A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

High hydrostatic pressure participates in atrial fibrosis through the p300/p53/Smad3 pathway.

As an independent risk factor of atrial fibrillation (AF), hypertension (HTN) can induce atrial fibrosis through cyclic stretch and hydrostatic pressure. The mechanism by which high hydrostatic pressure promotes atrial fibrosis is unclear yet. p300 and p53/Smad3 play important roles in the process of atrial fibrosis. This study investigated whether high hydrostatic pressure promotes atrial fibrosis by activating the p300/p53/Smad3 pathway. Biochemical experiments were used to study the expression of p300/p53/Smad3 pathway in left atrial appendage (LAA) tissues of patients with sinus rhythm (SR), AF, AF + HTN, and C57/BL6 mice, hypertensive C57/BL6 mice and atrial fibroblasts of mice. To investigate the roles of p300 and p53 in the process of atrial fibrosis, p300 and p53 in mice atrial fibroblasts were knocked in or knocked down, respectively. The expression of p300/p53/Smad3 and fibrotic factors was higher in patients with AF and AF + HTN than those with SR only. The expressions of p300/p53/Smad3 and fibrotic factors increased in hypertensive mice. Curcumin (Cur) and knocking down of p300 reversed the expressions of these factors. 40 mmHg hydrostatic pressure/overexpression of p300 upregulated the expressions of p300/p53/Smad3 and fibrotic factors in mice LAA fibroblasts. While Cur or knocking down p300 reversed these changes. Knocking down/overexpression of p53, the expressions of p53/Smad3 and fibrotic factors also decreased/increased, correspondingly. High hydrostatic pressure promotes atrial fibrosis by activating the p300/p53/Smad3 pathway, which further increases the susceptibility to AF.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

Alkbh5 plays indispensable roles in maintaining self-renewal of hematopoietic stem cells.

Alkbh5 is one of the primary demethylases responsible for reversing N6-methyladenosine (m6A) modifications on mRNAs, and it plays a crucial role in many physiological and pathological processes. Previous studies have shown that Alkbh5 is required for maintaining the function of leukemia stem cells but is dispensable for normal hematopoiesis. In this study, we found that Alkbh5 deletion led to a moderate increase in the number of multiple progenitor cell populations while compromising the long-term self-renewal capacity of hematopoietic stem cells (HSCs). Here, we used RNA-seq and m6A-seq strategies to explore the underlying molecular mechanism. At the molecular level, Alkbh5 may regulate hematopoiesis by reducing m6A modification of Cebpa and maintaining gene expression levels. Overall, our study unveiled an essential role for Alkbh5 in regulating HSC homeostasis and provides a reference for future research in this area.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data.

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

Relationship between serum fibroblast growth factor 19 and vascular endothelial growth factor and soluble klotho protein in type 1 diabetic children.

BACKGROUND: Fibroblast growth factor 19 (FGF19) takes part in maintaining the balance of glycolipids and may be involved in complications of type 1 diabetes(T1D) in children. This study aimed at at evaluating the relationship among the levels of serum FGF19 and vascular endothelial growth factor(VEGF)and soluble klotho protein(sklotho) in type 1 diabetic children. METHODS: In a cross-section single center study samples were obtained from 96 subjects: 66 T1D and 30 healthy children.Serum FGF19 and VEGF and sklotho concentrations were measured by ELISA. And 66 type 1 diabetes participants were divided into two groups according to T1D duration or three groups according to HbA1c.Furthermore,we compared the serum levels of FGF19 and VEGF and sklotho in different groups. RESULTS: The concentration of FGF19 was lower in T1D than in the controls(226.52 ± 20.86pg/mu vs.240.08 ± 23.53 pg/L, p = 0.03),while sklotho was also lower in T1D than in the controls (2448.67 ± 791.92pg/mL vs. 3083.55 ± 1113.47pg/mL, p = 0.011). In contrast, VEGF levels were higher in diabetic patients than in controls (227.95 ± 48.65pg/mL vs. 205.92 ± 28.27 pg/mL, p = 0.016). In T1D, FGF19 and VEGF and sklotho was not correlated with the duration of diabetes. FGF19 and VEGF and sklotho were correlated with HbA1c (r=-0.349, p = 0.004 and r = 0.302, p = 0.014 and r=-0.342, p = 0.005, respectively), but not with blood glucose and lipid. Among subjects in the T1D group, concentrations of FGF19,VEGF and sklotho protein were different between different groups according to the degree of HbA1c(P < 0.005).Furthermore, there was a positive correlation between the serum FGF19 concentration and sklotho levels (r = 0.247,p = 0.045), and a negative correlation between the serum FGF19 concentration and VEGF level(r=-0.335,P = 0.006). CONCLUSIONS: The serum FGF19 levels have a close relation with serum VEGF levels and sklotho levels among T1D subjects. FGF19 may be involved in the development of complications in children with type 1 diabetes through interaction with VEGF and sklotho.

BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data.

Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean-variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression.

BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants.

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

SCANNER: a web platform for annotation, visualization and sharing of single cell RNA-seq data.

In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality and complexity bring challenges to the data analysis and require extensive collaborations between biologists and bioinformaticians and/or biostatisticians. The communication between these two units demands a platform for easy data sharing and exploration. Here we developed Single-Cell Transcriptomics Annotated Viewer (SCANNER), as a public web resource for the scientific community, for sharing and analyzing scRNA-seq data in a collaborative manner. It is easy-to-use without requiring special software or extensive coding skills. Moreover, it equipped a real-time database for secure data management and enables an efficient investigation of the activation of gene sets on a single-cell basis. Currently, SCANNER hosts a database of 19 types of cancers and COVID-19, as well as healthy samples from lungs of smokers and non-smokers, human brain cells and peripheral blood mononuclear cells (PBMC). The database will be frequently updated with datasets from new studies. Using SCANNER, we identified a larger proportion of cancer-associated fibroblasts cells and more active fibroblast growth-related genes in melanoma tissues in female patients compared to male patients. Moreover, we found ACE2 is mainly expressed in lung pneumocytes, secretory cells and ciliated cells and differentially expressed in lungs of smokers and never smokers.

SCISSOR™: a single-cell inferred site-specific omics resource for tumor microenvironment association study.

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor-tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes.

Integrated profiling identifies ITGB3BP as prognostic biomarker for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly malignant tumor. In this study, we sought to identify a novel biomarker for HCC by analyzing transcriptome and clinical data. The R software was used to analyze the differentially expressed genes (DEGs) in the datasets GSE74656 and GSE84598 downloaded from the Gene Expression Omnibus database, followed by a functional annotation. A total of 138 shared DEGs were screened from two datasets. They were mainly enriched in the "Metabolic pathways" pathway (Padj = 8.21E-08) and involved in the carboxylic acid metabolic process (Padj = 0.0004). The top 10 hub genes were found by protein-protein interaction analysis and were upregulated in HCC tissues compared to normal tissues in The Cancer Genome Atlas database. Survival analysis distinguished 8 hub genes CENPE, SPDL1, Hyaluronan-mediated motility receptor, Rac GTPase activating protein 1, Thyroid hormone receptor interactor 13, cytoskeleton-associated protein (CKAP) 2, CKAP5, and Integrin subunit beta 3 binding protein (ITGB3BP) were considered as prognostic hub genes. Multivariate cox regression analysis indicated that all the prognostic hub genes were independent prognostic factors for HCC. Furthermore, the receiver operating characteristic curve revealed that the 8-hub genes model had better prediction performance for overall survival compared to the T stage (p = 0.008) and significantly improved the prediction value of the T stage (p = 0.002). The Human Protein Atlas showed that the protein expression of ITGB3BP was upregulated in HCC, so the expression of ITGB3BP was further verified in our cohort. The results showed that ITGB3BP was upregulated in HCC tissues and was significantly associated with lymph node metastasis.

Zfp521 is essential for the quiescence and maintenance of adult hematopoietic stem cells under stress.

