International Journal of Biological Markers最新文献

Introduction: Diagnosis of cholangiocarcinoma (CCA) can be challenging due to unclear imaging criteria and difficulty obtaining adequate tissue biopsy. Although serum cancer antigen 19-9 and carcinoembryonic antigen have been proposed as potential diagnostic aids, their use remains limited by insufficient sensitivity and specificity. This exploratory analysis aimed to identify individual- and combinations of serum biomarkers to distinguish CCA from hepatocellular carcinoma (HCC) and chronic liver disease (CLD) controls using samples from a published study.

Methods: This prospective, multicenter, case-control study included patients aged ≥18 years at high-risk of HCC. Serum and ethylene diamine tetraacetic acid-plasma samples were collected prior to any treatment and confirmed diagnosis of HCC or CCA. Fourteen biomarkers (measured by electrochemiluminescence immunoassays or enzyme-linked immunosorbent assays) were subjected to univariate analysis and 13 included in a multivariate analysis (per selected combinations and exhaustive search).

Results: Overall, 55 CCA, 306 HCC, and 733 CLD control samples were analyzed. For distinguishing CCA from HCC, alpha-fetoprotein and matrix metalloproteinase-2 (MMP-2) showed the best individual performance (area under the curve (AUC) 86.6% and 84.4%, respectively); tissue inhibitor of metalloproteinase-1 (TIMP-1) was most able to distinguish CCA from CLD (AUC 94.5%) and from HCC  +  CLD (AUC 88.6%). The combination of MMP-2 and TIMP-1 was the best-performing two-marker panel, with AUC >90% for all comparisons.

Conclusion: MMP-2 and TIMP-1 are promising biomarkers that could support differential diagnosis of CCA. Incorporating these assays into the diagnostic algorithm could provide additional diagnostic information in a non-invasive, rapid manner, and could supplement existing diagnostic methods.

Background: This study aimed to establish a nomogram to distinguish advanced- and early-stage lung cancer based on coagulation-related biomarkers and liver-related biomarkers.

Methods: A total of 306 patients with lung cancer and 172 patients with benign pulmonary disease were enrolled. Subgroup analyses based on histologic type, clinical stage, and neoplasm metastasis status were carried out and multivariable logistic regression analysis was applied. Furthermore, a nomogram model was developed and validated with bootstrap resampling.

Results: The concentrations of complement C1q, fibrinogen, and D-dimers, fibronectin, inorganic phosphate, and prealbumin were significantly changed in lung cancer patients compared to benign pulmonary disease patients. Multiple regression analysis based on subgroup analysis of clinical stage showed that compared with early-stage lung cancer, female (P < 0.001), asymptomatic admission (P = 0.001), and total bile acids (P = 0.011) were negatively related to advanced lung cancer, while C1q (P = 0.038), fibrinogen (P < 0.001), and D-dimers (P = 0.001) were positively related. A nomogram model based on gender, symptom, and the levels of total bile acids, C1q, fibrinogen, and D-dimers was constructed for distinguishing advanced lung cancer and early-stage lung cancer, with an area under the receiver operating characteristic curve of 0.919. The calibration curve for this nomogram revealed good predictive accuracy (P_{-Hosmer-Lemeshow} = 0.697) between the predicted probability and the actual probability.

Conclusions: We developed a nomogram based on gender, symptom, and the levels of fibrinogen, D-dimers, total bile acids, and C1q that can individually distinguish early- and advanced-stage lung cancer.

Background: The correlation of the expression of ankyrin repeat domain (ANKRD) family members with renal cell carcinoma prognosis was investigated.

Methods: The GEPIA2, GEO2R, UALCAN, GDC, OncoLnc, TIMER, PanglaoDB, CancerSEA, and Tabula Muris databases were used. Twelve ANKRD family members were identified as having overexpressed renal cell carcinoma samples. The ANKRD13D was identified as a renal cell carcinoma-specific target by cross-referencing the multiple survival databases. To clarify the role of ANKRD13D, the expression of NAKRD13D was analyzed at the single-cell level.

Results: ANKRD13D was mainly expressed in immune cells and positively correlated with Treg cell infiltration. The expression of ANKRD13D was also positively correlated with PDCD1, CTLA4, LAG3, TNFSF14, and ISG20. The overexpression of ANKRD13D in Treg was confirmed using reverse transcription-quantitative polymerase chain reaction. The structure of ANKRD13D was predicted using AlphaFold.

Conclusion: In conclusion, we identified ANKRD13D as a key immune regulator, and targeting ANKRD13D with immune checkpoints blockade may be a promoting strategy for renal cell carcinoma immunotherapy.

Objective: Radiation-induced trismus (RIT), one of the rare but serious side effects of concurrent chemoradiotherapy (C-CRT), is difficult to predict with high accuracy. We aimed to examine whether the pretreatment pan-immune-inflammation value (PIV) measures predict RIT in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) receiving C-CRT.

