Expression trend of 34 HCC specific genes reported as deregulated in more than 4 studies in the public Liverome database was compared to experimental expression trend.aaverage rate of expression from different Illumina probes.bdiscrepancies between Liverome studies results.Table 3 References.1) Chan, K.Y., Lai, P.B., Squire, J.A., Beheshti, B., Wong, N.L., Sy, S.M., Wong, N., 2006. Positional expression profiling indicates candidate genes in deletion hotspots of hepatocellular carcinoma. Mod Pathol 19, 1546–1554.2) Chung, E.J., Sung, Y.K., Farooq, M., Kim, Y., Im, S., Tak, W.Y., Hwang, Y.J., Kim, Y.I., Han, H.S., Kim, J.C., Kim, M.K., 2002. Gene expression profile analysis in human hepatocellular carcinoma by cDNA microarray. Mol Cells 14, 382–387.3) Cui, X.D., Lee, M.J., Yu, G.R., Kim, I.H., Yu, H.C., Song, E.Y., Kim, D.G., 2010. EFNA1 ligand and its receptor EphA2: potential biomarkers for hepatocellular carcinoma. Int J Cancer 126, 940–949.4) De Giorgi, V., Monaco, A., Worchech, A., Tornesello, M., Izzo, F., Buonaguro, L., Marincola, F.M., Wang, E., Buonaguro, F.M., 2009. Gene profiling, biomarkers and pathways characterizing HCV-related hepatocellular carcinoma. J Transl Med 7, 85.5) Delpuech, O., Trabut, J.B., Carnot, F., Feuillard, J., Brechot, C., Kremsdorf, D., 2002. Identification, using cDNA macroarray analysis, of distinct gene expression profiles associated with pathological and virological features of hepatocellular carcinoma. Oncogene 21, 2926–2937.6) Dong, H., Ge, X., Shen, Y., Chen, L., Kong, Y., Zhang, H., Man, X., Tang, L., Yuan, H., Wang, H., Zhao, G., Jin, W., 2009. Gene expression profile analysis of human hepatocellular carcinoma using SAGE and LongSAGE. BMC Med Genomics 2, 5.7) Goldenberg, D., Ayesh, S., Schneider, T., Pappo, O., Jurim, O., Eid, A., Fellig, Y., Dadon, T., Ariel, I., de Groot, N., Hochberg, A., Galun, E., 2002. Analysis of differentially expressed genes in hepatocellular carcinoma using cDNA arrays. Mol Carcinog 33, 113–124.8) Iizuka, N., Tsunedomi, R., Tamesa, T., Okada, T., Sakamoto, K., Hamaguchi, T., Yamada-Okabe, H., Miyamoto, T., Uchimura, S., Hamamoto, Y., Oka, M., 2006. Involvement of c-myc-regulated genes in hepatocellular carcinoma related to genotype-C hepatitis B virus. J Cancer Res Clin Oncol 132, 473–481.9) Kato, K., Yamashita, R., Matoba, R., Monden, M., Noguchi, S., Takagi, T., Nakai, K., 2005. Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues. Nucleic Acids Res 33, D533–536.10) Kim, B.Y., Lee, J.G., Park, S., Ahn, J.Y., Ju, Y.J., Chung, J.H., Han, C.J., Jeong, S.H., Yeom, Y.I., Kim, S., Lee, Y.S., Kim, C.M., Eom, E.M., Lee, D.H., Choi, K.Y., Cho, M.H., Suh, K.S., Choi, D.W., Lee, K.H., 2004. Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray. Biochim Biophys Acta 1739, 50–61.11) Kurokawa, Y., Matoba, R., Takemasa, I., Nakamori, S., Tsujie, M., Nagano, H., Dono, K., Umeshita, K., Sakon, M., Ueno, N., Kita, H., Oba, S., Ishii, S., Kato, K., Monden, M., 2003. Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study. J Hepatol 39, 1004–1012.12) Lee, M.J., Yu, G.R., Park, S.H., Cho, B.H., Ahn, J.S., Park, H.J., Song, E.Y., Kim, D.G., 2008. Identification of cystatin B as a potential serum marker in hepatocellular carcinoma. Clin Cancer Res 14, 1080–1089.13) Li, Y., Tang, R., Xu, H., Qiu, M., Chen, Q., Chen, J., Fu, Z., Ying, K., Xie, Y., Mao, Y., 2002. Discovery and analysis of hepatocellular carcinoma genes using cDNA microarrays. J Cancer Res Clin Oncol 128, 369–379.14) Okabe, H., Satoh, S., Kato, T., Kitahara, O., Yanagawa, R., Yamaoka, Y., Tsunoda, T., Furukawa, Y., Nakamura, Y., 2001. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Cancer Res 61, 2129–2137.15) Patil, M.A., Chua, M.S., Pan, K.H., Lin, R., Lih, C.J., Cheung, S.T., Ho, C., Li, R., Fan, S.T., Cohen, S.N., Chen, X., So, S., 2005. An integrated data analysis approach to characterize genes highly expressed in hepatocellular carcinoma. Oncogene 24, 3737–3747.16) Shirota, Y., Kaneko, S., Honda, M., Kawai, H.F., Kobayashi, K., 2001. Identification of differentially expressed genes in hepatocellular carcinoma with cDNA microarrays. Hepatology 33, 832–840.17) Tackels-Horne, D., Goodman, M.D., Williams, A.J., Wilson, D.J., Eskandari, T., Vogt, L.M., Boland, J.F., Scherf, U., Vockley, J.G., 2001. Identification of differentially expressed genes in hepatocellular carcinoma and metastatic liver tumors by oligonucleotide expression profiling. Cancer 92, 395–405.18) Xu, L., Hui, L., Wang, S., Gong, J., Jin, Y., Wang, Y., Ji, Y., Wu, X., Han, Z., Hu, G., 2001a. Expression profiling suggested a regulatory role of liver-enriched transcription factors in human hepatocellular carcinoma. Cancer Res 61, 3176–3181.19) Xu, X.R., Huang, J., Xu, Z.G., Qian, B.Z., Zhu, Z.D., Yan, Q., Cai, T., Zhang, X., Xiao, H.S., Qu, J., Liu, F., Huang, Q.H., Cheng, Z.H., Li, N.G., Du, J.J., Hu, W., Shen, K.T., Lu, G., Fu, G., Zhong, M., Xu, S.H., Gu, W.Y., Huang, W., Zhao, X.T., Hu, G.X., Gu, J.R., Chen, Z., Han, Z.G., 2001b. Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver. Proc Natl Acad Sci U S A 98, 15089–15094.20) Yamashita, T., Kaneko, S., Hashimoto, S., Sato, T., Nagai, S., Toyoda, N., Suzuki, T., Kobayashi, K., Matsushima, K., 2001. Serial analysis of gene expression in chronic hepatitis C and hepatocellular carcinoma. Biochem Biophys Res Commun 282, 647–654.21) Zekri, A.R., Hafez, M.M., Bahnassy, A.A., Hassan, Z.K., Mansour, T., Kamal, M.M., Khaled, H.M., 2008. Genetic profile of Egyptian hepatocellular-carcinoma associated with hepatitis C virus Genotype 4 by 15 K cDNA microarray: preliminary study. BMC Res Notes 1, 106.

