Single-Cell RNA Sequencing Reveals the Role of Phosphorylation-Related Genes in Hepatocellular Carcinoma Stem Cells

Revolutionizing medicine: Recent developments and future prospects in stem-cell therapy

Stem-cell therapy is a revolutionary frontier in modern medicine, offering enormous capacity to transform the treatment landscape of numerous debilitating illnesses and injuries. This review examines the revolutionary frontier of treatments utilizing stem cells, highlighting the distinctive abilities of stem cells to undergo regeneration and specialized cell differentiation into a wide variety of phenotypes. This paper aims to guide researchers, physicians, and stakeholders through the intricate terrain of stem-cell therapy, examining the processes, applications, and challenges inherent in utilizing stem cells across diverse medical disciplines. The historical journey from foundational contributions in the late 19th and early 20th centuries to recent breakthroughs, including ESC isolation and iPSC discovery, has set the stage for monumental leaps in medical science. Stem cells' regenerative potential spans embryonic, adult, induced pluripotent, and perinatal stages, offering unprecedented therapeutic opportunities in cancer, neurodegenerative disorders, cardiovascular ailments, spinal cord injuries, diabetes, and tissue damage. However, difficulties, such as immunological rejection, tumorigenesis, and precise manipulation of stem-cell behavior, necessitate comprehensive exploration and innovative solutions. This manuscript summarizes recent biotechnological advancements, critical trial evaluations, and emerging technologies, providing a nuanced understanding of the triumphs, difficulties, and future trajectories in stem cell-based regenerative medicine. Future directions, including precision medicine integration, immune modulation strategies, advancements in gene-editing technologies, and bioengineering synergy, offer a roadmap in stem cell treatment. The focus on stem-cell therapy's potential highlights its significant influence on contemporary medicine and points to a future in which individualized regenerative therapies will alleviate various medical disorders.

Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications

Hepatocellular Carcinoma (HCC) is one of the most common types of primary liver cancer. Current treatment options have limited efficacy against this malignancy, primarily owing to difficulties in early detection and the inherent resistance to existing drugs. Tumor heterogeneity is a pivotal factor contributing significantly to treatment resistance and recurrent manifestations of HCC. Intratumoral heterogeneity is an important aspect of the spectrum of complex tumor heterogeneity and contributes to late diagnosis and treatment failure. Therefore, it is crucial to thoroughly understand the molecular mechanisms of how tumor heterogeneity develops. This review aims to summarize the possible molecular dimensions of tumor heterogeneity with an emphasis on intratumoral heterogeneity, evaluate its profound impact on the diagnosis and therapeutic strategies for HCC, and explore the suitability of appropriate pre-clinical models that can be used to best study tumor heterogeneity; thus, opening new avenues for cancer treatment.

Propofol regulates the progression of hepatocellular carcinoma via the POLR2L/TGF-β signaling pathway

Background

Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality. Propofol has been reported to modulate tumorigenesis in HCC; the aim of this study was to investigate the effect of the interaction of propofol with POLR2L on HCC tumor progression in HCC.

Methods

The propofol-related GSE101724 dataset was analyzed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) to identify overlapping genes. Key genes were selected from The Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC)-DEGs for prognostic analysis. The impact of POLR2L on LIHC patient survival was assessed, followed by in vitro experiments to validated its effects on HCC cell behavior and signaling pathways.

Results

Fourteen overlapping genes were identified in the turquoise module (highest correlation) of up-regulated DEGs and GSE101724. Further analysis obtained 11 key overlapping genes from 14 overlapping genes and TCGA-LIHC-DEGs, among which HSPE1 and POLR2L showed significant prognostic correlation. Patients with LIHC have a worse chance of surviving when their POLR2L expression is elevated. Knockdown POLR2L significantly inhibited the proliferation, invasion, and migration of HCC cell lines. Downregulation of POLR2L was accompanied by induced apoptosis, cell cycle arrest, and modulation of the expression of apoptosis-related genes. Propofol was found to downregulate POLR2L expression, inhibiting cell proliferation and growth. Further, it was shown that propofol controlled the development of HCC by influencing the POLR2L/TGF-β signaling loop.

Conclusions

The results validated the predictive relevance of POLR2L in HCC and emphasized that propofol can regulate HCC progression through the POLR2L/TGF-β signaling pathway.

The Biological Behavior and Clinical Application Prospects of Deoxythymidine Kinase Gene in Tumors

Malignant tumors have become a significant risk factor for human mortality. Although there have been notable advancements in the treatment of tumors, patient prognosis remains poor. In recent years, gene diagnosis and gene therapy have brought great benefits to patients. Deoxythymidine kinase (DTYMK) is a highly promising biomarker, has been studied by many scholars, and plays a crucial role in the occurrence and development of various types of cancer. The abnormal expression of DTYMK is involved in tumor occurrence and development, and may also serve as a biomarker for tumor diagnosis, treatment, and prognosis. Several experimental studies have shown that DTYMK can impact tumor progression by regulating mechanisms such as cell cycle, tumor microenvironment, immune infiltration, and signaling pathways. Therefore, this article focuses on clarifying the mechanism of DTYMK in tumors and exploring its clinical application value to help patients prolong their survival cycle and improve their quality of life.