Methods: Data of patients with LA-NPC who underwent C-CRT and had maximum mouth openings (MMO) > 35 mm were reviewed. Any MMO of 35 mm or less after C-CRT was considered RIT. All PIV values were computed using the complete blood count test results: PIV = (Platelets × Monocytes × Neutrophils) ÷ Lymphocytes. The receiver operating characteristic analysis was employed to dissect a possible association between pre-treatment PIV readings and RIT status. Confounding variables were tested for their independent relationship with the RIT rates using logistic regression analysis.

Results: The research comprised 223 participants, and RIT was diagnosed in 46 (20.6%) at a median time from C-CRT to RIT of 10 months (range: 5-18 months). Pre-C-CRT PIV levels and RIT rates were analyzed using receiver operating characteristic curve analysis, with 830 being the optimal cutoff (area under the curve: 92.1%; sensitivity: 87.5%; specificity: 85.5%; Youden index: 0.730). RIT was significantly more prevalent in the PIV > 830 cohort than its PIV ≤ 830 counterpart (60.3% vs. 5%; hazard ratio 5.79; P < 0.001). Multivariate logistic regression analysis revealed that advanced T-stage (P = 0.004), masticatory apparatus dose V58Gy≥%32 (P = 0.003), and PIV > 830 (P < 0.001) were independently linked with significantly elevated rates of RIT.

Conclusion: The presence of elevated pre-C-CRT PIV is a unique biological marker that independently predicts increased RIT rates in LA-NPC undergoing C-CRT.

Background: Hepatocellular carcinoma carries a poor prognosis and poses a serious threat to global health. Currently, there are few potential prognostic biomarkers available for the prognosis of hepatocellular carcinoma.

Methods: This pilot study used 4D label-free quantitative proteomics to compare the proteomes of hepatocellular carcinoma and adjacent non-tumor tissue. A total of 66,075 peptides, 6363 identified proteins, and 772 differentially expressed proteins were identified in specimens from three hepatocellular carcinoma patients. Through functional enrichment analysis of differentially expressed proteins by Gene Ontology, KEGG pathway, and protein domain, we identified proteins with similar functions.

Results: Twelve differentially expressed proteins (RPL17, RPL27, RPL27A, RPS5, RPS16, RSL1D1, DDX18, RRP12, TARS2, YARS2, MARS2, and NARS1) were selected for identification and validation by parallel reaction monitoring. Subsequent Western blotting confirmed overexpression of RPL27, RPS16, and TARS2 in hepatocellular carcinoma compared to non-tumor tissue in 16 pairs of clinical samples. Analysis of The Cancer Genome Atlas datasets associated the increased expression of these proteins with poor prognosis. Tissue microarray revealed a negative association between high expression of RPL27 and TARS2 and the prognosis of hepatocellular carcinoma patients, although RPS16 was not significant.

Conclusions: These data suggest that RPL27 and TARS2 play an important role in hepatocellular carcinoma progression and may be potential prognostic biomarkers of overall survival in hepatocellular carcinoma patients.

Background: Globally, prostate cancer is the second most common malignancy in males. Serum microRNAs (miRNAs) may function as non-invasive and innovative biomarkers for various cancers. Our study aimed to determine potential miRNAs for prostate cancer screening.

Methods: A three-stage study was accomplished to ascertain crucial miRNAs as markers. In the screening stage, we searched PubMed for aberrantly expressed miRNAs relevant to prostate cancer and selected them as candidate miRNAs. In training and validation stages, with serum specimens from 112 prostate cancer patients and 112 healthy controls, expressions of candidate miRNAs were identified through quantitative reverse transcription-polymerase chain reaction. The diagnostic capabilities of miRNAs were determined by receiver operating characteristic curves. Bioinformatic analysis was utilized to explore the function of the critical miRNAs.

Results: Expression of six serum miRNAs (miR-34b-3p, miR-556-5p, miR-200c-3p, miR-361-5p, miR-369-3p, miR-485-3p) were significantly altered in prostate cancer patients contrasted with healthy controls. The optimal combination of critical miRNAs is a three-miRNA panel (miR-34b-3p, miR-200c-3p, and miR-361-5p) with good diagnostic capability. FLRT2, KIAA1755, LDB3, and NTRK3 were identified as the potential genes targeted by the three-miRNA panel.

Conclusions: The three-miRNA panel may perform as an innovative and promising serum marker for prostate cancer screening.

Objectives: Development and validation of a predictive model including serum vitamin concentration to estimate the risk of chemotherapy-induced grade 3/4 neutropenia in esophageal cancer, gastric cancer, or colorectal cancer patients who receive the first cycle of chemotherapy.

