Association of genetic variants in autophagy-lysosome pathway genes with susceptibility and survival to prostate cancer

Identification, and Experimental and Bioinformatics Validation of an Immune-Related Prognosis Gene Signature for Low-Grade Glioma Based on mRNAsi

BACKGROUND

Low-grade gliomas (LGGs), which are the second most common intracranial tumor, are diagnosed in seven out of one million people, tending to develop in younger people. Tumor stem cells and immune cells are important in the development of tumorigenesis. However, research on prognostic factors linked to the immune microenvironment and stem cells in LGG patients is limited. We critically need accurate related tools for assessing the risk of LGG patients.

METHODS

In this study, we aimed to identify immune-related genes (IRGs) in LGG based on the mRNAsi score. We employed differentially expressed gene (DEG) methods and weighted correlation network analysis (WGCNA). The risk signature was then further established using a lasso Cox regression analysis and a multivariate Cox analysis. Next, we used immunohistochemical sections (HPA) and a survival analysis to identify the hub genes. A nomogram was built to assess the prognosis of patients based on their clinical information and risk scores and was validated using a DCA curve, among other methods.

RESULTS

Four hub genes were obtained: C3AR1 (HR = 0.98, p < 0.001), MSR1 (HR = 1.02, p < 0.001), SLC11A1 (HR = 1.01, p < 0.01), and IL-10 (HR = 1.01, p < 0.001). For LGG patients, we created an immune-related prognostic signature (IPS) based on mRNAsi for estimating risk scores; different risk groups showed significantly different survival rates (p = 3.3 × 10^-16). Then, via an evaluation of the IRG-related signature, we created a nomogram for predicting LGG survival probability.

CONCLUSION

The outcome suggests that, when predicting the prognosis of LGG patients, our nomogram was more effective than the IPS. In this study, four immune-related predictive biomarkers for LGG were identified and proven to be IRGs. Therefore, the development of efficient immunotherapy techniques can be facilitated by the creation of the IPS.

A Multigene-Panel Study Identifies Single Nucleotide Polymorphisms Associated with Prostate Cancer Risk

The immune system plays a critical role in modulating cancer development and progression. Polymorphisms in key genes involved in immune responses are known to affect susceptibility to cancer. Here, we analyzed 35 genes to evaluate the association between variants of genes involved in immune responses and prostate cancer risk. Thirty-five genes were analyzed in 47 patients with prostate cancer and 43 healthy controls using next-generation sequencing. Allelic and genotype frequencies were calculated in both cohorts, and a generalized linear mixed model was applied to test the relationship between prostate cancer risk and nucleotide substitution. Odds ratios were calculated to describe the association between each single nucleotide polymorphism (SNP) and prostate cancer risk. Significant changes in allelic and genotypic distributions were observed for IL4R, IL12RB1, IL12RB2, IL6, TMPRSS2, and ACE2. Furthermore, a generalized linear mixed model identified statistically significant associations between prostate cancer risk and SNPs in IL12RB2, IL13, IL17A, IL4R, MAPT, and TFNRS1B. Finally, a statistically significant association was observed between IL2RA and TNFRSF1B and Gleason scores, and between SLC11A1, TNFRSF1B and PSA values. We identified SNPs in inflammation and two prostate cancer-associated genes. Our results provide new insights into the immunogenetic landscape of prostate cancer and the impact that SNPs on immune genes may have on affecting the susceptibility to prostate cancer.

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer

Background: Prostate cancer (PCa) is highly heterogeneous, which makes it difficult to precisely distinguish the clinical stages and histological grades of tumor lesions, thereby leading to large amounts of under- and over-treatment. Thus, we expect the development of novel prediction approaches for the prevention of inadequate therapies. The emerging evidence demonstrates the pivotal role of lysosome-related mechanisms in the prognosis of PCa. In this study, we aimed to identify a lysosome-related prognostic predictor in PCa for future therapies.

Methods: The PCa samples involved in this study were gathered from The Cancer Genome Atlas database (TCGA) (n = 552) and cBioPortal database (n = 82). During screening, we categorized PCa patients into two immune groups based on median ssGSEA scores. Then, the Gleason score and lysosome-related genes were included and screened out by using a univariate Cox regression analysis and the least absolute shrinkage and selection operation (LASSO) analysis. Following further analysis, the probability of progression free interval (PFI) was modeled by using unadjusted Kaplan–Meier estimation curves and a multivariable Cox regression analysis. A receiver operating characteristic (ROC) curve, nomogram and calibration curve were used to examine the predictive value of this model in discriminating progression events from non-events. The model was trained and repeatedly validated by creating a training set (n = 400), an internal validation set (n = 100) and an external validation (n = 82) from the cohort.

Results: Following grouping by ssGSEA score, the Gleason score and two LRGs—neutrophil cytosolic factor 1 (NCF1) and gamma-interferon-inducible lysosomal thiol reductase (IFI30)—were screened out to differentiate patients with or without progression (1-year AUC = 0.787; 3-year AUC = 0.798; 5-year AUC = 0.772; 10-year AUC = 0.832). Patients with a higher risk showed poorer outcomes (p &lt; 0.0001) and a higher cumulative hazard (p &lt; 0.0001). Besides this, our risk model combined LRGs with the Gleason score and presented a more accurate prediction of PCa prognosis than the Gleason score alone. In three validation sets, our model still achieved high prediction rates.

Conclusion: In conclusion, this novel lysosome-related gene signature, coupled with the Gleason score, works well in PCa for prognosis prediction.

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.

Development of a Platelet-Related Prognostic Model for Colorectal Cancer

Colorectal cancer (CRC) represents one of the most common malignancies with high morbidity worldwide. Growing evidence has suggested that platelets are a fundamental component of the tumor microenvironment and play crucial roles in driving tumor biological behavior. The construction of a platelet-related prognostic model that can reliably predict CRC prognosis is of great clinical significance. The 1427 CRC-specific platelet-related genes were collected and mainly enriched in the ribosome and immune-related pathways. Based on platelet-related genes, three subtypes of TCGA CRC samples were identified by consensus clustering and characterized by differences in angiogenesis, epithelial–mesenchymal transition, immune infiltration, and prognosis. A total of 100 prognostic platelet-related genes were identified by univariate Cox regression. LASSO Cox regression further shrank those genes and constructed a 10-gene prognostic model. The patients with higher risk scores had significantly worse disease-specific survival than those with lower scores in both TCGA and validation cohorts. The risk score demonstrated good predictive performance for prognosis by receiver operating characteristic (ROC) curves. Furthermore, multivariate Cox regression analysis showed that the risk score was independent of TNM stage, sex, and age, and a graphic nomogram based on the risk score and clinical factors was developed to predict survival probability of CRC patients. Patients from the high-risk group were characterized by higher infiltration of immunosuppressive cells such as MDSC and Treg and higher expression of checkpoints CTLA4, CD86, and PDCD1LG2. Taken together, we identified three platelet-related subtypes and specifically constructed a promising 10-gene prognostic model in CRC. Our results highlighted the potential survival effects of platelet-related genes and provided evidence about their roles in regulating tumor immunity.