OncoDB.HCC is the first comprehensive oncogenomic database for HCC. It effectively integrates three datasets from public references to provide multi-dimension view of current HCC studies. The three datasets included are Chromosome aberration studies, Gene expression studies, and HCC model organisms (rats and mice).

Objective: The purpose of this study was to develop and validate a novel immune checkpoint–related gene signature for prediction of overall survival (OS) in hepatocellular carcinoma (HCC).Methods: mRNA expression profiles and clinical follow-up information were obtained in the International Cancer Genome Consortium database. An external dataset from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma database was used to validate the results. The univariate and multivariate Cox regression analyses were performed based on the differentially expressed genes. We generated a four-mRNA signature to predict patient survival. Furthermore, the reliability and validity were validated in TCGA cohort. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value.Results: A four-gene (epidermal growth factor, mutated in colorectal cancer, mitogen-activated protein kinase kinase 2, and NRAS proto-oncogene, GTPase) signature was built to classify patients into two risk groups using a risk score with different OS in two cohorts (all P < 0.0001). Multivariate regression analysis demonstrated the signature was an independent predictor of HCC. Furthermore, the signature presented an excellent diagnostic power in differentiating HCC and adjacent tissues. Immune cell infiltration analysis revealed that the signature was associated with a number of immune cell subtypes.Conclusion: We identified a four–immune checkpoint–related gene signature as a robust biomarker with great potential for clinical application in risk stratification and OS prediction in HCC patients and could be a potential indicator of immunotherapy in HCC. The diagnostic signature had been validated to accurately distinguish HCC from adjacent tissues.

