Hepatocellular carcinoma (HCC) cells often invade the portal venous system and subsequently develop into portal vein tumour thrombosis (PVTT). Long noncoding RNAs (lncRNAs) have been associated with HCC, but a comprehensive analysis of their specific association with HCC metastasis has not been conducted. Here, by analysing 60 clinical samples' RNA-seq data from 20 HCC patients, we have identified and characterized 8,603 candidate lncRNAs. The expression patterns of 917 recurrently deregulated lncRNAs are correlated with clinical data in a TCGA cohort and published liver cancer data. Matched array data from the 60 samples show that copy number variations (CNVs) and alterations in DNA methylation contribute to the observed recurrent deregulation of 235 lncRNAs. Many recurrently deregulated lncRNAs are enriched in co-expressed clusters of genes related to cell adhesion, immune response and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. Thus, we provide a valuable resource of functional lncRNAs and biomarkers associated with HCC tumorigenesis and metastasis.

Hepatocellular carcinoma (HCC) is highly heterogeneous in nature and has been one of the most common cancer types worldwide. To ensure repeatability of identified gene expression patterns and comprehensively annotate the transcriptomes of HCC, we carefully curated 15 public HCC expression datasets that cover around 4000 clinical samples and developed the database HCCDB to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces. The global differential gene expression landscape of HCC was established by analyzing the consistently differentially expressed genes across multiple datasets. Moreover, a 4D metric was proposed to fully characterize the expression pattern of each gene by integrating data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). To facilitate a comprehensive understanding of gene expression patterns in HCC, HCCDB also provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses, including differential expression analysis, tissue-specific and tumor-specific expression analysis, survival analysis, and co-expression analysis. HCCDB is freely accessible at http://lifeome.net/database/hccdb.

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

Spatial transcriptome technique was applied to decipher the spatial architecture and TME characteristics of liver cancers.

Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

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Heterogeneity is the major challenge for cancer prevention and therapy. Here, we firstly constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from 7 patients. The sequential comparison of spatial tumor microenvironment (TME) characteristics from non-tumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100 μm wide cluster-cluster boundary contribute to maintaining intratumor architecture. Our study provides novel insights for diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

BackgroundHepatocellular carcinoma (HCC) is the major type of primary liver cancer. Intrahepatic metastasis, such as portal vein tumor thrombosis (PVTT), strongly indicates poor prognosis of HCC. But now, there are limited understandings of the molecular features and mechanisms of those metastatic HCCs.MethodsTo characterize the molecular alterations of the metastatic HCCs, we implemented an integrative analysis of the copy number variations (CNVs), DNA methylations and transcriptomes of matched adjacent normal, primary tumor and PVTT samples from 19 HCC patients.ResultsCNV analysis identified a frequently amplified focal region chr11q13.3 and a novel deletion peak chr19q13.41 containing three miRNAs. The integrative analysis with RNA-seq data suggests that CNVs and differential promoter methylations regulate distinct oncogenic processes. Then, we used individualized differential analysis to identify the differentially expressed genes between matched primary tumor and PVTT of each patient. Results show that 5 out of 19 studied patients acquire evidential progressive alterations of gene expressions (more than 1000 differentially expressed genes were identified in each patient). While, another subset of eight patients have nearly identical gene expressions between the corresponding matched primary tumor and PVTT. Twenty genes were found to be recurrently and progressively differentially expressed in multiple patients. These genes are mainly associated with focal adhesion, xenobiotics metabolism by cytochrome P450 and amino acid metabolism. For several differentially expressed genes in metabolic pathways, their expressions are significantly associated with overall survivals and vascular invasions of HCC patients. The following transwell assay experiments validate that they can regulate invasive phenotypes of HCC cells.ConclusionsThe metastatic HCCs with PVTTs have significant molecular alterations comparing with adjacent normal tissues. The recurrent alteration patterns are similar to several previously published general HCC cohorts, but usually with higher severity. By an individualized differential analysis strategy, the progressively differentially expressed genes between the primary tumor and PVTT were identified for each patient. A few patients aquire evidential progressive alterations of gene expressions. And, experiments show that several recurrently differentially expressed genes can strongly regulate HCC cell invasions.