Plasma cell subtypes analyzed using artificial intelligence algorithm for predicting biochemical recurrence, immune escape potential, and immunotherapy response of prostate cancer

Genomics and Artificial Intelligence: Prostate Cancer

Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response. As such, AI is becoming an indispensable tool in the pursuit of personalized medicine for prostate cancer.

Identification and validation of anoikis-related lncRNAs for prognostic significance and immune microenvironment characterization in ovarian cancer

Anoikis, a form of apoptotic cell death resulting from inadequate cell-matrix interactions, has been implicated in tumor progression by regulating tumor angiogenesis and metastasis. However, the potential roles of anoikis-related long non-coding RNAs (arlncRNAs) in the tumor microenvironment are not well understood. In this study, five candidate lncRNAs were screened through least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis based on differentially expressed lncRNAs associated with anoikis-related genes (ARGs) from TCGA and GSE40595 datasets. The prognostic accuracy of the risk model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) analyses revealed significant differences in immune-related hallmarks and signal transduction pathways between the high-risk and low-risk groups. Additionally, immune infiltrate analysis showed significant differences in the distribution of macrophages M2, follicular T helper cells, plasma cells, and neutrophils between the two risk groups. Lastly, silencing the expression of PRR34_AS1 and SPAG5_AS1 significantly increased anoikis-induced cell death in ovarian cancer cells. In conclusion, our study constructed a risk model that can predict clinicopathological features, tumor microenvironment characteristics, and prognosis of ovarian cancer patients. The immune-related pathways identified in this study may offer new treatment strategies for ovarian cancer.

A novel immune-related prognostic model with surgical status to predict tumor immune cell infiltration and drug sensitivity in head and neck squamous cell carcinoma

Tumor-infiltrating immune cells (TICs) affect tumorigenesis and tumor development in head and neck squamous cell carcinoma (HNSCC). We constructed a novel predictive model for HNSCC based on immune-related genes (IRGs) from The Cancer Genome Atlas and the Immunology Database and Analysis Portal. After identifying the IRGs, a predictive model involving 13 IRGs with high stratification value of overall survival (OS) was constructed by multiple support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression. We explored the relationship between the risk score (RS) and clinical characteristics. The nomogram showed high concordance and good agreement in OS. Four TICs affected the OS and were in agreement with the abundance analysis of the RS levels. Furthermore, the low-risk HNSCC group showed higher expression of PD-1, CTLA4, and TIGIT, while the high-risk group showed higher expression of EGFR. The high-risk HNSCC showed high sensitivity to eight drugs.

Management of Advanced Prostate Cancer in the Precision Oncology Era

Prostate cancer (PC) is the second leading cause of cancer death in men in the United States. While diversified and improved treatment options for aggressive PC have improved patient outcomes, metastatic castration-resistant prostate cancer (mCRPC) remains incurable and an area of investigative therapeutic interest. This review will cover the seminal clinical data supporting the indication of new precision oncology-based therapeutics and explore their limitations, present utility, and potential in the treatment of PC. Systemic therapies for high-risk and advanced PC have experienced significant development over the past ten years. Biomarker-driven therapies have brought the field closer to the goal of being able to implement precision oncology therapy for every patient. The tumor agnostic approval of pembrolizumab (a PD-1 inhibitor) marked an important advancement in this direction. There are also several PARP inhibitors indicated for patients with DNA damage repair deficiencies. Additionally, theranostic agents for both imaging and treatment have further revolutionized the treatment landscape for PC and represent another advancement in precision medicine. Radiolabeled prostate-specific membrane antigen (PSMA) PET/CT is rapidly becoming a standard of care for diagnosis, and PSMA-targeted radioligand therapies have gained recent FDA approval for metastatic prostate cancer. These advances in precision-based oncology are detailed in this review.