Objectives: The goal of our bioinformatics study was to comprehensively analyze the association between the whole calpain family members and the progression and prognosis of hepatocellular carcinoma (HCC).Methods: The data were collected from The Cancer Genome Atlas (TCGA). The landscape of the gene expression, copy number variation (CNV), mutation, and DNA methylation of calpain members were analyzed. Clustering analysis was performed to stratify the calpain-related groups. The least absolute shrinkage and selection operator (LASSO)-based Cox model was used to select hub survival genes.Results: We found 14 out of 16 calpain members expressed differently between tumor and normal tissues of HCC. The clustering analyses revealed high- and low-risk calpain groups which had prognostic difference. We found the high-risk calpain group had higher B cell infiltration and higher expression of immune checkpoint genes HAVCR2, PDCD1, and TIGHT. The CMap analysis found that the histone deacetylase (HDAC) inhibitor trichostatin A and the PI3K-AKT-mTOR pathway inhibitors LY-294002 and wortmannin might have a therapeutic effect on the high-risk calpain group. The DEGs between calpain groups were identified. Subsequent univariate Cox analysis of each DEG and LASSO-based Cox model obtained a calpain-related prognostic signature. The risk score model of this signature showed good ability to predict the overall survival of HCC patients in TCGA datasets and external validation datasets from the Gene Expression Omnibus database and the International Cancer Genome Consortium database.Conclusion: We found that calpain family members were associated with the progression, prognosis, and drug response of HCC. Our results require further studies to confirm.

The aim of the current study was to identify biomarkers that correlate with the Barcelona Clinic Liver Cancer (BCLC) staging system and prognosis of patients with hepatocellular carcinoma (HCC). We downloaded 4 gene expression datasets from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo), and screened for genes that were differentially expressed between HCC and normal liver tissues, using significance analysis of the microarray algorithm. We used a weighted gene co-expression network analysis (WGCNA) to identify hub genes that correlate with BCLC staging, functional enrichment analysis to associate hub genes with their functions, protein-protein interaction network analysis to identify interactions among hub genes, UALCAN analysis to assess gene expression levels based on tumour stage, and survival analyses to clarify the effects of hub genes on patients' overall survival (OS). We identified 50 relevant hub genes using WGCNA; among them, 13 genes (including TIGD5,...

Tumor-associated autoantibodies (TAAbs) have demonstrated potential as biomarkers for cancer detection. However, the understanding of their role in hepatocellular carcinoma (HCC) remains limited. In this study, we aimed to systematically collect and standardize information about these TAAbs and establish a comprehensive database as a platform for in-depth research. A total of 170 TAAbs were identified from published papers retrieved from PubMed, Web of Science, and Embase. Following normative reannotation, these TAAbs were referred to as 162 official symbols. The hccTAAb (tumor-associated autoantibodies in hepatocellular carcinoma) atlas was developed using the R Shiny framework and incorporating literature-based and multiomics data sets. This comprehensive online resource provides key information such as sensitivity, specificity, and additional details such as official symbols, official full names, UniProt, NCBI, HPA, neXtProt, and aliases through hyperlinks. Additionally, hccTAAb offers six analytical modules for visualizing expression profiles, survival analysis, immune infiltration, similarity analysis, DNA methylation, and DNA mutation analysis. Overall, the hccTAAb Atlas provides valuable insights into the mechanisms underlying TAAb and has the potential to enhance the diagnosis and treatment of HCC using autoantibodies. The hccTAAb Atlas is freely accessible at https://nscc.v.zzu.edu.cn/hccTAAb/.

