A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data.

In this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset. UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction and other preprocessing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, Middle East respiratory syndrome and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv.

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases.

Background: Brain metastasis, with an incidence of more than 30%, is a common complication of non-small cell lung cancer (NSCLC). Therefore, there is an urgent need for an assessment method that can effectively predict brain metastases in NSCLC and help understand its mechanism. Materials and methods: GSE30219, GSE31210, GSE37745, and GSE50081 datasets were downloaded from the GEO database and integrated into a dataset (GSE). The integrated dataset was divided into the training and test datasets. TCGA-NSCLC dataset was regarded as an independent verification dataset. Here, the limma R package was used to identify the differentially expression genes (DEGs). Importantly, the RiskScore model was constructed using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Moreover, we explored in detail the tumor mutational signature, immune signature, and sensitivity to treatment of brain metastases in NSCLC. Finally, a nomogram was built using the rms package. Results: First, 472 DEGs associated with brain metastases in NSCLC were obtained, which were closely associated with cancer-associated pathways. Interestingly, a RiskScore model was constructed using 11 genes from 472 DEGs, and the robustness was confirmed in GSE test, entire GSE, and TCGA datasets. Samples in the low RiskScore group had a higher gene mutation score and lower immunoinfiltration status. Moreover, we found that the patients in the low RiskScore group were more sensitive to the four chemotherapy drugs. In addition, the predictive nomogram model was able to effectively predict the outcome of patients through appropriate RiskScore stratification. Conclusion: The prognostic RiskScore model we established has high prediction accuracy and survival prediction ability for brain metastases in NSCLC.

Predicting the Clinical Outcome of Triple-Negative Breast Cancer Based on the Gene Expression Characteristics of Necroptosis and Different Molecular Subtypes.

Necroptosis, a kind of programmed necrotic cell apoptosis, is the gatekeeper for the host to defend against the invasion of pathogens. It helps to regulate different biological processes regarding human cancer. Nevertheless, studies that determine the impact of death on triple-negative breast cancer (TNBC) are scarce. Therefore, this paper has comprehensively examined the expression as well as clinical significance of necroptosis in TNBC. ConsensusClusterPlus was used to establish a stable molecular classification that used the expression regarding the necroptosis-linked genes. The clinical and immune characteristics of different subclasses were evaluated. Then, the weighted gene coexpression network analysis (WGCNA) assisted in determining key modules, and we selected the genes exhibiting obvious association with necroptosis prognosis through the relationship with prognosis. The univariate Cox regression analysis together with least absolute shrinkage and selection operator (LASSO) techniques served for the construction of the necroptosis-related prognostic risk score (NPRS) model, and the pathway characteristics of NPRS model grouping were further studied. Finally, the NPRS, taking into account the clinicopathological features, used the decision tree model for enhancing the prognostic model as well as the survival prediction. First, two stable molecular subtypes with different prognosis and immune characteristics were identified using necroptosis marker genes. Then, the key modules were identified, and 10 genes significantly related to the prognosis of necroptosis were selected. Then, the clinical prognostic model of NPRS was developed considering the prognosis-linked necroptosis genes. Finally, the NPRS model, taking into account the clinicopathological features, adopted the decision tree model for enhancing the prognostic model as well as the survival prediction. Herein, two new molecular subgroups considering necroptosis-linked genes are proposed, and an NPRS model composed of 10 genes is developed, which maybe assist in the personalized treatment and clinical treatment guidance of TNBC patients.