Zinc finger protein 521 (Zfp521), a quiescent hematopoietic stem cell (HSC)-enriched transcription factor, is involved in the self-renewal and differentiation of fetal liver HSC. However, its role in adult hematopoiesis remains elusive. Here, we found that Zfp521 deletion did not inhibit adult hematopoiesis under homeostatic conditions. In contrast, Zfp521-null chimeric mice showed significantly reduced pool size of HSC and hematopoietic progenitor cells associated with increased apoptosis and loss of quiescence. Competitive serial transplantation assays revealed that Zfp521 regulates HSC self-renewal and differentiation under regenerative stress. Mechanistically, Zfp521 transcriptionally repressed Rela expression by increasing H3K9ac and decreasing H3K9me3 levels in its promoter. Knockdown of Rela inhibited the hyper-activated NF-κB pathway and reversed the loss of quiescence in Zfp521-null HSC under stress. Thus, our results reveal a previously unrecognized role for Zfp521 as critical regulator of quiescence and self-renewal of HSC in adult hematopoiesis mediated at least partly by controlling Rela expression.

A Cluster-Based Approach for Identifying Prognostic microRNA Signatures in Digestive System Cancers.

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value < 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

A single copy of large tumor suppressor 1 or large tumor suppressor 2 is sufficient for normal hematopoiesis.

BACKGROUND: Hematopoietic stem cells (HSCs) have the ability to differentiate into all subsets of blood cells and self-renew. Large tumor suppressor 1 (LATS1) and large tumor suppressor 2 (LATS2) kinases are essential for cell cycle regulation, organism fitness, genome integrity, and cancer prevention. Here, we investigated whether Lats1 and Lats2 are critical for the maintenance of the self-renewal and quiescence capacities of HSCs in mice. METHODS: Quantitative reverse transcription-polymerase chain reaction was used to determine the expression levels of Lats1 and Lats2 in subsets of progenitor cells and mature bone marrow cells. A clustered regularly interspaced short palindromic repeats system was used to generate Lats1 or Lats2 knockout mice. Complete blood cell counts were used to compare the absolute number of white blood cells, lymphocytes, monocytes, neutrophils, and platelets between Lats1 or Lats2 heterozygotes and littermates. Flow cytometry was used to assess the size of hematopoietic progenitor cells (HPCs) and HSC pools in Lats1 or Lats2 heterozygotes and littermates. The comparison between the two groups was analyzed using Student's t test. RESULTS: Lats1 and Lats2 were widely expressed in hematopoietic cells with higher expression levels in primitive hematopoietic cells than in mature cells. Lats1 or Lats2 knockout mice were generated, with the homozygotes showing embryonic lethality. The size of the HPC and HSC pools in Lats1 (HPC: wild-type [WT] vs. heterozygote, 220,426.77 ±â€Š54,384.796 vs. 221,149.4 ±â€Š42,688.29, P = 0.988; HSC: WT vs. heterozygote, 2498.932 ±â€Š347.856 vs. 3249.763 ±â€Š370.412, P = 0.105) or Lats2 (HPC: WT vs. heterozygote, 425,540.52 ±â€Š99,721.86 vs. 467,127.8 ±â€Š89,574.48, P = 0.527; HSC: WT vs. heterozygote, 4760.545 ±â€Š1518.01 vs. 5327.437 ±â€Š873.297, P = 0.502) heterozygotes were not impaired. Moreover, the depletion of Lats1 or Lats2 did not affect the overall survival of the heterozygotes (Lats1: P = 0.654; Lats2: P = 0.152). CONCLUSION: These results indicate that a single allele of Lats1 or Lats2 may be sufficient for normal hematopoiesis.

Zinc finger and BTB domain-containing protein 46 is essential for survival and proliferation of acute myeloid leukemia cell line but dispensable for normal hematopoiesis.