Methods: Data from 535 patients treated at the Affiliated Fuyang People's Hospital of Anhui Medical University from January 1, 2020, to March 2, 2022, were used to derive the predictive model. Least absolute shrinkage and selection operator regression analysis was performed to screen potential risk characteristics, and multivariate logistic regression was utilized to investigate efficient factors associated with chemotherapy-induced neutropenia. A nomogram was constructed using this logistic model. This nomogram was then tested on a temporal validation cohort containing 212 consecutive patients.

Results: In the cohort of all 747 eligible patients, grade 3/4 neutropenia incidence was 45.2%. Age, Eastern Cooperative Oncology Group-performance status, neutrophil count, serum albumin, and hemoglobin data were entered into the final model. The performance of the final predictive nomogram was assessed by the area under the receiver operating characteristic curve in both the development and validation datasets. The calibration curves indicated that the estimated risks were accurate. Decision curve analysis for the predictive model exhibited improved clinical practicality.

Conclusion: In the present study, we established an accessible risk predictive model and identified valuable serum vitamin concentration parameters associated with chemotherapy-induced neutropenia. The predictive model may improve the grade 3/4 neutropenia risk prediction in patients with gastrointestinal malignancies who receive oxaliplatin- and fluoropyrimidine-based chemotherapy and help physicians make appropriate decisions for disease management.

Background: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used.

Methods: In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance.

Results: A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900.

Conclusions: Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

Background: Colon cancer (CC) is one of the most common malignancies worldwide. Characterization of new prognostic biomarkers for right-sided CC (RCC) and left-sided CC (LCC) may contribute to improving early detection. An association of human leukocyte antigens class II (HLA-II) with the predisposition to CC was suggested.

Aim of the study: We evaluated the association of DRB1 and DQB1 with the risk of LCC and RCC.

Patients and methods: Our study comprised 93 CC patients and 100 healthy controls. Genotyping of HLA class II alleles were performed by the Polymerase Chain Reaction Sequence-Specific Primers (PCR-SSP).

Results: DRB1*03 was positively associated with CC. In contrast, DRB1*11, DRB1*13, DQB1*03, and DQB1*05 were negatively linked to CC. Haplotype analysis revealed that DRB1*04-DQB1*04 and DRB1*09-DQB1*02 were positive, while DRB1*01-DQB1*05, DRB1*04-DQB1*03, DRB1*07-DQB1*02, DRB1*11-DQB1*03, DRB1*11-DQB1*05, and DRB1*13-DQB1*06 were negatively associated with CC. For sigmoid CC, DRB1*13, DRB1*11, and DQB1*05 were negative, while DRB1*04-DQB1*02, and DRB1*07-DQB1*03 were positively associated. DRB1*03 and DRB1*04-DQB1*04 were positive, while DRB1*11 and DQB1*03 were negatively linked to RCC. According to the LCC, DRB1*07, DRB1*11, DQB1*03, DQB1*05, and DRB1*07-DQB1*02 were negative. In contrast, DRB1*09-DQB1*02 was positively associated with LCC. Stratified analysis revealed that DRB1*11 is associated with higher risk of metastasis in CC and sigmoid CC, and tolerance to treatment in RCC. DQB1*03 was associated with lymph-node invasion in CC.

Conclusion: DRB1 and DQB1 polymorphisms could be used as future biomarkers for the early detection of subjects at a higher risk of developing RCC and LCC, metastasis in sigmoid CC, and tolerance to treatment in RCC.

Background: Gastric cancer is a common cancer developed in a carcinogenesis process from precancerous lesions including chronic gastritis, intestinal metaplasia, and dysplasia. Survivin, an inhibitor-of-apoptosis protein, is associated with the initiation and progression of gastric cancer. The present study aimed to evaluate the immunohistochemical expression patterns of survivin and its relationship with early diagnosis of gastric cancer in Iranian patients.

Methods: In this retrospective case-control study, immunoexpression of survivin was investigated on sections obtained from formalin-fixed paraffin-embedded tissue blocks of 38 chronic gastritis, 32 intestinal metaplasia, 20 dysplasia, 28 gastric adenocarcinoma, and 22 controls.

Results: Survivin immunoexpression in chronic gastritis was higher than controls, but this difference was not statistically significant (P > 0.05). However, survivin immunoexpression had a steady significant increase from control and chronic gastritis to intestinal metaplasia to dysplasia to gastric adenocarcinoma (P < 0.05). Sensitivity, specificity, and area under the curve of survivin immunohistochemical test for the diagnosis of gastric cancer were 87.5%, 74.4%, and 0.85, respectively. Males had a significantly higher survivin expression than females (P < 0.001). Also, survivin expression was significantly higher in older patients than in younger ones (P < 0.001).

Conclusion: It seems that the steady increase in survivin expression from different precancerous lesions to gastric adenocarcinoma suggests that survivin can be used as a potential biomarker for the prevention and early diagnosis of gastric cancer.