International Journal of Biological Markers最新文献

Background: The acid glycoprotein 1 (AGP1) is downregulated in lung cancer. However, the performance of AGP1 in distinguishing benign from malignant lung lesions is still unknown.

Methods: The expression of AGP1 in benign diseases and lung cancer samples was detected by Western blot. The receiver operating characteristic curves, bivariate correlation, and multivariate analysis was analyzed by SPSS software.

Results: AGP1 expression levels were significantly downregulated in lung cancer and correlated with carcinoembryonic antigen (CEA), CA199, and CA724 tumor biomarkers. The diagnostic performance of AGP1 for distinguishing malignant from benign pulmonary lesions was better than the other four clinical biomarkers including CEA, squamous cell carcinoma-associated antigen, neuron-specific enolase, and cytokeratin 19 fragment 21-1, with an area under the curve value of 0.713 at 88.8% sensitivity. Furthermore, the multivariate analysis indicated that the variates of thrombin time and potassium significantly affected the AGP1 levels in lung cancer.

Conclusions: Our study indicates that AGP1 expression is decreased in lung cancer compared to benign samples, which helps distinguish benign and malignant pulmonary lesions.

The expression of programmed cell death-ligand 1 (PD-L1) on circulating tumor cells offers a noninvasive method for the detection of PD-L1 expression in lung cancer, and could serve as a potential surrogate for cancer tissue. However, discrepant results make it difficult to apply PD-L1 on circulating tumor cells to clinical practice. Therefore, we conducted a meta-analysis to investigate the diagnostic value of PD-L1 on circulating tumor cells in lung cancer. To identify the relationship between the expression of PD-L1 on circulating tumor cells and lung cancer, the PubMed, Web of Science, Embase, China National Knowledge Infrastructure, and Wanfang databases were searched from inception to March 2023. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the corresponding 95% confidence intervals were calculated to assess the diagnostic performance of PD-L1. We also conducted subgroup and sensitivity analyses. A total of 11 studies including 472 lung cancer patients were included in our study. The overall performance in terms of pooled sensitivity and specificity was 0.72 (0.52-0.86) and 0.54 (0.25-0.81), respectively. The positive likelihood ratio, negative likelihood ratio, and area under the curve were 1.57 (0.87-2.84), 0.52 (0.30-0.90), and 0.70 (0.66-0.74), respectively. Deeks' funnel plot test indicated no publication bias. Our analysis demonstrated that positive PD-L1 expression on circulating tumor cells (CTCs) exhibited a moderate diagnostic value in lung cancer, and CTCs may serve as a feasible alternative tissue analysis for the detection of PD-L1 in lung cancer.

Introduction: Complement C5a is an important component of the innate immune system. An increasing number of reports have revealed the relevance of C5a in tumor progression; however, its exact role in metastatic renal cell carcinoma (mRCC) remains unknown.

Methods: We evaluated C5a expression in tumor tissue microarrays of 231 mRCC patients and analyzed the relationship between C5a levels and clinical outcomes, and the expression of epithelial-mesenchymal transition (EMT)-related proteins, programmed cell death protein 1 (PD-1), and programmed cell death-ligand 1 (PD-L1). In-vitro functional experiments using exogenous C5a stimulation and C5a silencing in renal cell carcinoma cells were used to validate the tissue findings.

Results: High C5a expression was associated with poor therapeutic responses, poor overall and progression-free survival, and high expression of EMT-related proteins and PD-1/PD-L1 in mRCC patients. Exogenous C5a promoted proliferation, migration, and invasion of renal cell carcinoma cells, and induced the expression of EMT-related proteins and PD-1/PD-L1. Conversely, C5a silencing inhibited migration and invasion of renal cell carcinoma cells and decreased the expression of EMT-related proteins and PD-1/PD-L1.

Conclusions: Our findings indicate that elevated C5a expression is associated with poor outcomes in patients with mRCC, and this effect may be partly attributed to the ability of C5a to promote EMT and PD-1/PD-L1 expression. C5a may be a potential novel target for the treatment of mRCC.

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.