International Journal of Biological Markers最新文献

Introduction: There is a need for a rapid, accurate, less-invasive approach to distinguishing malignant from benign pleural effusions. We investigated the diagnostic value of five pleural tumor markers in exudative pleural effusions.

Methods: By immunochemiluminescence assay, we measured pleural concentrations of tumor markers. We used the receiver operating characteristic curve analysis to assess their diagnostic values.

Results: A total of 281 patients were enrolled. All tumor markers were significantly higher in malignant pleural effusions than benign ones. The area under the curve of carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 15-3, cytokeratin fragment 19 (CYFRA) 21-1, CA-19-9, and CA-125 were 0.81, 0.78, 0.75, 0.65, and 0.65, respectively. Combined markers of CEA + CA-15-3 and CEA + CA-15-3 + CYFRA 21-1 had a sensitivity of 87% and 94%, and specificity of 75% and 58%, respectively. We designed a diagnostic algorithm by combining pleural cytology with pleural tumor marker assay. CEA + CYFRA 21-1 + CA-19-9 + CA-15-3 was the best tumor markers panel detecting 96% of cytologically negative malignant pleural effusions, with a negative predictive value of 98%.

Conclusions: Although cytology is specific enough, it has less sensitivity in identifying malignant pleural fluids. As a result, the main gap is detecting malignant pleural effusions with negative cytology. CEA was the best single marker, followed by CA-15-3 and CYFRA 21-1. Through both cytology and suggested panels of tumor markers, malignant and benign pleural effusions could be truly diagnosed with an accuracy of about 98% without the need for more invasive procedures, except for the cohort with negative cytology and a positive tumor markers panel, which require more investigations.

Objective: Peripheral systemic inflammatory, nutritional, and coagulation biomarkers have prognostic and predictive value in various malignancies. We evaluated the prognostic and predictive roles of systemic inflammatory, nutritional, and coagulation biomarkers in the circulating blood of patients with advanced cervical cancer.

Methods: A retrospective study of 795 patients with cervical cancer who received concurrent chemoradiation therapy was performed. Overall survival was evaluated by the Kaplan-Meier estimator. Univariate and multivariate Cox regression models were used to determine prognostic factors associated with overall survival.

Results: The median follow-up time was 76 months. In the univariate analysis, overall survival showed positive prognostic value in patients with a platelet-to-lymphocyte ratio (PLR) <164.29 (P = 0.010), and a plasma fibrinogen (FIB) level <4 g/L(P = 0.012). In the multivariate analysis, the PLR (P = 0.036), and FIB level (P = 0.047) maintained their significance for overall survival. Therefore, the PLR and FIB levels are independent prognostic factors in patients with advanced cervical cancer.

Conclusions: Systemic inflammatory and coagulation biomarkers could help to understand survival differences in the clinical treatment of advanced cervical cancer. The PLR and FIB levels are independent prognostic factors of poor survival in patients with advanced cervical cancer.

Background: The DNA mismatch repair system is one of the defense mechanisms in the body, and the inactivation of mismatch repair plays a pivotal role in secondary carcinogenesis and progression. However, the significance of mismatch repair in esophageal squamous cell carcinoma (ESCC) has not been established. In this study, we explored the diagnostic and prognostic significance of mismatch repair markers, mutL homologue 1 (MLH1), post-meiotic segregation increased 2 (PMS2), mutS homologue 2 (MSH2), and mutS homologue 6 (MSH6), in patients with ESCC.

Methods: We used a notation based on the proportion of immunoreactivity/expression for immunohistochemistry (PRIME notation), which allows the comparison of mismatch repair expression by assigning a score to PRIME notation. MLH1, PMS2, MSH2, and MSH6 were examined immunohistochemically in 189 surgically resected ESCC specimens.

Results: A total of 100/189 patients with ESCC (53%) received preoperative chemotherapy. The rates of ESCC cases with decreased mismatch repair status were 13.2%, 15.3%, 24.8%, and 12.6% for MLH1, PMS2, MSH2, and MSH6, respectively. The decreased status of individual mismatch repair markers was significantly correlated with worse prognosis in patients with ESCC. Additionally, MSH2, MSH6, and PMS2 were significantly associated with response to preoperative chemotherapy. Multivariate analysis revealed that MLH1, PMS2, and MSH2 are independent prognostic factors.

Conclusion: Our results suggest that mismatch repair is a prognostic biomarker for ESCC and could contribute to the selection of appropriate adjuvant therapy for patients with ESCC.

Background: Dysbiosis commonly occurs in pancreatic cancer, but its specific characteristics and interactions with pancreatic cancer remain obscure.

