LC-MS-based serum metabolomics analysis for the screening and monitoring of colorectal cancer.

Background: Colorectal Cancer (CRC) is a prevalent digestive system tumour with significant mortality and recurrence rates. Serum metabolomics, with its high sensitivity and high throughput, has shown potential as a tool to discover biomarkers for clinical screening and monitoring of the CRC patients. Methods: Serum metabolites of 61 sex and age-matched healthy controls and 62 CRC patients (before and after surgical intervention) were analyzed using a ultra-performance liquid chromatography-high resolution mass spectrometer (UPLC-MS). Statistical methods and pathway enrichment analysis were used to identify potential biomarkers and altered metabolic pathways. Results: Our analysis revealed a clear distinction in the serum metabolic profile between CRC patients and healthy controls (HCs). Pathway analysis indicated a significant association with arginine biosynthesis, pyrimidine metabolism, pantothenate, and CoA biosynthesis. Univariate and multivariate statistical analysis showed that 9 metabolites had significant diagnostic value for CRC, among them, Guanosine with Area Under the Curve (AUC) values of 0.951 for the training group and0.998 for the validation group. Furthermore, analysis of four specific metabolites (N-Phenylacetylasparticacid, Tyrosyl-Gamma-glutamate, Tyr-Ser and Sphingosine) in serum samples of CRC patients before and after surgery indicated a return to healthy levels after an intervention. Conclusion: Our results suggest that serum metabolomics may be a valuable tool for the screening and monitoring of CRC patients.

Development and validation of a nomogram for predicting pelvic lymph node metastasis and prognosis in patients with cervical cancer.

Objective: Cervical cancer (CC) is one of the main causes of death among gynecological malignancies. Patients with CC with lymph node metastasis (LNM) have poor prognoses. We investigated the risk factors and prognosis of LNM in patients with CC patients using data from the SEER database. Methods: We collected the information of cervical cancer patients registered in SEER database from 2010 to 2015. The dataset was divided into a training set and a validation set at a 7:3 ratio. LASSO regression analysis was used to evaluate risk factors for LNM in patients with CC. Using the results, we established a nomogram prediction model. C-index, ROC curves, calibration curves, decision curve analysis, and clinical impact curves were used to evaluate the prediction performance of the model. Results: We included 14,356 patients with CC in the analysis. Among these, 3997 patients were diagnosed with LNM. A training set (10,050 cases) and a validation set (4306 cases) were used for the following analysis. We established nomogram LNM prediction models for the patients with T1-2-stage CC. The C-indices for the internal and external validations of the prediction models were 0.758 and 0.744, respectively. In addition, we established a prognostic nomogram for all CC patients with LNM, and the internal and external validation C-indices were 0.763 and 0.737. Conclusion: We constructed a quantitative and visual predictive nomogram that predicted prognosis of patients with LNM in CC to provide clinicians with a reference for diagnosis and treatment.

The prognostic value of circRNAs for gastric cancer: A systematic review and meta-analysis.

Gastric cancer is the third leading cause of cancer-related deaths worldwide. Novel biomarkers circRNAs can play an important role in the development of gastric cancer as oncogenes or tumor suppressor genes. The purpose of this study was to clarify the relationship between the abnormal expression of multiple circRNAs and their prognostic value in gastric cancer patients through a meta-analysis. We researched articles reporting the relationship between circRNAs and the prognosis of gastric cancer published in PubMed, Cochrane, Embase, Web of Science, Wanfang, CNKI, and VIP databases before 31 December 2019. Thirty-five articles were selected for the meta-analysis, involving 3135 gastric cancer patients. The total HR values (95% CI) of OS and DFS related to highly expressed circRNAs that indicated worse prognosis were 1.83 (1.64-2.03; p < 0.001) and 1.66 (1.33-2.07; p < 0.001), respectively. The total HR (95% CI) of OS and DFS related to highly expressed circRNAs that indicated better prognosis was 0.54 (0.45-0.66; p < 0.001) and 0.58 (0.43-0.78; p < 0.001), respectively. Two panels of five circRNAs predicted a more considerable HR value (circ_0009910, hsa_circ_0000467, hsa_circ_0065149, hsa_circ_0081143, and circDLST; and circSMARCA5, circLMTK2, hsa_circ_0001017, hsa_circ_0061276, and circ-KIAA1244). The results of the meta-analysis were 2.63 (2.08-3.33; p < 0.001) and 0.39 (0.27-0.59; p < 0.001) for OS, respectively. The two panels of dysregulated circRNAs can be considered as more suitable potential prognostic tumor biomarkers in patients with gastric cancer because of their larger HR values.

Risk factors and prognosis of bone metastases in newly diagnosed gastric cancer.

Aim: To predict the occurrence of bone metastases and prognosis among patients with gastric cancer on a population level. Materials & methods: Data were obtained from the SEER database (2010-2016). Multivariable logistic regression and multivariable Cox regression were used to determine factors that predict the occurrence of bone metastasis and prognosis. Results: Cardia cancer, younger age, white race, poor differentiation grade, higher N stage, diffuse-type were positively associated with the presence of bone metastasis. For gastric cancer patients with bone metastasis, the median survival time was longer (9.0 months) among patients with surgery of primary site compared with those without surgery (3.0 months). Conclusion: According to the results of risk assessment, clinical efforts should be targeted to focus on screening high-risk patients.