ABSTRACT
Acute respiratory distress syndrome(ARDS)is one of the most common complications of sepsis, resulting in the high risk of death in patients with sepsis.By comparison with non-septic ARDS, sepsis-associated ARDS is characterized by high morbidity, heterogeneity and mortality.It is vital to early identify the occurrence of ARDS, accurately assess the severity, as well as effectively implement the individualized treatment.Based on the genome-wide association study, mass cytometry, and multiple omics data analysis, the molecular signatures of sepsis-associated ARDS have been elucidated, which were related to genetic susceptibility, inflammatory reaction pathway, and metabolic characteristics.The development of novel biomarkers is helpful to molecular classifier, risk stratification, early recognition and assessing severity, implement early intervention, then improving the prognosis.
ABSTRACT
Metastasis is the leading cause of human cancer deaths. Unfortunately, no approved drugs are available for anti-metastatic treatment. In our study, high-throughput sequencing-based high-throughput screening (HTS) and a breast cancer lung metastasis (BCLM)-associated gene signature were combined to discover anti-metastatic drugs. After screening of thousands of compounds, we identified Ponatinib as a BCLM inhibitor. Ponatinib significantly inhibited the migration and mammosphere formation of breast cancer cells in vitro and blocked BCLM in multiple mouse models. Mechanistically, Ponatinib represses the expression of BCLM-associated genes mainly through the ERK/c-Jun signaling pathway by inhibiting the transcription of JUN and accelerating the degradation of c-Jun protein. Notably, JUN expression levels were positively correlated with BCLM-associated gene expression and lung metastases in breast cancer patients. Collectively, we established a novel approach for the discovery of anti-metastatic drugs, identified Ponatinib as a new drug to inhibit BCLM and revealed c-Jun as a crucial factor and potential drug target for BCLM. Our study may facilitate the therapeutic treatment of BCLM as well as other metastases.
ABSTRACT
PURPOSE: We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients. MATERIALS AND METHODS: We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation, we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database. RESULTS: We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival. CONCLUSION: Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.
Subject(s)
Humans , Disease-Free Survival , DNA , Forests , Gene Expression , Genome , Genome, Human , Logistic Models , Lymphatic Metastasis , Ovarian Neoplasms , RNA, Messenger , ROC Curve , Support Vector Machine , TranscriptomeABSTRACT
Objective: To conduct a integrated analysis of gene expression signature and gene expression profile, so as to provide reference for the prognosis and drug-resistance of serrated colorectal adenocarcinoma (SCA). Methods: We downloaded four gene expression datasets (GSE14333, GSE17538, GSE33113, and GSE37892) of colorectal carcinoma with the follow-up survival data from GEO database, and then integrated them into a entire expression profile (n=600) with batch adjustment and gene expression value extraction. An SCA gene signature and the corresponding expression profile were integrated with the previous expression dataset. Using the gene signature score model we assigned score to each patient in the gene expression dataset and classified the patients into serrated, transitional, or conventional subtypes. Kaplan-Meier analysis and Cox model were used to compare the risks of cancer recurrence between three subtypes of colorectal carcinoma. Gene signature model were also used to generate the score for each patient in the datasets associated with alleviative therapy of colorectal cancer (GSE28702, GSE5851). The SCA drug-resistance was analyzed by observing the therapeutic effective group and non-effective group. Results: According to cut-off value of CRC subtypes, 600 patients in the combined dataset were classified as 50 with serrated subtype, 126 with transitional subtype, and 424 with conventional subtype. Survival analysis showed the serrated and transitional subtypes had very similar scores to predict patient survival, and they were also independent risk factors for postoperative recurrence. When comparing serrated and conventional subtypes, multivariate Cox model analysis indicated the patients with serrated subtype was an unfavorable independent risk factor for prognosis (AHR=1.792, 95%CI 1.011-3.177). Responders to cetuximab treatment had significantly higher signature scores than non-responders (P=0.017), while responders to FOLFOX treatment had similar signature scores with the non-responders. Conclusion: Serrated subtype is an independent risk factor of postoperative recurrence in SCA patients, and is related to the treatment with cetuximab.