Materials and methods: The 16S rRNA sequencing method was used to analyze multisite (oral and gut) microbiota characteristics of pancreatic cancer, chronic pancreatitis, and healthy controls. Differential analysis was used to identify the pancreatic cancer-associated genera and pathways. A random forest algorithm was adopted to establish the diagnostic models for pancreatic cancer.

Results: The chronic pancreatitis group exhibited the lowest microbial diversity, while no significant difference was found between the pancreatic cancer group and healthy controls group. Diagnostic models based on the characteristics of the oral (area under the curve (AUC) 0.916, 95% confidence interval (CI) 0.832-1) or gut (AUC 0.856; 95% CI 0.74, 0.972) microbiota effectively discriminate the pancreatic cancer samples in this study, suggesting saliva as a superior sample type in terms of detection efficiency and clinical compliance. Oral pathogenic genera (Granulicatella, Peptostreptococcus, Alloprevotella, Veillonella, etc.) and gut opportunistic genera (Prevotella, Bifidobacterium, Escherichia/Shigella, Peptostreptococcus, Actinomyces, etc.), were significantly enriched in pancreatic cancer. The 16S function prediction analysis revealed that inflammation, immune suppression, and barrier damage pathways were involved in the course of pancreatic cancer.

Conclusion: This study comprehensively described the microbiota characteristics of pancreatic cancer and suggested potential microbial markers as non-invasive tools for pancreatic cancer diagnosis.

Background: To evaluate the diagnostic value of combinations of tumor markers carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, and CA19-9 in identifying malignant pleural effusion (MPE) from non-malignant pleural effusion (non-MPE) using machine learning, and compare the performance of popular machine learning methods.

Methods: A total of 319 samples were collected from patients with pleural effusion in Beijing and Wuhan, China, from January 2018 to June 2020. Five machine learning methods including Logistic regression, extreme gradient boosting (XGBoost), Bayesian additive regression tree, random forest, and support vector machine were applied to evaluate the diagnostic performance. Sensitivity, specificity, Youden's index, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of different diagnostic models.

Results: For diagnostic models with a single tumor marker, the model using CEA, constructed by XGBoost, performed best (AUC = 0.895, sensitivity = 0.80), and the model with CA153, also by XGBoost, showed the largest specificity 0.98. Among all combinations of tumor markers, the combination of CEA and CA153 achieved the best performance (AUC = 0.921, sensitivity = 0.85) in identifying MPE under the diagnostic model constructed by XGBoost.

Conclusions: Diagnostic models for MPE with a combination of multiple tumor markers outperformed the models with a single tumor marker, particularly in sensitivity. Using machine learning methods, especially XGBoost, could comprehensively improve the diagnostic accuracy of MPE.

Background: Liver cancer seriously threatens human health. Natural killer (NK) cells are an important part of the innate immune system and have strong anti-tumor ability. Immunotherapy based on NK cells has become a hot topic in the treatment of liver cancer.

Methods: In this study, we checked the serum DKK3 (sDKK3) and circulating CD56^bright NK cells using ELISA and flow cytometry, respectively, in the blood of liver cancer patients. The effect on recombinant human DKK3 (rhDKK3) on CD56^bright NK cells was analyzed in vitro.

Results: We found low levels of sDKK3 in liver cancer patients and a negative correlation between sDKK3 and circulating CD56^bright NK cells. In addition, we found that DKK3 induced the differentiation and improved the cytotoxicity of CD56^bright NK cells for the first time. It could be used as an agonist for NK cell-based immunotherapy.

Conclusions: Improving the clinical efficacy of NK cells through DKK3 will become a new strategy for cancer immunotherapy.

Background: The Gleason Score is well correlated with biological behavior and prognosis in prostate adenocarcinoma (PRAD). This study was derived to determine the clinical significance and function of Gleason-Score-related genes in PRAD.

Methods: RNA-sequencing profiles and clinical data were extracted from the The Cancer Genome Atlas PRAD database. The Gleason-Score-related genes were screened out by the Jonckheere-Terpstra rank-based test. The "limma" R package was performed for differentially expressed genes. Next, a Kaplan-Meier survival analysis was performed. Correlation MT1L expression levels with tumor stage, non-tumor tissue stage, radiation therapy, and residual tumor were analyzed. Further, MT1L expression was detected in PRAD cell lines by reverse transcription-quantitative polymerase chain reaction assay. Overexpression of MT1L was constructed and used for cell count kit-8, flow cytometric assay, transwell assay, and wound-healing assay.