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with poor prognosis. There is a necessary search for improvement in diagnosis and treatment methods to improve the prognosis. Some useful prognostic markers of HCC are still lacking. Pyroptosis is a type of programmed cell death caused by the inflammasome. It is still unknown whether pyroptosis-related genes (PRGs) are involved in the prognosis in HCC. The gene expression and clinical data of LIHC (liver hepatocellular carcinoma) patients were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium database (ICGC). In this study, we identified 40 PRGs that were differentially expressed between LIHC and normal liver tissues. Based on the TCGA-LIHC cohort, a 9-gene prediction model was established with the Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression. The risk score was calculated according to the model in the TCGA-LIHC cohort and the ICGC-LIHC cohort. Utilizing the median risk score from the TCGA cohort, LIHC patients from the ICGC-LIHC cohort were divided into two risk subgroups. The Kaplan–Meier (KM) survival curves demonstrated that patients with lower risk scores had significantly favorable overall survival (OS). Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of LIHC patients in both the TCGA-LIHC cohort and the ICGC-LIHC cohort. Functional enrichment and immune function analysis were carried out. Furthermore, a nomogram based on risk score, age, gender, and tumor stage was used to predict mortality of patients with LIHC. Moreover, KM survival analysis was performed for 9 genes in the risk model, among which CHMP4A, SCAF11, and GSDMC had significantly different results and the ceRNA network was constructed. Based on the core role of SCAF11, we performed loss-of-function experiments to explore the function of SCAF11 in vitro. Suppression of SCAF11 expression inhibited the proliferation, attenuated the migration and invasion, and induced apoptosis of liver cancer cell lines. In conclusion, the pyroptosis-related model and nomogram can be utilized for the clinical prognostic prediction in LIHC. This study has demonstrated for the first time that SCAF11 promotes the progression of liver cancer.

Objective: Transcription elongation factor 1 (TCERG1) is a nuclear protein consisted of multiple protein structural domains that plays an important role in regulating the transcription, extension, and splicing regulation of RNA polymerase II. However, the prognostic and immunological role of TCERG1 in human cancer remains unknown. In this study, we analyzed the expression of TCERG1 gene in hepatocellular carcinoma (HCC) patients, its clinical significance, and its possible prognostic value by bioinformatics.Methods: RNA sequencing data and clinicopathological characteristics of patients with HCC were collected from TCGA and CCLE databases. The Wilcoxon rank-sum test was used to analyze the expression of TCERG1 in HCC tissues and normal tissues. The protein levels of TCERG1 between normal and liver cancer tissues were analyzed by the Human Protein Atlas Database (HPA) (www.proteinatlas.org). Validation was performed using the Gene Expression Omnibus (GEO) dataset of 167 samples. The expression of TCERG1 in HCC cells were verified by qRT-PCR, and CCK-8, scratch assay and Transwell assay were performed to detect cell proliferation, migration and invasion ability. According to the median value of TCERG1 expression, patients were divided into high and low subgroups. Logistic regression, GSEA enrichment, TME, and single-sample set gene enrichment analysis (ssGSEA) were performed to explore the effects of TCERG1 on liver cancer biological function and immune infiltrates. TCERG1 co-expression networks were studied through the CCLE database and the LinkedOmics database to analyze genes that interact with TCERG1.Results: The expression levels of TCERG1 in HCC patient tissues were significantly higher than in normal tissues. Survival analysis showed that high levels of TCERG1 expression were significantly associated with low survival rates in HCC patients. Multifactorial analysis showed that high TCERG1 expression was an independent risk factor affecting tumor prognosis. This result was also verified in the GEO database. Cellular experiments demonstrated that cell proliferation, migration and invasion were inhibited after silencing of TCERG1 gene expression. Co-expression analysis revealed that CPSF6 and MAML1 expression were positively correlated with TCERG1. GSEA showed that in samples with high TCERG1 expression, relevant signaling pathways associated with cell cycle, apoptosis, pathways in cancer and enriched in known tumors included Wnt signaling pathway, Vegf signaling pathway, Notch signaling pathway, MAPK signaling pathway and MTOR pathways. The expression of TCERG1 was positively correlated with tumor immune infiltrating cells (T helper two cells, T helper cells).Conclusion:TCERG1 gene is highly expressed in hepatocellular carcinoma tissues, which is associated with the poor prognosis of liver cancer, and may be one of the markers for the diagnosis and screening of liver cancer and the prediction of prognosis effect. At the same time, TCERG1 may also become a new target for tumor immunotherapy.

BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation–reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA–mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

Prefoldins (PFDNs), a group of proteins known to be associated with cytoskeletal rearrangement, are involved in tumor progression in various cancer types. However, little is known about the roles of PFDNs in hepatocellular carcinoma (HCC). Herein, we investigated the transcriptional and survival data of PFDNs from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the potential functions of PFDN1/2/3/4. We also detected the expression of PFDN1/2/3/4 via immunohistochemistry (IHC), Western blotting, and real-time PCR in our clinical samples. We found that the PFDN family showed elevated expression in HCC tissues, while only PFDN1/2/3/4 were found to be significantly correlated with poor prognosis of patients with HCC in the TCGA database. Further investigation was associated with PFDN1–4. We found that the expression of PFDN1/2/3/4 was significantly associated with advanced clinicopathologic features. Apart from the TCGA database, IHC, real-time PCR, and immunoblotting identified the overexpression of PFDN1/2/3/4 in HCC tissues and HCC cell lines. Taken together, these results indicated that PFDN1/2/3/4 might be novel prognostic biomarkers and treatment targets for patients with HCC.

BackgroundHepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.MethodsThe bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.ResultsWe ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.ConclusionThe present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.

Current approaches to stage chronic liver diseases have limited utility to directly predict liver cancer risk. Here, we employed single nucleus RNA sequencing (snRNA-seq) to characterize the cellular microenvironment of healthy and chronically injured pre-malignant livers using two distinct mouse models. Analysis of 40,748 hepatic nuclei unraveled a previously uncharacterized disease-associated hepatocyte transcriptional state (daHep). These cells were absent in healthy livers, but were increasingly prevalent as chronic liver disease progressed towards hepatocarcinogenesis. Ultra-low-pass whole genome sequencing (LP-WGS) followed by copy number variation (CNV) analysis of microdissected mouse tissue demonstrated that daHep enriched regions are riddled with structural variants, suggesting these cells represent a pre-malignant intermediary. Integrated analysis of three recent human snRNA-seq datasets confirmed the presence of a similar phenotype in human chronic liver disease and further supported its enhanced mutational burden compared to normal hepatocytes. Gene expression deconvolution of 1,439 human liver transcriptomes from publicly available datasets revealed that daHep frequencies highly correlate with current histopathological liver disease staging systems. Importantly, we show that high daHep levels precede carcinogenesis in mice and humans and predict a higher risk of hepatocellular carcinoma (HCC) development. This novel transcriptional signature with diagnostic and, more importantly, prognostic significance has the potential to change the way chronic liver disease patients are staged, surveilled and risk-stratified.

Data Set Name: Hepatocellular Carcinoma Dataset (HCC dataset)

Abstract: Hepatocellular Carcinoma dataset (HCC dataset) was collected at a University Hospital in Portugal. It contains real clinical data of 165 patients diagnosed with HCC.

Donors: Miriam Seoane Santos (miriams@student.dei.uc.pt) and Pedro Henriques Abreu (pha@dei.uc.pt), Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra Armando Carvalho (aspcarvalho@gmail.com) and Adélia Simão (adeliasimao@gmail.com), Internal Medicine Service, Hospital and University Centre of Coimbra

Data Type: Multivariate Task: Classification, Regression, Clustering, Casual Discovery Attribute Type: Categorical, Integer and Real

Area: Life Sciences Format Type: Matrix Missing values: Yes

Instances and Attributes: Number of Instances (records in your data set): 165 Number of attributes (fields within each record): 49

Relevant Information: HCC dataset was obtained at a University Hospital in Portugal and contais several demographic, risk factors, laboratory and overall survival features of 165 real patients diagnosed with HCC. The dataset contains 49 features selected according to the EASL-EORTC (European Association for the Study of the Liver - European Organisation for Research and Treatment of Cancer) Clinical Practice Guidelines, which are the current state-of-the-art on the management of HCC.

This is an heterogeneous dataset, with 23 quantitative variables, and 26 qualitative variables. Overall, missing data represents 10.22% of the whole dataset and only eight patients have complete information in all fields (4.85%). The target variables is the survival at 1 year, and was encoded as a binary variable: 0 (dies) and 1 (lives). A certain degree of class-imbalance is also present (63 cases labeled as “dies” and 102 as “lives”).

