Evaluation of Molecular Residual Disease by a Fixed Panel in Resectable Colorectal Cancer.

Purpose: Molecular residual disease (MRD) is a promising biomarker in colorectal cancer (CRC) for prognosis and guiding treatment, while the whole-exome sequencing (WES) based tumor-informed assay is standard for evaluating MRD based on circulating tumor DNA (ctDNA). In this study, we assessed the feasibility of a fixed-panel for evaluating MRD in CRC. Materials and Methods: 75 patients with resectable stage I-III CRC were enrolled. Tumor tissues obtained by surgery, and pre-operative and post-operative day 7 blood samples were collected. The ctDNA was evaluated using the tumor-agnostic and tumor-informed fixed assays, as well as the WES-based and panel-based personalized assays in randomly selected patients. Results: The tumor-informed fixed assay had a higher pre-operative positive rate than the tumor-agnostic assay (73.3% vs 57.3%). The pre-op ctDNA status failed to predict disease-free survival (DFS) in either of the fixed assays, while the tumor-informed fixed assay-determined post-op ctDNA positivity was significantly associated with worse DFS (HR, 20.74, 95%CI 7.19-59.83; p<0.001), which was an independent predictor by multivariable analysis (HR, 28.57, 95%CI 7.10-114.9; p<0.001). Sub-cohort analysis indicated the WES-based personalized assay had the highest pre-operative positive rate (95.1%). The two personalized assays and the tumor-informed fixed assay demonstrated same results in post-op landmark (HR, 26.34, 95%CI, 6.01-115.57; p<0.001), outperforming the tumor-agnostic fixed panel (HR, 3.04, 95%CI, 0.94-9.89; p=0.052). Conclusion: Our study confirmed the prognostic value of the ctDNA positivity at post-op day 7 by the tumor-informed fixed panel. The tumor-informed fixed panel may be a cost-effective method to evaluate MRD, which warrants further studies in future.

Long Noncoding RNA LINC00578 Inhibits Ferroptosis in Pancreatic Cancer via Regulating SLC7A11 Ubiquitination.

Background: Pancreatic cancer is a highly aggressive malignancy worldwide with rapid development and an exceedingly poor prognosis. lncRNAs play crucial roles in regulating the biological behaviors of tumor cells. In this study, we discovered that LINC00578 acted as a regulator of ferroptosis in pancreatic cancer. Methods: A series of loss- and gain-of-function experiments in vitro and in vivo were performed to explore the oncogenic role of LINC00578 in pancreatic cancer development and progression. Label-free proteomic analysis was performed to select LINC00578-related differentially expressed proteins. Pull-down and RNA immunoprecipitation assays were carried out to determine and validate the binding protein of LINC00578. Coimmunoprecipitation assays were used to investigate the association of LINC00578 with SLC7A11 in ubiquitination and to confirm the interaction between ubiquitin-conjugating enzyme E2 K (UBE2K) and SLC7A11. An immunohistochemical assay was used to confirm the correlation between LINC00578 and SLC7A11 in the clinic. Results: LINC00578 positively regulated cell proliferation and invasion in vitro and tumorigenesis in vivo in pancreatic cancer. LINC00578 can obviously inhibit ferroptosis events, including cell proliferation, reactive oxygen species (ROS) generation, and mitochondrial membrane potential (MMP) depolarization. In addition, the LINC00578-induced inhibitory effect on ferroptosis events was rescued by SLC7A11 knockdown. Mechanistically, LINC00578 directly binds UBE2K to decrease the ubiquitination of SLC7A11, thus accelerating SLC7A11 expression. In the clinic, LINC00578 is closely associated with clinicopathologic factors and poor prognosis and correlated with SLC7A11 expression in pancreatic cancer. Conclusions: This study elucidated that LINC00578 acts as an oncogene to promote pancreatic cancer cell progression and suppress ferroptosis by directly combining with UBE2K to inhibit the ubiquitination of SLC7A11, which provides a promising option for the diagnosis and treatment of pancreatic cancer.

Development and validation of a tissue-based DNA methylation risk-score model to predict the prognosis of surgically resected pancreatic cancer patients.

