Single-cell analysis reveals the landscape of tumor heterogeneity and identifies MLXIPL as a potential biomarker in tumor evolution trajectory of hepatocellular carcinoma
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ABSTRACT:
PROVIDER: PRJNA647863 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12289500.fastq.gz | Fastqsanger.gz | |||
| SRR12289600.fastq.gz | Fastqsanger.gz | |||
| SRR12289700.fastq.gz | Fastqsanger.gz | |||
| SRR12289800.fastq.gz | Fastqsanger.gz | |||
| SRR12289501.fastq.gz | Fastqsanger.gz |
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Project description:Hepatocellular carcinoma (HCC) is a globally prevailing cancer with a low five-year survival rate. Little is known about its intricate gene expression profile and tumor evolutionary traits. Single-cell RNA sequencing (scRNA-seq) is an indispensable tool to explore the genetic characteristics at the single-cell level. In this study, we profiled gene expression of single cells from human HCC tumor and para-tumor tissues using Smart-seq 2 sequencing method. Based on DEGs, we identified heterogeneous subclones in HCC tissues, including 4 HCC and 2 hepatocyte subclones. Then, we carried out hub-gene co-network and functional annotations analysis to further explain tumor heterogeneity. Next, we conducted the evolution trajectory by pseudo-time analysis and its regulated transcriptional factors (TF) gene network. In the following research, we found that MLXIPL, as a TF, was commonly upregulated in HCC tissues and cells at both the mRNA and protein levels, promoting cell proliferation and inhibiting its apoptosis. Mechanistically, MLXIPL activation is crucial for accelerating HCC cell glycolysis. Taken together, our work identified the heterogeneity of HCC cell subclones, and MLXIPL might be a promising therapeutic target of HCC.
2021-01-27 | GSE154906 | GEO
Project description:Hepatocellular adenomas (HCA) are rare benign tumours of the liver which have been extensively classified at the clinico-pathological, genetic and proteomic levels. The b-HCA subtype has several types of mutations in the ß-catenin gene (CTNNB1) with very different clinical phenotypes and risks. Recently, we revealed a glutamine synthetase (GS) positive rim in b-HCA exon 3 S45 and b-HCA exon 7/8, indicating protein expression heterogeneity into b-HCA in comparison with the rest of the tumor. A difference in vascularization had been associated with this rim revealed by a diffuse CD34 labeling only at the center of the tumor and not in the rim. In this study, we sought to characterize this tumor heterogeneity in a tumor considered monoclonal. We used laser microdissection to analyze the rim (R) at the molecular level. We cut 1mm² areas of GS+/CD34- rim on serial cuts and we analyzed in parallel, the mutation status of CTNNB1 and the levels of protein expression by mass spectrometry. Comparison of the proteomic profiles of the rim versus the tumor center (T) revealed a sufficiently large protein expression differential to distinguish them by principal component analysis. Forty proteins were significantly differentially expressed between R and T, including a large majority of amino acid and lipid metabolism proteins whose deregulations were comparable to a perivenous expression profile in a normal hepatocyte context. Our results suggest that protein expression in the rim seems not to be dependent on the mutational status of CTNNB1 but rather on the influence of venous drainage. Thanks to laser microdissection coupled with proteomic analysis on fixed tissue, we were able to analyze this tumor heterogeneity and provide elements of biological interpretation.
2023-05-02 | PXD031323 | Pride
Project description:The inter-patient variability of tumor proteomes has been investigated on a large scale but many tumors display also intra-tumoral heterogeneity (ITH) regarding morphological and genetic features. To what extent the local proteome of tumors intrinsically differs remains largely unknown. Here, we used hepatocellular carcinoma (HCC) as a model system, to quantify both inter- and intra-tumor heterogeneity across human patient specimens with spatial resolution. We first defined proteomic features that robustly distinguish neoplastic from the directly adjacent non-neoplastic tissue by integrating proteomic data from human patient samples and genetically defined mouse models with available gene expression data. We then demonstrated the existence of intra-tumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry based proteomics to analyze diagnostic tumor specimens with spatial resolution
2018-01-24 | PXD007052 | Pride
Project description:Hepatocellular carcinoma (HCC) is a globally prevailing cancer with a low 5-year survival rate. Little is known about its intricate gene expression profile. Single-cell RNA sequencing is an indispensable tool to explore the genetic characteristics of HCC at a more detailed level. In this study, we profiled the gene expression of single cells from human HCC tumor and para-tumor tissues using the Smart-seq 2 sequencing method. Based on differentially expressed genes, we identified heterogeneous subclones in HCC tissues, including five HCC and two hepatocyte subclones. We then carried out hub-gene co-network and functional annotations analysis followed pseudo-time analysis with regulated transcriptional factor co-networks to determine HCC cellular trajectory. We found that MLX interacting protein like (MLXIPL) was commonly upregulated in the single cells and tissues and associated with a poor survival rate in HCC. Mechanistically, MLXIPL activation is crucial for promoting cell proliferation and inhibits cell apoptosis by accelerating cell glycolysis. Taken together, our work identifies the heterogeneity of HCC subclones, and suggests MLXIPL might be a promising therapeutic target for HCC.
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Project description:Hepatocellular Carcinoma (HCC) biomarker discovery cohort for small RNA profiling
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Project description:The tumor heterogeneity between the primary and recurrent hepatocellular carcinoma (HCC) has rarely been explored. The present study aims to illustrate the heterogeneity between the primary and recurrent tumors by integrating multiomic analyses.
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Project description:The tumor heterogeneity between the primary and recurrent hepatocellular carcinoma (HCC) has rarely been explored. The present study aims to illustrate the heterogeneity between the primary and recurrent tumors by integrating multiomic analyses.
2021-03-18 | PXD024810 |
Project description:Saliva is rich in proteins, DNA, RNA and microorganisms, and can be regarded as a biomarker library. In order to explore a noninvasive and simple means of early screening for liver cancer, proteomics was used to screen salivary markers of hepatitis B associated liver cancer. We used mass spectrometry coupled isobaric tags for relative and absolute quantitation (iTRAQ)-technology to identify differentially expressed proteins (DEPs). Western blot, immunohistochemistry and enzyme linked immunosorbent assay were used to detect marker expression of in tissues and saliva. Statistical analysis was used to analyze the diagnostic efficacy of the markers was analyzed through statistical analyses. By comparing the hepatocellular carcinoma (HCC) group with non-HCC groups, we screened out 152 salivary DEPs. We found orosomucoid 1(ORM1) had significantly higher expression in saliva of HCC patients compared with non-HCC groups (p<0.001) and the expression of ORM1 in liver cancer tissues was significantly higher than that in adjacent normal tissues(p<0.001). The combination of salivary ORM1 and alpha-fetoprotein (AFP) showed reasonable specificities and sensitivities for detecting HCC. In a word, salivary ORM1 as a new biomarker of hepatitis B associated hepatocellular carcinoma, combination of salivary ORM1 and AFP as an improved diagnostic tool for hepatocellular carcinoma.
2022-07-13 | PXD033752 | Pride
Project description:BACKGROUND & AIMS: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. METHODS: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. RESULTS: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. CONCLUSIONS: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses. Center and peripheral portions of hepatocellular carcinoma tumors as well as surrounding non-tumor cirrhotic liver tissues were obtained from fresh frozen surgically resected tissues, and isolated total RNA samples were analyzed for genome-wide expression profiles.
2014-03-25 | E-GEOD-56140 | biostudies-arrayexpress
Project description:IntroductionMounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).MethodsA total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.ResultsAccording to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as "immune-hot", having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.ConclusionsOur study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management.
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