort with anti-PD-1 therapy was obtained as an external validation data set to verify the predictive value of IRPS.

Results

We constructed a 10 gene signature related to the prognosis and immune response of ccRCC patients. Considering the IRPS risk score, patients were split into high and low risk groups. Patients with high risk in the TCGA cohort tended towards advanced tumor stage and grade with poor prognosis (p < 0.001), which was validated in GEO database (p = 0.004). High-risk group tumors were related with lower PD-L1 expression, higher TMB, higher MSIsensor score, lower IPS, higher TIDE score, and enriched Treg cells, which might be the potential mechanism of immune dysfunction and exclusion. Patients in the IRPS low risk group had better PFS (HR:0.73; 95% CI: 0.54-1.0; P = 0.047).

Conclusion

A novel biomarker of IRPS was constructed to predict the benefit of immunotherapy, which might lead to more individualized prognoses and tailored therapy for kidney cancer patients.

Development of a Novel Immune-Related Gene Prognostic Index for Breast Cancer

Objective

To construct an immune-related gene prognostic index (IRGPI) for breast cancer (BC) and investigate its prognostic specificity and the molecular and immune characteristics.

Methods

BC hub genes were identified from The Cancer Genome Atlas and immune-related databases using weighted gene co-expression network analysis (WGCNA). IRGPI was constructed using univariate, LASSO, and multivariate regression analyses, and was validated with GSE58812 and GSE97342 in the Gene Expression Omnibus database (GEO). At the same time, we evaluated the predictive ability of IRGPI for different BC subtypes. Subsequently, the molecular and immune characteristics, clinical relevance, and benefits of immune checkpoint inhibitor treatment were analyzed for different IRGPI subgroups.

Results

IRGPI consisted of six genes: SOCS3, TCF7L2, TSLP NPR3, ANO6, and HMGB3. The IRGPI 1-, 5-, and 10-years area under curve (AUC) values were 0.635, 0.752, and 0.753, respectively, indicating that IRGPI has good potential in predicting the long-term survival of BC patients, consistent with the results in the GEO cohort. IRGPI showed good predictive power in four different breast cancer subtypes: ER positive, PR positive, HER2 positive and triple-negative (P<0.01). Compared with the low-IRGPI group, the high-IRGPI group had a worse prognosis and a lower degree of immune infiltrating cells (p < 0.05). IRGPI showed specificity in distinguishing age, TNM stage, ER, and HER2 statuses, and our study found that the high-IRGPI group had low tumor immune dysfunction and exclusion (TIDE), microsatellite instability (MSI), and T cell dysfunction scores (p < 0.05). In addition, compared with the TIDE and TIS models, showed that the AUCs of IRGPI were better during the 5-year follow-up.

Conclusion

IRGPI can be used as an independent prognostic indicator of breast cancer, providing a method for monitoring the long-term treatment of BC.