Biomarkers Associated With Tumor Ki67 and Cathepsin L Gene Expression in Prostate Cancer Patients Participating in a Presurgical Weight Loss Trial

The association of body mass index with tumor aggression among men undergoing radical prostatectomy

OBJECTIVES

To evaluate the association of preoperative body mass index (BMI) on adverse pathology in peripheral (PZ) and transition zone (TZ) tumors at time of prostatectomy for localized prostate cancer.

METHODS

Clinical and pathologic characteristics were obtained from up to 100 consecutive prostatectomy patients from 10 prostate surgeons. BMI groups included normal (18.5-24.9), overweight (25-29.9) and obese (> 29.9). "Aggressive" pathology was defined as the presence of Grade Group (GG) 3 or higher and/or pT3a or higher. Pathologic characteristics were evaluated for association with BMI using univariate analyses. Our primary outcome was the association of BMI with adverse pathology, which was assessed using logistic regression accounting for patient age. We hypothesized that obese BMI would be associated with aggressive TZ tumor.

RESULTS

Among 923 patients, 140 (15%) were classified as "normal" BMI, 413 (45%) were "overweight", and 370 (40%) were "obese." 474 patients (51%) had aggressive PZ tumors while 102 (11%) had aggressive TZ tumors. "Obese" BMI was not associated with aggressive TZ tumor compared to normal weight. Increasing BMI group was associated with overall increased risk of aggressive PZ tumor (HR 1.56 [95CI 1.04-2.34]; P = 0.03). Among patients with GG1 or GG2, increasing BMI was associated with presence of pT3a or higher TZ tumor (P = 0.03).

CONCLUSIONS

Increased BMI is associated with adverse pathology in PZ tumors. TZ adverse pathology risk may be increased among obese men with GG1 or GG2 disease, which has implications for future studies assessing behavioral change among men whose tumors are actively monitored.

The influence of lifestyle changes (diet, exercise and stress reduction) on prostate cancer tumour biology and patient outcomes: A systematic review

Background

The mostly indolent natural history of prostate cancer (PCa) provides an opportunity for men to explore the benefits of lifestyle interventions. Current evidence suggests appropriate changes in lifestyle including diet, physical activity (PA) and stress reduction with or without dietary supplements may improve both disease outcomes and patient's mental health.

Objective

This article aims to review the current evidence on the benefits of all lifestyle programmes for PCa patients including those aimed at reducing obesity and stress, explore their affect on tumour biology and highlight any biomarkers that have clinical utility.

Evidence acquisition

Evidence was obtained from PubMed and Web of Science using keywords for each section on the affects of lifestyle interventions on (a) mental health, (b) disease outcomes and (c) biomarkers in PCa patients. PRISMA guidelines were used to gather the evidence for these three sections (15, 44 and 16 publications, respectively).

Evidence synthesis

For lifestyle studies focused on mental health, 10/15 demonstrated a positive influence, although for those programmes focused on PA it was 7/8. Similarly for oncological outcomes, 26/44 studies demonstrated a positive influence, although when PA was included or the primary focus, it was 11/13. Complete blood count (CBC)-derived inflammatory biomarkers show promise, as do inflammatory cytokines; however, a deeper understanding of their molecular biology in relation to PCa oncogenesis is required (16 studies reviewed).

Conclusions

Making PCa-specific recommendations on lifestyle interventions is difficult on the current evidence. Nevertheless, notwithstanding the heterogeneity of patient populations and interventions, the evidence that dietary changes and PA may improve both mental health and oncological outcomes is compelling, especially for moderate to vigorous PA. The results for dietary supplements are inconsistent, and although some biomarkers show promise, significantly more research is required before they have clinical utility.

