Identifying prognostic signatures in the microenvironment of prostate cancer

The Negative Impact of Inflammation-Related Parameters in Prostate Cancer after Robot-Assisted Radical Prostatectomy: A Retrospective Multicenter Cohort Study in Japan (the MSUG94 Group)

Background and Objectives: We aimed to examine the relationship between the inflammation-related parameters, such as the neutrophil-to-lymphocyte ratio (NLR), and the pathological findings and biochemical recurrence (BCR) in patients with prostate cancer (PCa) undergoing robot-assisted radical prostatectomy (RARP). Materials and Methods: A retrospective multicenter cohort study of patients with PCa who underwent RARP at 10 institutes in Japan was conducted. This study enrolled 3195 patients. We focused on patients undergoing RARP who underwent the preoperative measurement of their inflammation-related parameters and who did not receive any neo- or adjuvant therapy. Data on the pre- and postoperative variables for the enrolled patients were obtained. The primary endpoint of this study was the association between BCR and the inflammation-related parameters after RARP. The secondary endpoint was the association between the inflammation-related parameters and the pathological diagnosis of PCa. Results: Data from 2429 patients with PCa who met the study's eligibility criteria were analyzed. The median follow-up period was 25.1 months. The inflammation-related parameters were divided into two groups, and cutoff values were determined based on the receiver operating characteristics. There were no statistically significant differences in biochemical recurrence-free survival for any of the parameters. In the univariate analysis, the NLR was predictive of pathological T3 and lymphovascular invasion; however, there were no significant differences in the multivariate analysis. Conclusions: The inflammation-related parameters did not significantly affect the incidence of BCR, at least among patients with PCa who underwent RARP.

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature

BACKGROUND

The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.

METHODS

The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.

RESULTS

The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.

CONCLUSION

Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Prognostic Significance of Amino Acid Metabolism-Related Genes in Prostate Cancer Retrieved by Machine Learning

Prostate cancer is among the leading cancers according to both incidence and mortality. Due to the high molecular, morphological and clinical heterogeneity, the course of prostate cancer ranges from slow growth that usually does not require immediate therapeutic intervention to aggressive and fatal disease that spreads quickly. However, currently available biomarkers cannot precisely predict the course of a disease, and novel strategies are needed to guide prostate cancer management. Amino acids serve numerous roles in cancers, among which are energy production, building block reservoirs, maintenance of redox homeostasis, epigenetic regulation, immune system modulation and resistance to therapy. In this article, by using The Cancer Genome Atlas (TCGA) data, we found that the expression of amino acid metabolism-related genes is highly aberrant in prostate cancer, which holds potential to be exploited in biomarker design or in treatment strategies. This change in expression is especially evident for catabolism genes and transporters from the solute carrier family. Furthermore, by using recursive partitioning, we confirmed that the Gleason score is strongly prognostic for progression-free survival. However, the expression of the genes SERINC3 (phosphatidylserine and sphingolipids generation) and CSAD (hypotaurine generation) can refine prognosis for high and low Gleason scores, respectively. Therefore, our results hold potential for novel prostate cancer progression biomarkers.