Prostate Cancer Survival and Mortality according to a 13-year retrospective cohort study in Brazil: Competing-Risk Analysis

Allelic variants and immunostaining profile in CXCL12/CXCR4 axis: An investigation of association with prognosis in prostate cancer

Prostate cancer (PCa) is the malignant neoplasm that most commonly affects men and is an important cause of death. It can be detected by changes in serum levels of Prostate Specific Antigen (PSA) and digital rectal examination, but often symptoms do not appear until advanced stages and metastases. The C-X-C Motif Chemokine Ligand 12/C-X-C Motif Chemokine Receptor 4 (CXCL12/CXCR4) axis acts in cell migration and may be involved in the metastatic process. In this context, the aim of this study was to evaluate the allelic variants rs1801157 (CXCL12) and rs2228014 (CXCR4) and the immunostaining of CXCR4 protein as candidates for prognostic markers in PCa. Samples (n = 60) were divided according to prognostic parameters (with and without metastasis at diagnosis) in tree groups: better prognosis, worse prognosis with metastasis at diagnosis and worse prognosis without metastasis at diagnosis, and immunostaining was evaluated by indirect immunohistochemistry, considering tumoral and adjacent tissues from the same patient (n = 120). A significant association was found between the C allele of rs2228014 (CXCR4) and the extraprostatic extension. For CXCR4 immunostaining a weak labeling and a cytoplasmic localization predominated, as well as a significant difference between malignant versus adjacent tissue, with higher protein expression in the malignant tissue. A significant association was found between CXCR4 tumor immunostaining with TNM staging (T2b-T2c) and PSA level (> 20 ng/mL). None of the allelic variants affected CXCR4 immunostaining. Prognostic groups did not differ in allelic variant frequency or immunostaining profile. Findings suggest that CXCR4 receptor may be one of the ways to worsen the prognosis of prostatic cancer.

Treatment Outcomes and Its Associated Factors Among Adult Patients with Selected Solid Malignancies at Kenyatta National Hospital: A Hospital-Based Prospective Cohort Study

Introduction

The treatment outcome of cancer is poor in the African setting due to inadequate treatment and diagnostic facilities. However, there is a paucity of data on solid cancers in Kenya. Hence, this study aimed to investigate the treatment outcomes and its determinant factors among adult patients diagnosed with selected solid malignancies at Kenyatta National Hospital (KNH).

Materials and Methods

A prospective cohort study was employed at the Oncology Department of KNH from 1^st July 2020 to 31^st December 2021. All new patients with a confirmed diagnosis of lymphoma, prostate cancer and breast cancer were studied. A consecutive sample of 99 breast cancer, 50 lymphomas, and 82 prostate cancer patients was included in the study. Semi-structured questionnaires consisting of socio-demographics, clinical characteristics, and quality of life were employed to collect the data. All enrolled patients were followed prospectively for 12 months. Treatment outcomes were reported as mortality, cancer-specific survival and health-related quality of life. The data were entered and analyzed using the SPSS 20.0 statistical software. Survival outcomes and its predictors were evaluated using the Kaplan–Meier analysis and Cox regression analyses, respectively.

Results

The study showed that the mortality rate among breast and prostate cancer patients was 3% and 4.9%, respectively. In contrast, the mortality rate was 10% among lymphoma patients. Most of the patients had partial remission and a good overall global health-related quality of life. Older age above 60 years, co-morbidity, distant metastasis and advanced stages of disease were significant predictors of mortality.

Conclusion

Although the mortality was not high at 12 months, only a few patients had complete remission. For many patients, the disease was progressing, despite 12-month mortality was not high. Therefore, longer follow-up will be required to report cancer mortality accurately. In addition, most of the patients had a good overall global health-related quality of life.

How socioeconomic and clinical factors impact prostate-cancer-specific and other-cause mortality in prostate cancer stratified by clinical stage: Competing-risk analysis

BACKGROUND

The aim of this study was to analyze the causes of death and risk factors of prostate-cancer-specific mortality (PCSM) and other-cause mortality (OCM) at different clinical stages using data from the Surveillance, Epidemiology, and End Results database.

METHODS

The characteristics and cause-specific death classifications of males with prostate cancer (PCa) were extracted. Multivariate competing-risk regression analysis was used to identify significant predictors and quantify the cumulative incidence of PCSM and OCM at different clinical stages.

RESULTS

Of the 244,433 PCa patients who were included, 19,274 died from 7356 PCSM, and 11,918 from OCM. The proportion of PCSM gradually increased from 2010 to 2016. The risk factors for PCSM in the localized PCa stage included older age, not being married, living in a county with higher poverty rates, and higher PSA levels and Gleason scores. Meanwhile, Medicaid and lower education levels were the additional risk factors of OCM. The risk factors for PCSM in the regional PCa stage included older age, not being married, Medicaid, living in a county with higher poverty rates, and higher PSA levels and Gleason scores. Meanwhile, the income level did not affect OCM risk. The risk factors for PCSM in the distant metastatic PCa stage included a separated/divorced/widowed marital status, Medicaid, and higher PSA levels and Gleason scores. Meanwhile, older age, an unmarried or separated/divorced/widowed marital status, and higher PSA levels were risk factors for OCM. In addition, receiving both surgery and radiation was worse than just receiving surgery for PCa specific survival in localized and regional PCa patients.

CONCLUSION

Some pretreatment and treatment factors may influence OCM that are not identical to those for PCSM at the corresponding stage. Decision-makers and managers should fully consider OCM to maximize treatment benefits for PCa.