A detailed description of the HCC dataset (feature’s type/scale, range, mean/mode and missing data percentages) is provided in Santos et al. “A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients”, Journal of biomedical informatics, 58, 49-59, 2015.

Time Series Clinical Data for patients with BCLC stage B Hepatocellular carcinomaThis database contain three independent electronic forms, which includes the time-series clinical data and survival outcome of patient with BCLC stage B hepatocellular carcinoma, with an time interval of three months. The derivation cohort includes 979 patients, the internal validation cohort includes 627 patients, and the multicentric validation cohort includes 414 patients. All the categorical variables were annotated.Dryad_English_Raw.xlsxStrategy in Construction of Survival Path of HCC patients manuallyThis picture illustrate how we construct the survival path system of patients using time-series data manually.Network Frame for Survival Path.tiff

Protein-Protein, Genetic, and Chemical Interactions for Zhao Y (2020):Comprehensive analysis of ubiquitin-specific protease 1 reveals its importance in hepatocellular carcinoma. curated by BioGRID (https://thebiogrid.org); ABSTRACT: In this study, we comprehensively analysed the role of ubiquitin-specific protease 1(USP1) in hepatocellular carcinoma (HCC) using data from a set of public databases.We analysed the mRNA expression of USP1 in HCC and subgroups of HCC using Oncomine and UALCAN. Survival analysis of USP1 in HCC was conducted with the Kaplan-Meier Plotter database. The mutations of USP1 in HCC were analysed using cBioPortal and the Catalogue of Somatic Mutations in Cancer database. Differential genes correlated with USP1 and WD repeat domain 48 (WDR48) were obtained using LinkedOmics. USP1 was knocked down with small interfering RNA (siRNA) or pharmacologically inhibited by ML-323 in MHCC97H or SK-Hep-1 cell lines for function analysis.High USP1 expression predicted unfavourable overall survival in HCC patients. USP1 showed positive correlations with the abundances of macrophages and neutrophils. We identified 98 differential genes that were positively correlated with both USP1 and WDR48. These genes were mainly involved in the cell cycle, aldosterone synthesis and secretion and oestrogen signalling pathways. Interactions between USP1 and WDR 48 were confirmed using co-immunoprecipitation. USP1 knockdown or ML-323 treatment reduced the expression of proliferating cell nuclear antigen (PCNA), cyclin D1 and cyclin E1.Overall, USP1 is a promising target for HCC treatment with good prognostic value. USP1 and WDR48 function together in regulating cancer cell proliferation via the cell cycle.

Introduction:

Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TiME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity.

Methods:

Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human fresh frozen HCC samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 15 HCC patient samples (as well as one spleen and one lymph node specimen from anonymous deceased donors for validation purposes) using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms. This extremely high-dimensional dataset was generated to allow data-driven investigation in patho-physiological immune cell interactions in the context of HCC. Clinical metadata including TMN stage, sex, ethnicity, pretreatment, and histopathological reports are available for all patient samples.

Results:

Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC. In situ phenotyping of 4,500,000 single cells (including 1,500,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers and spatial features of each individual cell. CODEX imaging revealed detailed composition of the immune cell niche in human liver cancer tissue allowing for further distinct spatial Beyond that, whole slide imaging allowed for the identification of the tumor-to-liver interface as a unique site of immune cell inhibition.

Discussion:

Here, we demonstrate that spatially resolved, single-cell analysis of human liver cancer tissue allows for the in-depth characterization of the immune cell composition of HCC. This tool can be used for biomarker research, to determine cellular functional states in intact tissue and to spatially and functionally quantify interactions between immune cells in the context of hepatocarcinogenesis. We expect that making this dataset publicly available will stimulate broad research endeavors into the immune tumor microenvironment of HCC and allow computational scientists to discover new biomarkers and features. Further details on the study can be obtained in our paper.

