Identification of Novel Diagnosis Biomarkers for Therapy-Related Neuroendocrine Prostate Cancer

CDHu40: a novel marker gene set of neuroendocrine prostate cancer (NEPC)

Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen (PSA) levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor (AR) signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction (PPI) networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named as CDHu40, demonstrated superior performance in distinguishing NE prostate cancer (NEPC) and non-NEPC samples based on gene expression profiles compared to other published marker sets. Notably, some novel marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression.

Significance

our study integrates gene expression variances in multiple NEPC studies and protein-protein interaction network to pinpoint a specific set of NEPC maker genes namely CDHu40. These genes and scores based on their gene expression levels effectively distinguish NEPC samples and underscore the clinical prognostic significance and potential mechanism.

Hsa_circ_0081069 facilitates tongue squamous cell carcinoma progression by modulating MAP2K4 expression via miR-634

It has been demonstrated that circular RNA (circRNA) is involved in the progression of tongue squamous cell carcinoma (TSCC). The aim of this study was to investigate the intrinsic mechanism of circ_0081069 in TSCC progression. The expression levels of circ_00081069, miR-634, and mitogen-activated protein kinase kinase 4 (MAP2K4) in TSCC tissues and cells were detected by quantitative real-time PCR (qRT-PCR). Cell counting kit 8 assay, Edu assay, and flow cytometry assay were used to detect cell proliferation and cell cycle distribution. Transwell assay was used to detect cell migration and invasion abilities. Western blot analysis was performed to detect the protein expression. Dual-luciferase reporter assay was used to detect the targeting relationships of circ_0081069, miR-634 and MAP2K4. Immunohistochemical staining was used to measure MAP2K4-positive cells in tissues. The effect of circ_0081069 silencing on tumor formation in TSCC in vivo was explored by xenograft tumor assay. Circ_0081069 was highly expressed in TSCC tissues and cells. Silencing of circ_0081069 inhibited cell proliferation, cell cycle progress, cell migration and invasion in vitro, as well as hindered tumor growth in vivo. Mechanistically, circ_0081069 targeted miR-634 to negatively regulate miR-634 expression, and inhibition of miR-634 was able to weaken the inhibitory effect of circ_0081069 knockdown on proliferation, migration, and invasion of TSCC cells. MiR-634 targeted MAP2K4 and negatively regulated MAP2K4 expression, and overexpression of miR-634 inhibited TSCC cell proliferation, migration, and invasion, while co-overexpression of MAP2K4 was able to reverse the effects of miR-634 in TSCC cells. Circ_0081069 is involved in the regulation of proliferation, cycle progress, migration, and invasion of TSCC cells through the miR-634/MAP2K4 axis and has the potential to serve as a diagnostic biomarker and therapeutic target.

Molecular predictors of metastasis in patients with prostate cancer

INTRODUCTION

Prostate cancer is a serious threat to the health of older adults worldwide. The quality of life and survival time of patients sharply decline once metastasis occurs. Thus, early screening for prostate cancer is very advanced in developed countries. The detection methods used include Prostate-specific antigen (PSA) detection and digital rectal examination. However, the lack of universal access to early screening in some developing countries has resulted in an increased number of patients presenting with metastatic prostate cancer. In addition, the treatment methods for metastatic and localized prostate cancer are considerably different. In many patients, early-stage prostate cancer cells often metastasize due to delayed observation, negative PSA results, and delay in treatment time. Therefore, the identification of patients who are prone to metastasis is important for future clinical studies.

AREAS COVERED

this review introduced a large number of predictive molecules related to prostate cancer metastasis. These molecules involve the mutation and regulation of tumor cell genes, changes in the tumor microenvironment, and the liquid biopsy.

EXPERT OPINION

In next decade, PSMA PET/CT and liquid biopsy will be the excellent predicting tools, while ¹⁷⁷ Lu- PSMA-RLT will be showed excellent anti-tumor efficacy in mPCa patients.

