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Background: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors.Methods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated.Results: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately.Conclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic.

Background Compared with colon cancer, the increase of morbidity is more significant for rectal cancer. The current study set out to identify novel and critical biomarkers or features that may be used as promising targets for early diagnosis and treatment monitoring of rectal cancer. Methods Microarray datasets of rectal cancer with a minimum sample size of 30 and RNA-sequencing datasets of rectal adenocarcinoma (READ) were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The method of robust rank aggregation was utilized to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was structured using the STRING platform, and hub genes were identified using the Cytoscape plugin cytoHubba and an UpSet diagram. R software was employed to perform functional enrichment analysis. Receiver operating characteristic (ROC) curves based on the GEO data and Kaplan-Meier curves based on the TCGA data were drawn to assess the diagnostic and prognostic values of the hub genes. Immune cell infiltration analysis was conducted with CIBERSORT, and the diagnostic value and correlations between prognostic genes and infiltrated immune cells were analyzed by principal component analysis (PCA), ROC curves, and correlation scatter plots. Results A total of 137 robust DEGs were obtained by integrating datasets in GEO. Twenty-four hub genes, including CHGA, TTR, SAA1, SPP1, MMP1, TGFBI, COL1A1, and PCK1, were identified as a diagnostic gene biomarker group for rectal cancer, and SAA1, SPP1, and SI were identified as potential novel prognostic biomarkers. Functionally, the hub genes were mainly involved in the rectal cancer related interleukin (IL)-17 and proximal tubule bicarbonate reclamation pathways. Twelve sensitive infiltrated immune cells were identified, and were correlated with prognostic genes. Conclusions The integrated gene biomarker group combined with immune cell infiltration can effectively indicate rectal cancer.

Adverse pregnancy outcomes (APOs) have been a devastating actuality in clinic. However, the pre-onset risk factors, that correlated with pregnancy failure, including antiphospholipid antibodies (APLs) and angiogenic factors, remain unclear. A retrospective study was performed in this research, and data from 145 pregnant women were collected during their pregnancy. Patients were finally divided into non-APO group (n = 89) and APO group (n = 56) according to their pregnancy outcomes. The associations among their characteristics, laboratory tests, therapies, and outcomes were analyzed. Univariate analysis demonstrated that patients with APOs showed significant prevalence of lupus anticoagulant (LAC) positive (P

Background Serum samples of patients with hemorrhagic cerebral infarction (HCI), cerebral infarction (CI), and healthy controls (HCs) were used to screen statistically different protein peaks as potential biomarkers and to establish a decision tree classification model. Methods The serum samples from clinically confirmed patients with HCI and CI from November 2018 to October 2019 were collected, along with those of HCs who visited our hospital during the same period. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with CM10 ProteinChip was used to analyze the differences in serum protein expression profiles of 30 patients with HCI, 32 patients with CI, and 31 HCs in the training group, and a decision tree classification model was established. At the same time, the blind test group (18 patients with HCI, 21 patients with CI, and 17 HCs) was tested by a blind method. Results Model 1 was successfully established by software analysis with a mass-to-charge ratio of 3,495.2, 8,941.0, and 15,890.4 as a differential protein peak. The sensitivity, specificity, and accuracy of model 1 in distinguishing HCI from HCs were 86.8%, 87.1%, and 86.9%, respectively. After verification of model 1 by the blind test group, the results showed that the sensitivity, specificity, and accuracy were 88.9%, 94.1%, and 91.4%, respectively. The sensitivity, specificity, and accuracy of model 2 with a mass-to-charge ratio of 2,941.3 as a differential protein peak were 86.7%, 75.0%, and 80.6%, respectively. After verification of model 2 by the blind test group, the results showed that the sensitivity, specificity, and accuracy were 83.3%, 90.4%, and 87.2%, respectively. Conclusions Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and CM10 ProteinChip can be used to screen serum protein markers in patients with HCI. Mass-to-charge ratio of 3,495.2, 8,941.0, 15,890.4, and 2,941.3 may be potential protein biomarkers of HCI and used to distinguish HCI patients from HCs and CI.

Recent studies have revealed that artesunate (ART) has clear anti-tumor activity, suggesting that it could be a good candidate chemotherapeutic agent. In this study, we researched the inhibitory effect of ART on MCF7 cells and explored the possible mechanisms. MTT assay was used to detect the effect of ART on the proliferation of MCF7 cells. Crystal violet staining was used to observe morphological and quantitative changes. Flow cytometry was used to detect the cell cycle of the drug-acting MCF7 cells. In addition, western blotting was used to detect the drug influence on expression of the ATM, phospho-ATM(S1981), H2AX, γH2AX(S139), CHK2 and phospho-CHK2(T68), cdc25C, and phospho-cdc25C(S216). In the experimental groups, the proliferation of MCF7 cells was inhibited in a dose-dependent manner and the original cell morphology was lost. The number of G2/M phase cells in the experimental groups increased significantly, and the expression of DNA damage response-associated proteins was significantly increased, such as phospho-ATM(S1981), γH2AX(S139), phospho-CHK2(T68), and phospho-cdc25C(S216). ART can inhibit cell proliferation and promote G2/M arrest in MCF7 cells through ATM activation and the ensuing “ATM-Chk2-Cdc25C” pathway, thus implicating ART as a novel candidate for breast cancer chemotherapy.

Objective: To explore the role of platelet-derived growth factor-B (PDGF-B)/platelet-derived growth factor receptor-β (PDGFR-β) signaling in chronic atrial fibrillation (AF). Methods: Thirty-nine AF patients and 33 patients with sinus rhythm (SR) were enrolled. Twenty canines were randomized into 5 groups: control, sham and AF lasting 1, 2 or 4 weeks. The AF canine models were made by rapid atrial pacing. Rat atrial fibroblasts were treated with PDGF-BB or PDGF-BB + PDGFR inhibitor AG1295, respectively. Gene expression in the right atrial appendage of patients, the left atrium of canines and rat atrial fibroblasts was measured by quantitative real-time PCR and Western blot, respectively. The degree of atrial fibrosis was evaluated by Masson trichrome staining. Results: The degree of atrial fibrosis and the expression of PDGF-B, PDGFR-β and collagen type I (COL1) in AF patients significantly increased compared to patients with SR. The degree of atrial fibrosis and the expression of PDGF-B and COL1 in canines increased progressively with the increased duration of AF. The expression of PDGFR-β increased progressively 2 weeks after AF. PDGF-BB promoted the proliferation and COL1 secretion of rat atrial fibroblasts. AG1295 attenuated these effects. Conclusions: Our study suggests that PDGF-B/PDGFR-β signaling, which promotes the proliferation and COL1 secretion of atrial fibroblasts, is an important contributor to atrial fibrosis in AF and may represent a novel target for the intervention of AF.