Components of the JNK-MAPK pathway play distinct roles in hepatocellular carcinoma.

PURPOSE: Mitogen-activated protein kinases (MAPK), specifically the c-Jun N-terminal kinase (JNK)-MAPK subfamily, play a crucial role in the development of various cancers, including hepatocellular carcinoma (HCC). However, the specific roles of JNK1/2 and their upstream regulators, MKK4/7, in HCC carcinogenesis remain unclear. METHODS: In this study, we performed differential expression analysis of JNK-MAPK components at both the transcriptome and protein levels using TCGA and HPA databases. We utilized Kaplan-Meier survival plots and receiver operating characteristic (ROC) curve analysis to evaluate the prognostic performance of a risk scoring model based on these components in the TCGA-HCC cohort. Additionally, we conducted immunoblotting, apoptosis analysis with FACS and soft agar assays to investigate the response of JNK-MAPK pathway components to various death stimuli (TRAIL, TNF-α, anisomycin, and etoposide) in HCC cell lines. RESULTS: JNK1/2 and MKK7 levels were significantly upregulated in HCC samples compared to paracarcinoma tissues, whereas MKK4 was downregulated. ROC analyses suggested that JNK2 and MKK7 may serve as suitable diagnostic genes for HCC, and high JNK2 expression correlated with significantly poorer overall survival. Knockdown of JNK1 enhanced TRAIL-induced apoptosis in hepatoma cells, while JNK2 knockdown reduced TNF-α/cycloheximide (CHX)-and anisomycin-induced apoptosis. Neither JNK1 nor JNK2 knockdown affected etoposide-induced apoptosis. Furthermore, MKK7 knockdown augmented TNF-α/CHX- and TRAIL-induced apoptosis and inhibited colony formation in hepatoma cells. CONCLUSION: Targeting MKK7, rather than JNK1/2 or MKK4, may be a promising therapeutic strategy to inhibit the JNK-MAPK pathway in HCC therapy.

Identification of hematopoietic stem cells residing in the meninges of adult mice at steady state.

Steady-state extramedullary hematopoiesis during adulthood is an emerging field of great interest. The meninges contain both innate and adaptive immune cells, which provide immunosurveillance of the central nervous system (CNS). Hematopoietic progenitors that give rise to meningeal immune cells remain elusive. Here, we report that steady-state meninges of adult mice host hematopoietic stem cells (HSCs), as defined by long-term, efficient, multi-lineage reconstitution and self-renewal capacity in the meninges, blood, spleen, and bone marrow of sublethally irradiated adult recipients. HSCs lodge in the meninges after birth with local expression of pro-hematopoietic niche factors. Meningeal HSCs are locally maintained in homeostasis and get replenished from the blood only when the resident pool is reduced. With a tissue-specific expression profile, meningeal HSCs can provide the CNS with a constant supply of leukocytes more adapted to local microenvironment.

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration.

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data.

Extensive amounts of multi-omics data and multiple cancer subtyping methods have been developed rapidly, and generate discrepant clustering results, which poses challenges for cancer molecular subtype research. Thus, the development of methods for the identification of cancer consensus molecular subtypes is essential. The lack of intuitive and easy-to-use analytical tools has posed a barrier. Here, we report on the development of the COnsensus Molecular SUbtype of Cancer (COMSUC) web server. With COMSUC, users can explore consensus molecular subtypes of more than 30 cancers based on eight clustering methods, five types of omics data from public reference datasets or users' private data, and three consensus clustering methods. The web server provides interactive and modifiable visualization, and publishable output of analysis results. Researchers can also exchange consensus subtype results with collaborators via project IDs. COMSUC is now publicly and freely available with no login requirement at http://comsuc.bioinforai.tech/ (IP address: http://59.110.25.27/). For a video summary of this web server, see S1 Video and S1 File.

Cross-Site Concordance Evaluation of Tumor DNA and RNA Sequencing Platforms for the CIMAC-CIDC Network.

PURPOSE: Whole-exome (WES) and RNA sequencing (RNA-seq) are key components of cancer immunogenomic analyses. To evaluate the consistency of tumor WES and RNA-seq profiling platforms across different centers, the Cancer Immune Monitoring and Analysis Centers (CIMAC) and the Cancer Immunologic Data Commons (CIDC) conducted a systematic harmonization study. EXPERIMENTAL DESIGN: DNA and RNA were centrally extracted from fresh frozen and formalin-fixed paraffin-embedded non-small cell lung carcinoma tumors and distributed to three centers for WES and RNA-seq profiling. In addition, two 10-plex HapMap cell line pools with known mutations were used to evaluate the accuracy of the WES platforms. RESULTS: The WES platforms achieved high precision (> 0.98) and recall (> 0.87) on the HapMap pools when evaluated on loci using > 50× common coverage. Nonsynonymous mutations clustered by tumor sample, achieving an index of specific agreement above 0.67 among replicates, centers, and sample processing. A DV200 > 24% for RNA, as a putative presequencing RNA quality control (QC) metric, was found to be a reliable threshold for generating consistent expression readouts in RNA-seq and NanoString data. MedTIN > 30 was likewise assessed as a reliable RNA-seq QC metric, above which samples from the same tumor across replicates, centers, and sample processing runs could be robustly clustered and HLA typing, immune infiltration, and immune repertoire inference could be performed. CONCLUSIONS: The CIMAC collaborating laboratory platforms effectively generated consistent WES and RNA-seq data and enable robust cross-trial comparisons and meta-analyses of highly complex immuno-oncology biomarker data across the NCI CIMAC-CIDC Network.

CRISP-view: a database of functional genetic screens spanning multiple phenotypes.

High-throughput genetic screening based on CRISPR/Cas9 or RNA-interference (RNAi) enables the exploration of genes associated with the phenotype of interest on a large scale. The rapid accumulation of public available genetic screening data provides a wealth of knowledge about genotype-to-phenotype relationships and a valuable resource for the systematic analysis of gene functions. Here we present CRISP-view, a comprehensive database of CRISPR/Cas9 and RNAi screening datasets that span multiple phenotypes, including in vitro and in vivo cell proliferation and viability, response to cancer immunotherapy, virus response, protein expression, etc. By 22 September 2020, CRISP-view has collected 10 321 human samples and 825 mouse samples from 167 papers. All the datasets have been curated, annotated, and processed by a standard MAGeCK-VISPR analysis pipeline with quality control (QC) metrics. We also developed a user-friendly webserver to visualize, explore, and search these datasets. The webserver is freely available at http://crispview.weililab.org.