Glossary:

The files are labeled according to the following system:

• Example: reg001_cyc002_ch002_CD56
• reg001: multiple regions can be imaged in one tissue. For this experiment all tissues were images as one region: reg001.
• Cycle: CODEX imaging is done in cycles: this indicates which cycle the signal was detected in.
• Channel: the tissue was imaged on four channels: this indicates what channel the particular fluorophore-conjugated oligonucleotide signal was imaged at. Ch001: DAPI (DAPI filter), Ch002: AF488 (FITC filter), Ch003: Atto 550 (CY3 filter) and Ch004: AF647 (CY5 filter).
• The last part of the file extension corresponds to the antigen name: e.g. CD56

Open multimodal dataset of hepatocellular carcinoma (HCC), which includes both image data (contrast-enhanced CT and MRI images) and tabular data (will be uploaded later).

Please cite our data paper published in "Data in Brief": https://www.sciencedirect.com/science/article/pii/S2352340923007473

BackgroundLiver cancer ranks as the third leading cause of cancer-related mortality worldwide [1] and alarmingly, both the incidence and mortality rates of liver cancer are increasing [2; 3]. Among the various types of primary liver cancer, hepatocellular carcinoma (HCC) stands out as the most prevalent, accounting for approximately 70-85% of liver cancer cases [4]. Leveraging the advantages of magnetic resonance (MR) imaging, HCC can be reliably detected and diagnosed without the requirement of an invasive biopsy [5]. MR imaging offers high tissue contrast, which can be further enhanced through contrast-enhanced multiphasic magnetic resonance imaging (mpMRI) techniques. This enables accurate identification and non-invasive diagnosis of HCC [6].

ObjectivePrecise segmentation of the liver plays a crucial role in volumetry assessment and serves as a vital pre-processing step for subsequent tumor detection algorithms [7]. However, accurate liver segmentation can be particularly challenging in patients with cancer-related tissue alterations and deformations in shape [8]. Accurate HCC tumor segmentation is essential for the extraction of quantitative imaging biomarkers such as radiomics and can be used for studies on treatment response assessment and prognosis evaluation and provides critical information about the tumor biology. In order to enhance the reproducibility of liver and tumor segmentation, automated methods utilizing image analysis techniques and machine learning have been developed. These methods have demonstrated promising results [7; 8]; however, most algorithms were tested only on small internal test sets and therefore do not guarantee generalizable and consistent performance on external data. Publicly available datasets allow for fair and objective comparisons between different algorithms, techniques, or approaches. Researchers can evaluate the strengths and weaknesses of their methods in relation to existing solutions and establish benchmarks for performance evaluation. In addition to providing a benchmark with this dataset, we also assess the inter-rater variability between two different sets of tumor segmentations. This analysis serves as a measure of reproducibility for human segmentations, highlighting the consistency or variability that may exist among different human raters. Understanding the reproducibility of human segmentations is essential in assessing the reliability of manual annotations and establishing a baseline for algorithm performance comparison. By introducing LiverHccSeg, we aim to fill the gap of lacking publicly available mpMRI HCC datasets and offer researchers and developers a valuable resource for algorithmic evaluation on external data and imaging biomarker analyzes.

Materials and Methods Inclusion of PatientsAll available scans from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436) were downloaded [9]. One multiphasic MRI scan (pre and triphasic post contrast) per patient was included. Patients who did not exhibit a tumor or residual tumor were excluded from the tumor segmentation dataset; however, they were included in the liver segmentation dataset.

MR Imaging DataSubsequently, all imaging data was converted to the Neuroimaging Informatics Technology Initiative (NIfTI) format with the dcm2nii (v2.1.53) package [10] and available header information was extracted using the pydicom (v.2.1.2) package [11]. Multiparametric MR sequences were labeled with a consistent syntax ('pre', 'art', 'pv', 'del', for the pre-contrast, arterial, portal-venous and delayed contrast phases, respectively). All images were already de-identified by the TCIA website. Images were acquired between the years 1993 and 2007 on Philips and Siemens scanners with field strengths of 1.5 and 3 Tesla. Full details of the imaging parameters can be found in Table 5. Briefly, the median repetition time (TR) and median echo time (TE) were 365.8 ms and 26.4 ms, respectively. The median slice thickness was 9.5 mm, the median bandwidth 536.9 Hz.

Scientific ReadingAfter conversion, all images were read in a scientific reading by two board-certified abdominal radiologists (S.A. and S.H with 9 and 10 years of experience, respectively). Any disagreement between the two raters was discussed in a consensus meeting. All HCC lesions were classified according to LI-RADS criteria [6].

