Exploration of the Value of Combined UA, IL-6, and fPSA/tPSA in the Diagnosis of Prostate Cancer

Technetium-99m-methylene diphosphonate single photon emission computed tomography/computed tomography combined with prostate-specific antigen/free prostate-specific antigen ratio for bone metastasis of prostate cancer

BACKGROUND

Prostate cancer (PC) is one of the most common malignant tumors in men, and bone metastasis is one of its common complications, which seriously affects the quality of life and prognosis of patients.

AIM

To investigate the diagnostic value of technetium-99m-methylene diphosphonate (99mTc-MDP) single photon emission computed tomography (SPECT)/CT imaging combined with the serum prostate-specific antigen (PSA)/free PSA ratio for PC bone metastasis (PCBM).

METHODS

One hundred patients with PC who visited the Hospital of Chengdu University of Traditional Chinese Medicine from January 2020 to January 2022 were recruited as the experimental (Exp) group, while 30 patients with benign prostatic lesions (BPLs) were recruited as the control (Ctrl) group. All patients underwent 99mTc-MDP SPECT/CT imaging and serum PSA/fPSA testing. The SPECT/CT imaging results and serum PSA/fPSA ratios of patients were analyzed to evaluate their diagnostic values for PCBM.

RESULTS

The difference in general information of the patients was not obvious, showing comparability. The two methods showed no visible differences in negative predictive value and sensitivity for patients with PCBM, but had great differences in positive predictive value and specificity (P < 0.05). The PSA/fPSA ratio of patients with PC in the Exp group was lower than those with BPLs, and patients with PCBM had a much lower PSA/fPSA ratio than those without PC (P < 0.05). The results confirmed that the combined use of 99mTc-MDP SPECT/CT imaging and serum PSA/fPSA ratio achieved a detection rate of 95% for PCBM.

CONCLUSION

The combination of 99mTc-MDP SPECT/CT and PSA/fPSA ratio is accurate and reliable for the diagnosis of PCBM, which provides an important reference for clinical practice.

Diagnostic accuracy of interleukin-6 in multiple diseases: An umbrella review of meta-analyses

Objective

This review aims to conduct a comprehensive study of the diagnostic accuracy of interleukin-6 (IL-6) for multiple diseases by utilizing existing systematic reviews and meta-analyses.

Methods

We performed a thorough search of Embase, Web of Science, PubMed, and Cochrane Database of Systematic Reviews up to April 2023 to gather meta-analyses that investigate the diagnostic accuracy of IL-6. To assess the methodological quality of the studies, we employed the Assessing the Methodological Quality of Systematic Reviews-2 and Grading of Recommendations, Assessment, Development and Evaluation criteria.

Results

We included 34 meta-analyses out of the 3024 articles retrieved from the search. These meta-analyses covered 9 categories of diseases of the International Classification of Diseases-11. Studies rated as "Critically Low" or "Very Low" in the quality assessment process were excluded, resulting in a total of 6 meta-analyses that encompassed sepsis, colorectal cancer, tuberculous pleural effusion (TPE), endometriosis, among others. Among these diseases, IL-6 demonstrated a relatively high diagnostic potential in accurately identifying TPE and endometriosis.

Conclusions

IL-6 exhibited favorable diagnostic accuracy across multiple diseases, suggesting its potential as a reliable diagnostic biomarker in the near future. Substantial evidence supported its high diagnostic accuracy, particularly in the cases of TPE and endometriosis.

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.