Novel biomarkers predict prognosis and drug-induced neuroendocrine differentiation in patients with prostate cancer

Background

A huge focus is being placed on the development of novel signatures in the form of new combinatorial regimens to distinguish the neuroendocrine (NE) characteristics from castration resistant prostate cancer (CRPC) timely and accurately, as well as predict the disease-free survival (DFS) and progression-free survival (PFS) of prostate cancer (PCa) patients.

Methods

Single cell data of 4 normal samples, 3 CRPC samples and 3 CRPC-NE samples were obtained from GEO database, and CellChatDB was used for potential intercellular communication, Secondly, using the "limma" package (v3.52.0), we obtained the differential expressed genes between CRPC and CRPC-NE both in single-cell RNA seq and bulk RNA seq samples, and discovered 12 differential genes characterized by CRPC-NE. Then, on the one hand, the diagnosis model of CRPC-NE is developed by random forest algorithm and artificial neural network (ANN) through Cbioportal database; On the other hand, using the data in Cbioportal and GEO database, the DFS and PFS prognostic model of PCa was established and verified through univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox regression in R software. Finally, somatic mutation and immune infiltration were also discussed.

Results

Our research shows that there exists specific intercellular communication in classified clusters. Secondly, a CRPC-NE diagnostic model of six genes (HMGN2, MLLT11, SOX4, PCSK1N, RGS16 and PTMA) has been established and verified, the area under the ROC curve (AUC) is as high as 0.952 (95% CI: 0.882-0.994). The mutation landscape shows that these six genes are rarely mutated in the CRPC and NEPC samples. In addition, NE-DFS signature (STMN1 and PCSK1N) and NE-PFS signature (STMN1, UBE2S and HMGN2) are good predictors of DFS and PFS in PCa patients and better than other clinical features. Lastly, the infiltration levels of plasma cells, T cells CD4 naive, Eosinophils and Monocytes were significantly different between the CRPC and NEPC groups.

Conclusions

This study revealed the heterogeneity between CRPC and CRPC-NE from different perspectives, and developed a reliable diagnostic model of CRPC-NE and robust prognostic models for PCa.

Chromogranin A: a useful biomarker in castration-resistant prostate cancer

PURPOSE

The natural history of prostate cancer (PC) almost always evolves to castration-resistant prostate cancer (CRPC) status, sometimes comprising pure or mixed neuroendocrine prostate cancers (NEPC) differentiation. In CRPC, monitoring using only prostate-specific antigen (PSA) is not optimal since neuroendocrine differentiated cells do not secrete PSA. Thus, monitoring with PSA and chromogranin A (CgA) may be useful. This review aims to evaluate evidence for the usefulness of CgA assessments during the monitoring of prostate cancer.

METHOD

This review was based on three recent meta-analysis concerning CgA and prostate cancer. Further data were obtained from PubMed and Embase databases by searches using keywords, including chromogranin A and prostate cancer.

RESULTS

CgA levels remain largely unchanged during the early PC evolution. The development of NEPC is characterised by lower PSA secretion and increased CgA secretion. Data supporting the prognostic value of high CgA baseline levels for survival are contrasting and scarce. However, increasing CgA levels early during treatment of metastatic (m)CRPC suggests resistance to treatment and predicts shorter survival, particularly in men with high baseline levels of CgA levels. In men with mCRPC, the first-line chemotherapy may be more appropriate than other agents when baseline CgA levels are high. Also, increasing CgA levels during treatment may indicate disease progression and may warrant a change of therapy.

CONCLUSION

CgA monitoring at baseline and regularly during mCRPC management may be useful for monitoring disease evolution. An increased CgA baseline levels and increasing CgA levels may assist physicians with choosing and modifying therapy.

Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Objective

Post-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.

Methods

Five-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.

Results

The DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.

Conclusion

We developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.