Image RegistrationThe co-registration of pre-contrast, portal-venous, and delayed-phase images with arterial phase images was performed using the software BioImage Suite (v3.5) [12]. A non-rigid intensity-based registration approach was applied, employing a parameterized free-form deformation (FFD) with 3D B-splines [13]. The optimal FFD transformation was estimated by maximizing the normalized mutual information similarity metric [14] through gradient descent optimization. To enhance the optimization process, a multi-resolution image pyramid with three levels was utilized. The final B-spline control point spacing was set to 80 mm. The estimated transformation was then employed to warp the moving images (pre-contrast, portal-venous, and delayed-phase) into the reference image space, specifically the arterial phase image.

Liver and Tumor Segmentation and Statistical AnalysisAll livers and tumors were manually segmented under the supervision of two board-certified abdominal radiologists using the software 3D Slicer (v4.10.2) [15]. To compare the segmentation agreement between the two sets of liver and tumor segmentations, we calculated segmentation metrics using the Python package seg-metrics (v1.0.0) [16]. All segmentation metrics and statistics were calculated in Python (v3.7).

Data descriptionThe data that appears in this article include:

dicoms.zip: This zip file contains all the raw MR images from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) [1] in the Digital Imaging and Communications in Medicine (DICOM) format used for the curation of this dataset. The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique unidentified patient ID, DATE is the date of the image acquisition, and SEQUENCE is the name of the MR sequence. LiverHccSeg_MetaData.xlsx: This spreadsheet contains all the metadata from the DICOM headers along with the data from the scientific image readings. nifti_and_segms.zip: This zip file contains all MR images along with the liver and tumor segmentations in the Neuroimaging Informatics Technology Initiative (NIfTI) format.The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique anonymized patient identifier, DATE is the date of the image acquisition, and SEQUENCE is the name of the MRI sequence or segmentation image.The NIfTI files are named as follows:pre.nii.gz : Pre-contrast T1-weighted MRIart.nii.gz: Arterial-phase T1-weighted MRIpv.nii.gz: Portal-venous-phase T1-weighted MRIdel.nii.gz: Delayed-phase T1-weighted MRIart_pre.nii.gz: Pre-contrast T1-weighted MRI registered to the corresponding arterial-phase T1-weighted imageart_pv.nii.gz: Portal-venous-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRIart_del.nii.gz: Delayed-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRIThe corresponding manual segmentations are named after the rater and the type of segmentation and follow the format 'RATER_ROI.nii.gz' where RATER denotes the human rater and ROI denotes the region of interest that was segmented, for example, 'rater1_liver.nii.gz', 'rater2_liver.nii.gz', 'rater1_tumor1.nii.gz', and 'rater2_tumor1.nii.gz'. For tumor segmentations, an integer indicates the tumor identification number for different tumor ROIs, for example, 'rater1_tumor1.nii.gz' and 'rater2_tumor1.nii.gz'. The segmentations can be used for the arterial phase NIfTI file as well as the corresponding co-registered pre-contrast (art_pre.nii.gz), portal-venous (art_pv.nii.gz), and delayed-phase (art_del.nii.gz) images. segm_metrics.xlsx: This spreadsheet summarizes the segmentation agreement between the two sets of liver and tumor segmentations by the two board-certified abdominal radiologists.

References 1 Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-249 2 Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7-34 3 White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB (2017) Incidence of Hepatocellular Carcinoma in All 50 United States, From 2000 Through 2012. Gastroenterology 152:812-820.e815 4 Perz JF, Armstrong GL, Farrington LA, Hutin YJ, Bell BP (2006) The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide. J Hepatol 45:529-538 5 Hamer OW, Schlottmann K, Sirlin CB, Feuerbach S (2007) Technology insight: advances in liver imaging. Nat Clin Pract Gastroenterol Hepatol 4:215-228 6 Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 289:816-830 7 Bousabarah K, Letzen B, Tefera J et al (2020) Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol. 10.1007/s00261-020-02604-5 8 Gross M, Spektor M, Jaffe A et al (2021) Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 16:e0260630 9 Erickson BJ, Kirk S, Lee Y et al (2016) Radiology Data from The Cancer Genome Atlas