TNM clinical staging of prostate cancer: issues and solutions

Integrated machine learning identifies epithelial cell marker genes for improving outcomes and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa.

METHODS

Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments.

RESULTS

We identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated.

CONCLUSIONS

Our findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.

Unambiguous radiologic extranodal extension determined by MRI could be a biomarker in predicting metastatic prostate cancer

OBJECTIVE

To explore the relationship between unambiguous radiologic extranodal extension (rENE) and M1 staging in patients with metastatic PCa.

METHODS

A respective analysis of 1073 patients of PCa N1 staging from January 2004 to May 2022 was retrospectively enrolled. They were divided into rENE + and rENE -  groups and retrospectively analyzed the M staging with nuclear medicine data. The correlation index between unambiguous rENE and M1b staging was calculated. Logistic regression was used to evaluate the predictive performance of unambiguous rENE in M1b staging. ROC curves were used to investigate the relationship between unambiguous rENE and M staging in patients who underwent ⁶⁸ Ga-PSMA PET/CT.

RESULTS

A total of 1073 patients were included. Seven hundred and eighty patients were classified into the rENE + group (mean age, 69.6 years ± 8.7 [standard deviation]), and 293 were classified into rENE -  group (mean age, 66.7 years ± 9.4 [standard deviation]). Relationship between unambiguous rENE and M1b existed (r = 0.58, 95%CI: 0.52-0.64, P < 0.05). Unambiguous rENE could be an independent predictor for M1b (OR = 13.64, 95%CI: 9.23-20.14, P < 0.05). The AUC of unambiguous rENE in predicting M1b and M staging was 0.835 and 0.915, respectively, in patients who underwent ⁶⁸ Ga-PSMA PET/CT.

CONCLUSIONS

Unambiguous rENE could be a strong biomarker to predict M1b and M staging in patients with PCa. When rENE came up, patients should perform nuclear medicine immediately, and a systematic treatment should be considered.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

Identification of the Key Genes Involved in the Tumorigenesis and Prognosis of Prostate Cancer

Background. Prostate cancer (PCa) is a malignant tumor in males, with a majority of the cases advancing to metastatic castration resistance. Metastasis is the leading cause of mortality in PCa. The traditional early detection and prediction approaches cannot differentiate between the different stages of PCa. Therefore, new biomarkers are necessary for early detection and clear differentiation of PCa stages to provide precise therapeutic intervention. Methods. The objective of the study was to find significant differences in genes and combine the three GEO datasets with TCGA-PRAD datasets (DEG). Weighted gene coexpression network analysis (WGCNA) determined the gene set and PCa clinical feature correlation module utilizing the TGGA-PRAD clinical feature data. The correlation module genes were rescreened using the biological information analysis tools, with the three hub genes (TOP2A, NCAPG, and BUB1B) for proper verification. Finally, internal (TCGA) and external (GSE32571, GSE70770) validation datasets were used to validate and predict the value of last hub genes. Results. The hub gene was abnormally upregulated in PCa samples during verification. The expression of each gene was favorably connected with the Gleason score and TN tumor grade in clinical samples but negatively correlated with the overall survival rate. The expression of these genes was linked to CD8 naive cells and macrophages, among other cells. Antitumor immune cells like NK and NKT were favorably and adversely correlated with infiltrating cells, respectively. Simultaneously, the GSCV and GSEA indicated that the hub gene is connected with cell proliferation, death, and androgen receptor, among other signaling pathways. Therefore, these genes could influence the incidence and progression of PCa by participating in or modulating various signaling pathways. Furthermore, using the online tool of CMap, we examined the individual medications for Hughes and determined that tipifarnib could be useful for the clinical therapy of PCa. Conclusion. TOP2A, NCAPG, and BUB1B are important genes intimately linked to the clinical prognosis of PCa and can be employed as reliable biomarkers for early diagnosis and prognosis. Moreover, these genes can provide a theoretical basis for precision differentiation and treatment of PCa.

Development and Validation of Nomograms to Predict Cancer-Specific Survival and Overall Survival in Elderly Patients With Prostate Cancer: A Population-Based Study

Objective

Prostate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.

Methods

Information on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D’Amico risk stratification system.

Results

135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.

Conclusions

We developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.

Molecular mechanisms of ferroptosis and its role in prostate cancer therapy

Prostate cancer (PCa) is a highly prevalent disease that affects men's health worldwide and is the second most common malignancy in males. Ferroptosis is a novel form of programmed cell death characterized by iron overload and the accumulation of lipid peroxidation, which differs from the regulated cell death modes of necrosis, apoptosis, and autophagy. Substantial progress has been achieved in researching the occurrence and regulatory mechanisms of ferroptosis, which is closely associated with cancer initiation, progression, and suppression and is expected to become a new breakthrough point in the PCa treatment. This review will summarize the mechanisms involved in PCa, and we detail the molecular mechanisms of ferroptosis and its role in PCa treatment.

Incorporating Prognostic Biomarkers into Risk Assessment Models and TNM Staging for Prostate Cancer

In current practice, prostate cancer staging alone is not sufficient to adequately assess the patient's prognosis and plan the management strategies. Multiple clinicopathological parameters and risk tools for prostate cancer have been developed over the past decades to better characterize the disease and provide an enhanced assessment of prognosis. Herein, we review novel prognostic biomarkers and their integration into risk assessment models for prostate cancer focusing on their capability to help avoid unnecessary imaging studies, biopsies and diagnosis of low risk prostate cancers, to help in the decision-making process between active surveillance and treatment intervention, and to predict recurrence after radical prostatectomy. There is an imperative need of reliable biomarkers to stratify prostate cancer patients that may benefit from different management approaches. The integration of biomarkers panel with risk assessment models appears to improve prostate cancer diagnosis and management. However, integration of novel genomic biomarkers in future prognostic models requires further validation in their clinical efficacy, standardization, and cost-effectiveness in routine application.