A Bibliometric Analysis on the Research Trend of Exercise and the Gut Microbiome

This article aims to provide an overview of research hotspots and trends in exercise and the gut microbiome, a field which has recently gained increasing attention. The relevant publications on exercise and the gut microbiome were identified from the Web of Science Core Collection database. The publication types were limited to articles and reviews. VOSviewer 1.6.18 (Centre for Science and Technology Studies, Leiden University, Leiden, the Netherlands) and the R package "bibliometrix" (R Foundation: Vienna, Austria) were used to conduct a bibliometric analysis. A total of 327 eligible publications were eventually identified, including 245 original articles and 82 reviews. A time trend analysis showed that the number of publications rapidly increased after 2014. The leading countries/regions in this field were the USA, China, and Europe. Most of the active institutions were from Europe and the USA. Keyword analysis showed that the relationship between disease, the gut microbiome, and exercise occurs throughout the development of this field of research. The interactions between the gut microbiota, exercise, status of the host's internal environment, and probiotics, are important facets as well. The research topic evolution presents a trend of multidisciplinary and multi-perspective comprehensive analysis. Exercise might become an effective intervention for disease treatment by regulating the gut microbiome. The innovation of exercise-centered lifestyle intervention therapy may become a significant trend in the future.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Expression signature, prognosis value and immune characteristics of cathepsin F in non-small cell lung cancer identified by bioinformatics assessment

Background

In recent years, immunotherapies and targeted therapies contribute to population-level improvement in NSCLC cancer-specific survival, however, the two novel therapeutic options have mainly benefit patients containing mutated driven genes. Thus, to explore other potential genes related with immunity or targeted therapies may provide novel options to improve survival of lung cancer patients without mutated driven genes. CTSF is unique in human cysteine proteinases. Presently, CTSF has been detected in several cell lines of lung cancer, but its role in progression and prognosis of lung cancer remains unclear.

Methods

CTSF expression and clinical datasets of lung cancer patients were obtained from GTEx, TIMER, CCLE, THPA, and TCGA, respectively. Association of CTSF expression with clinicopathological parameters and prognosis of lung cancer patients was analyzed using UALCAN and Kaplan–Meier Plotter, respectively. LinkedOmics were used to analyze correlation between CTSF and CTSF co-expressed genes. Protein–protein interaction and gene–gene interaction were analyzed using STRING and GeneMANIA, respectively. Association of CTSF with molecular markers of immune cells and immunomodulators was analyzed with Immunedeconv and TISIDB, respectively.

Results

CTSF expression was currently only available for patients with NSCLC. Compared to normal tissues, CTSF was downregulated in NSCLC samples and high expressed CTSF was correlated with favorable prognosis of NSCLC. Additionally, CTSF expression was correlated with that of immune cell molecular markers and immunomodulators both in LUAD and LUSC. Noticeably, high expression of CTSF-related CTLA-4 was found to be associated with better OS of LUAD patients. Increased expression of CTSF-related LAG-3 was related with poor prognosis of LUAD patients while there was no association between CTSF-related PD-1/PD-L1 and prognosis of LUAD patients. Moreover, increased expression of CTSF-related CD27 was related with poor prognosis of LUAD patients while favorable prognosis of LUSC patients.

Conclusions

CTSF might play an anti-tumor effect via regulating immune response of NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01796-w.

Obesity and prostate cancer: A narrative review

Overweight and obese men with prostate cancer are at an increased risk of disease recurrence, exacerbated treatment-related adverse effects, development of obesity-related comorbidities, earlier progression and development of metastatic disease, and higher all-cause and prostate cancer-specific mortality. The physiological mechanisms associating obesity with poor prostate cancer outcomes remain largely unknown; however, an increased inflammatory environment and metabolic irregularities associated with excess fat mass are commonly postulated. Although research is limited, fat loss strategies using exercise and nutrition programmes may slow down prostate cancer progression and improve a patient's prognosis. This review is an overview of: 1) the association between obesity and poor prostate cancer prognosis; 2) potential physiological mechanisms linking obesity and prostate cancer progression; 3) the effect of obesity on treatments for prostate cancer; and 4) the potential for weight loss strategies to improve outcomes in patients with prostate cancer.

A Systematic Review of Artificial Intelligence in Prostate Cancer

The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.