BACKGROUND: Zinc finger and BTB domain-containing protein 46 (Zbtb46) is a transcription factor identified in classical dendritic cells, and maintains dendritic cell quiescence in a steady state. Zbtb46 has been reported to be a negative indicator of acute myeloid leukemia (AML). We found that Zbtb46 was expressed at a relatively higher level in hematopoietic stem and progenitor cells (HSPCs) compared to mature cells, and higher in AML cells compared to normal bone marrow (BM) cells. However, the role of Zbtb46 in HSPCs and AML cells remains unclear. Therefore, we sought to elucidate the effect of Zbtb46 in normal hematopoiesis and AML cells. METHODS: We generated Zbtb46 and Zbtb46Mx1-Cre mice. The deletion of Zbtb46 in Zbtb46Mx1-Cre mice was induced by intraperitoneal injection of double-stranded poly (I). poly (C) (poly(I:C)), and referred as Zbtb46 cKO. After confirming the deletion of Zbtb46, the frequency and numbers of HSPCs and mature blood cells were analyzed by flow cytometry. Serial intraperitoneal injection of 5-fluorouracil was administrated to determine the repopulation ability of HSCs from Zbtb46 and Zbtb46 cKO mice. The correlation between Zbtb46 expression and prognosis was analyzed using the data from the Cancer Genome Atlas. To investigate the role of Zbtb46 in AML cells, we knocked down the expression of Zbtb46 in THP-1 cells using lentiviral vectors expressing small hairpin RNAs targeting Zbtb46. Cell proliferation rate was determined by cell count assay. Cell apoptosis and bromodeoxyuridine incorporation were determined by flow cytometry. RESULTS: The percentages and absolute numbers of HSPCs and mature blood cells were comparable in Zbtb46 cKO mice and its Zbtb46 littermates (Zbtb46vs. Zbtb46 cKO, HPC: 801,310 ±â€Š84,282 vs. 907,202 ±â€Š97,403, t = 0.82, P = 0.46; LSK: 86,895 ±â€Š7802 vs. 102,210 ±â€Š5025, t = 1.65, P = 0.17; HSC: 19,753 ±â€Š3116 vs. 17,608 ±â€Š3508, t = 0.46, P = 0.67). The repopulation ability of HSCs from Zbtb46Mx1-Cre mice was similar to those from Zbtb46 control (P = 0.26). Zbtb46 had elevated expression in AML cells compared to total BM cells from normal control. Knockdown of Zbtb46 in THP-1 cells led to a significant increase in cell apoptosis and reduced cell growth and proliferation. CONCLUSION: Collectively, our data indicate that Zbtb46 is essential for survival and proliferation of AML cells, but dispensable for normal hematopoiesis.

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer.

Simultaneous identification of multiple single genes and multi-gene prognostic signatures with higher efficacy in liver cancer has rarely been reported. Here, 1,173 genes potentially related to the liver cancer prognosis were mined with Coremine, and the gene expression and survival data in 370 samples for overall survival (OS) and 319 samples for disease-free survival (DFS) were retrieved from The Cancer Genome Atlas. Numerous survival analyses results revealed that 39 genes and 28 genes significantly associated with DFS and OS in liver cancer, including 18 and 12 novel genes that have not been systematically reported in relation to the liver cancer prognosis, respectively. Next, totally 9,139 three-gene combinations (including 816 constructed by 18 novel genes) for predicting DFS and 3,276 three-gene combinations (including 220 constructed by 12 novel genes) for predicting OS were constructed based on the above genes, and the top 15 of these four parts three-gene combinations were selected and shown. Moreover, a huge difference between high and low expression group of these three-gene combination was detected, with median survival difference of DFS up to 65.01 months, and of OS up to 83.57 months. The high or low expression group of these three-gene combinations can predict the longest prognosis of DFS and OS is 71.91 months and 102.66 months, and the shortest is 6.24 months and 13.96 months. Quantitative real-time polymerase chain reaction and immunohistochemistry reconfirmed that three genes F2, GOT2, and TRPV1 contained in one of the above combinations, are significantly dysregulated in liver cancer tissues, low expression of F2, GOT2, and TRPV1 is associated with poor prognosis in liver cancer. Overall, we discovered a few novel single genes and multi-gene combinations biomarkers that are closely related to the long-term prognosis of liver cancer, and they can be potential therapeutic targets for liver cancer.