Results: Survival analysis showed 15 Gleason-Score-related genes as prognostic biomarkers in PRAD. The high-frequency deletion of MT1L was verified in PRAD. Furthermore, MT1L expression was decreased in PRAD cell lines than RWPE-1 cells, and overexpression of MT1L repressed cell proliferation and migration, and induced apoptosis in PC-3 cells.

Conclusion: Gleason-Score-related MT1L may serve as a biomarker of poor prognostic biomarker in PRAD. In addition, MT1L plays a tumor suppressor in PRAD progression, which is beneficial for PRAD diagnosis and treatment research.

Personalized Medicine is a novel medical practice that uses an individual's genetic profile to guide decisions made regarding the prevention, diagnosis, and treatment of disease. Knowledge of a patient's genetic profile is crucial to support doctors in selecting the proper therapy and administer it using the correct dose or regimen. Personalized Medicine is a great opportunity to turn the "one size fits all" approach to diagnostics, therapy, and prevention, into an individualized approach. In this paper we analyze the most recent achievements and regulatory challenges in Personalized Medicine and the role that research infrastructures can play in advancing its development.

The relationship between PLIN2 expression and prognosis, and clinicopathological significance of various cancers has been extensively studied, but the results are not completely consistent. This review followed the guidelines for systematic reviews of prognostic factors studies and was reported under the Preferred Reporting Program for Systematic Reviews and Meta-Analysis (PRISMA). We searched PubMed, Embase, Cochrane Library, Web of Science, and Google Academia for relevant articles up to September 2, 2022, and calculated the pooled hazard ratios (HR) with 95% confidence intervals (CI) to determine the association between PLIN2 expression and the prognosis of various cancers. The meta-analysis ultimately included 17 studies. The quality of all included cohort studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool, and an adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to assess the certainty of the results. High expression of PLIN2 was associated with poorer overall survival (HR  =  1.65; 95% CI  =  1.14, 2.38; P  =  0.008), metastasis-free survival (HR  =  1.48; 95% CI  =  1.12, 1.94; P  =  0.005), progression-free survival (HR  =  2.11; 95% CI  =  1.55, 2.87; P < 0.0005) and recurrence-free survival/relapse-free survival (HR  =  2.21; 95% CI  =  1.64, 2.98; P < 0.0005) in cancers. The clinicopathological parameters of digestive system malignancies suggested that high expression of PLIN2 was notably associated with distant metastasis ( + ) (odds ratio (OR)  =   3.37; 95% CI  =  1.31, 8.67; P  =  0.012), lymph node metastasis ( + ) (OR  =  1.61; 95% CI  =  1.01, 2.54; P  =  0.004), and tumor stage (III-IV) (OR  =   1.96; 95% CI  =   1.24, 3.09; P  =  0.006). In summary, overexpression of PLIN2 is significantly associated with a poor prognosis in various human cancers, especially in respiratory and digestive malignancies. Thus, PLIN2 expression may be a potential prognostic biomarker in cancer patients.

Background: Programmed death-ligand 1 (PD-L1) expression in some tumors has prognostic implications. This work aims at investigating PD-L1 expression in diffuse large B-cell lymphoma (DLBCL) and to study its association with clinicopathological variables.

Methods: The study consisted of 75 DLBCL patients who were cared for at the King Hussein Cancer Center during the period 2015-2018. The expression of PD-L1 in tumor tissue was assessed by immunohistochemistry using the anti-human PD-L1 (Clone 22C3) monoclonal antibody. The correlation between gender, age, clinical stage, pre-treatment-LDH level, tumor location, response to therapy, overall and event-free survival with PD-L1 expression was studied.

Results: Six patients were excluded from further analysis as they were in relapse at the time of tissue sampling. The tumor proportion score (TPS) was ≥1% in 16/69 (23.2%) of DLBCL cases while the combined positive score (CPS) at a cut-off of ≥20 was observed in 23/69 (33.3%) cases. No significant difference in PD-L1 expression was found between germinal center B-cell-like (GCB) and non-GCB subtypes. Similarly, no differences in PD-L1 expression (at CPS ≥20 and TPS ≥1) were found between different genders, age groups, clinical stages, tumor location, and patient response to therapy. However, base-line lactate dehydrogenase was significantly elevated in patients with PD-L1 CPS ≥20. The overall survival was not significantly different between PD-L1-positive and -negative groups. On the other hand, the median event-free survival was higher in either of the PD-L1 TPS or CPS negative groups at 107months each versus 54 months in the PD-L1 positive group of either category.

Conclusions: PD-L1 expression can predict event-free survival in DLBCL cases and therefore poor prognosis.