Effectiveness of D-dimer in predicting distant metastasis in colorectal cancer.

PURPOSE: Patients with cancer often present with a hypercoagulable state, which is closely associated with tumor progression. The purpose of this study was to assess the diagnostic efficacy of D-dimer in predicting distant metastasis in colorectal cancer (CRC). METHODS: This study included 529 patients diagnosed with CRC at our hospital between January 2020 and December 2022. Plasma coagulation indicators and tumor markers were collected prior to treatment and their diagnostic efficacy for predicting CRC metastasis was assessed by receiver operating characteristic (ROC) curves. Independent risk factors for evaluating tumor metastasis were obtained by multivariate logistic regression analysis. RESULTS: The level of D-dimer in the metastatic group was significantly higher than that in the non-metastatic group (P<0.001). The results of the multiple logistic regression analysis indicated that lower level of prealbumin and platelet, and higher level of glucose, CEA and D-dimer were independent risk factors for distant metastasis in patients with CRC (P<0.05, respectively). The combination of prealbumin, glucose, D-dimer, platelet and tumor markers (PRE2) was found to be significantly more effective in predicting metastasis of CRC when compared to the combination of tumor marker alone (PRE1, P<0.001). CONCLUSION: Plasma D-dimer may be a novel tumor marker for screening metastases of CRC.

Circulating tumor cells are a good predictor of tumor recurrence in clinical patients with gastric cancer.

Circulating tumor cells (CTCs) as a liquid biopsy have great potential in clinical applications and basic cancer research, but their clinical use in gastric cancer remains unclear. This study investigated whether CTCs could be used as a potential prognosis predictor in patients with gastric cancer. A total of 120 patients with pathologically confirmed gastric cancer were enrolled from January 1, 2015, to December 1, 2019. All patients were initially diagnosed without previous treatment, and then the number of CTCs was detected using the NEimFISH method before radical surgical resection. Regular follow-up was performed in all patients, and the correlations between the number of CTCs and clinical endpoints, such as disease-free survival (DFS) and overall survival (OS), were evaluated. The univariate and multivariate hazard ratios were calculated using the Cox proportional hazard model. Based on the number of CTCs, we defined CTCs ≥ 2 per 7.5 mL of whole blood as the positive group and CTCs < 2 as the negative group. Among the 120 patients who underwent CTC detection before surgery, the rate of CTC-positive patients was 64.17% (77/120) of which stage I and II patients accounted for 22.50% and stage III patients accounted for 41.67% (P = 0.014). By detecting CTCs before surgery and at the time of recurrence, the number of CTCs tends to increase concomitantly with disease progression (median: 2 VS 5 per 7.5 mL). Multivariate analysis showed that age (HR, 0.259; 95% CI, 0.101-0.662; P = 0.005), D-dimer (HR, 3.146; 95% CI, 1.169-8.461; P = 0.023), and lymph node metastasis (HR, 0.207; 95% CI, 0.0071-0.603; P = 0.004) were factors correlated with CTCs. In addition, the median follow-up of all the patients was 38.0 months (range of 28-80 months); the DFS in CTC-positive patients was significantly shorter than that of the CTC-negative patients, and a significant difference was found based on the Cox proportional hazard regression model analysis (44.52 ± 2.83 m vs. 74.99 ± 2.78 m, HR = 4.550, P = 0.018). The OS was shorter in the CTC-positive group than in the CTC-negative group before the operation, but the result was not significant based on the Cox proportional hazard regression model analysis (47.58 ± 2.46 m vs. 70.68 ± 3.53 m, HR = 2.261, P = 0.083). The number of CTCs tends to increase concomitantly with disease progression. In addition, the detection of CTCs was an independent predictor of shorter DFS in gastric cancer. However, the relationship between CTCs and OS needs to be determined in future studies.

pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory.

Predicting DNase I hypersensitive sites (DHSs) is an essential topic in the field of transcriptional regulatory elements, which provides clues for deciphering the function of noncoding genomic regions. To the best of our knowledge, several computational approaches are currently available for prediction of DHSs in the plant genome, but there is still room for improvement. In the present work, a DS evidence theory-based method was proposed. At first, four sequence-derived feature representation methods, i.e., kmer, reverse complement kmers, mismatch profile, and pseudo dinucleotide composition, were utilized to encode the sequences. Then, four support vector machine based sub-classifiers was built with these sequence-derived features. Finally, the DS evidence theory was applied to obtain the final results by fusing the outputs of these four base learners. In this work, to solve the data imbalance problem, a bidirectional synthetic sampling algorithm was proposed to obtain balanced dataset during training the models. In the computational experiments, the proposed method achieved accuracy up to 88.85%, and 88.60% in Arabidopsis thaliana and rice genome, respectively. Compared with existing DHSs prediction methods, the proposed method can achieve comparable or better performances, suggesting the usefulness of the method for DHSs prediction.