Background: Pancreatic cancer (PC) is a highly malignant tumor associated with low survival rates. It is challenging to predict the survival of surgically resected patients with PC. A prognostic staging tool could be beneficial to guide treatments and also aid post-treatment surveillance. This study aimed to identify tissue-based DNA methylation risk-score model to predict the prognosis of surgically resected pancreatic cancer patients. Methods: We performed a monocentric, retrospective study that included 50 patients with stage I-II PC from The First Affiliated Hospital of Soochow University (SU cohort). Both tumor and adjacent normal tissues were obtained from each patient and subjected to capture-based targeted methylation profiling. Results: In total, 1,162 DNA methylation blocks (DMBs) were differentially methylated in tumor tissues compared with adjacent long-distance tissues (P<0.05). Least Absolute Shrinkage and Selection Operator (LASSO) and stepwise regression analyses revealed a significant correlation between the methylation signature (risk score) and overall survival (OS). Patients in the high-risk group showed significantly poorer OS than those in the low-risk group in the survival analysis [P≤0.001; area under curve (AUC) at 1 year, 0.789; AUC at 2 years, 0.852]. The risk score was also validated using clinical and methylation data of 166 PC patients from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PDAC) dataset. Patients in the high-risk group showed significantly poorer OS than those in the low-risk group (P=0.004; AUC at 1 years, 0.677; AUC at 3 years, 0.611). When clinical parameters were considered, the risk score was the only independent prognostic parameter (P<0.001) in the Cox regression analysis. Furthermore, low-risk patients had higher levels of immune infiltration, anti-tumor immune activation, and increased sensitivity to gemcitabine and paclitaxel. In contrast, high-risk patients had lower KRAS mutation rates and benefited more from cisplatin. Conclusions: In our study, we constructed and validated a tissue-based DNA methylation risk-score model to predict prognosis and identify PC patients with a high mortality risk at the time of surgery. This model might provide a tissue-based prognostic assessment tool for clinicians to aid their treatment decision-making.

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.

The COVID-19 outbreak poses a huge challenge to international public health. Reliable forecast of the number of cases is of great significance to the planning of health resources and the investigation and evaluation of the epidemic situation. The data-driven machine learning models can adapt to complex changes in the epidemic situation without relying on correct physical dynamics modeling, which are sensitive and accurate in predicting the development of the epidemic. In this paper, an ensemble hybrid model based on Temporal Convolutional Networks (TCN), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Q-learning, and Support Vector Machine (SVM) models, namely TCN-GRU-DBN-Q-SVM model, is proposed to achieve the forecasting of COVID-19 infections. Three widely-used predictors, TCN, GRU, and DBN are used as elements of the hybrid model ensembled by the weights provided by reinforcement learning method. Furthermore, an error predictor built by SVM, is trained with validation set, and the final prediction result could be obtained by combining the TCN-GRU-DBN-Q model with the SVM error predictor. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models (TCN-GRU-DBN-Q, LSTM, N-BEATS, ANFIS, VMD-BP, WT-RVFL, and ARIMA models) are selected. The experimental results show that: (1) the prediction effect of the TCN-GRU-DBN-Q-SVM model on COVID-19 infection is satisfactory, which has been verified in three national infection data from the UK, India, and the US, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SVM can efficiently predict the possible error of the predicted series given by TCN-GRU-DBN-Q components; (3) the integrated weights based on Q-learning can be adaptively adjusted according to the characteristics of the data in the forecasting tasks in different countries and multiple situations, which ensures the accuracy, robustness and generalization of the proposed model.

Optimized photochemistry enables efficient analysis of dynamic RNA structuromes and interactomes in genetic and infectious diseases.

Direct determination of RNA structures and interactions in living cells is critical for understanding their functions in normal physiology and disease states. Here, we present PARIS2, a dramatically improved method for RNA duplex determination in vivo with >4000-fold higher efficiency than previous methods. PARIS2 captures ribosome binding sites on mRNAs, reporting translation status on a transcriptome scale. Applying PARIS2 to the U8 snoRNA mutated in the neurological disorder LCC, we discover a network of dynamic RNA structures and interactions which are destabilized by patient mutations. We report the first whole genome structure of enterovirus D68, an RNA virus that causes polio-like symptoms, revealing highly dynamic conformations altered by antiviral drugs and different pathogenic strains. We also discover a replication-associated asymmetry on the (+) and (-) strands of the viral genome. This study establishes a powerful technology for efficient interrogation of the RNA structurome and interactome in human diseases.