DNA damage repair-related gene signature for identifying the immune status and predicting the prognosis of hepatocellular carcinoma

The heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.

Pan-cancer integrated bioinformatic analysis of RNA polymerase subunits reveal RNA Pol I member CD3EAP regulates cell growth by modulating autophagy

Transcription is a crucial stage in gene expression. An integrated study of 34 RNA polymerase subunits (RNAPS) in the six most frequent cancer types identified several genetic and epigenetic modification. We discovered nine mutant RNAPS with a mutation frequency of more than 1% in at least one tumor type. POLR2K and POLR2H were found to be amplified and overexpressed, whereas POLR3D was deleted and downregulated. Multiple RNAPS were also observed to be regulated by variations in promoter methylation. 5-Aza-2-deoxycytidine mediated re-expression in cell lines verified methylation-driven inhibition of POLR2F and POLR2L expression in BRCA and NSCLC, respectively. Next, we showed that CD3EAP, a Pol I subunit, was overexpressed in all cancer types and was associated with worst survival in breast, liver, lung, and prostate cancers. The knockdown studies showed that CD3EAP is required for cell proliferation and induces autophagy but not apoptosis. Furthermore, autophagy inhibition rescued the cell proliferation in CD3EAP knockdown cells. CD3EAP expression correlated with S and G2 phase cell cycle regulators, and CD3EAP knockdown inhibited the expression of S and G2 CDK/cyclins. We also identified POLR2D, an RNA pol II subunit, as a commonly overexpressed and prognostic gene in multiple cancers. POLR2D knockdown also decreased cell proliferation. POLR2D is related to the transcription of just a subset of RNA POL II transcribe genes, indicating a distinct role. Taken together, we have shown the genetic and epigenetic regulation of RNAPS genes in most common tumors. We have also demonstrated the cancer-specific function of CD3EAP and POLR2D genes.

Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making

Liver cancer, primarily HCC, exhibits highly heterogeneous histological and molecular aberrations across tumors and within individual tumor nodules. Such intertumor and intratumor heterogeneities may lead to diversity in the natural history of disease progression and various clinical disparities across the patients. Recently developed multimodality, single-cell, and spatial omics profiling technologies have enabled interrogation of the intertumor/intratumor heterogeneity in the cancer cells and the tumor immune microenvironment. These features may influence the natural history and efficacy of emerging therapies targeting novel molecular and immune pathways, some of which had been deemed undruggable. Thus, comprehensive characterization of the heterogeneities at various levels may facilitate the discovery of biomarkers that enable personalized and rational treatment decisions, and optimize treatment efficacy while minimizing the risk of adverse effects. Such companion biomarkers will also refine HCC treatment algorithms across disease stages for cost-effective patient management by optimizing the allocation of limited medical resources. Despite this promise, the complexity of the intertumor/intratumor heterogeneity and ever-expanding inventory of therapeutic agents and regimens have made clinical evaluation and translation of biomarkers increasingly challenging. To address this issue, novel clinical trial designs have been proposed and incorporated into recent studies. In this review, we discuss the latest findings in the molecular and immune landscape of HCC for their potential and utility as biomarkers, the framework of evaluation and clinical application of predictive/prognostic biomarkers, and ongoing biomarker-guided therapeutic clinical trials. These new developments may revolutionize patient care and substantially impact the still dismal HCC mortality.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

Background

Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.

Methods

The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.

Result

11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.

Conclusion

TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is currently one of the most life-threatening diseases worldwide. However, the factors, genes, and processes involved in the mechanisms of HCC initiation, development, and metastasis remain to be identified.Methods: WNT signalling pathways may play important roles in cancer initiation and progression. Thus, it would be informative to construct a WNT signature-based gene model for the prognosis of HCC and the prediction of therapeutic efficacy. We curated genomic profiles for HCC from The Cancer Genome Atlas (TCGA) and divided them into training and internal validation datasets. We also used samples from GSE14520 and HCCDB18 as validation datasets and clustered them by ConsensusClusterPlus analysis. We applied WebGestaltR to the WNT score-associated differentially expressed genes (DEGs) and conducted a signalling pathway enrichment analysis. We assessed the tumour immune microenvironment with ESTIMATE, Microenvironment Cell Populations (MCP)-counter, single-sample gene set enrichment analysis (ssGSEA), and tumour immune dysfunction and exclusion (TIDE).Results: We performed a least absolute shrinkage and selection operator (LASSO) regression analysis to identify the prognosis-related hub genes, identified the risk and protective factor genes associated with HCC, classified them into two clusters, and found that Cluster 2 had a significantly better prognosis than Cluster 1. Moreover, the latter had advanced clinical features compared with the former. Uridine-cytosine kinase 1 (UCK1), myristoylated alanine-rich C-kinase substrate-like protein 1 (MARCKSL1), P-antigen family member 1 (PAGE1), and killer cell lectin-like receptor B1 (KLRB1) were detected and used to construct a simplified prognostic model for HCC. The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively.Conclusions: The WNT score-related gene-based model designed and evaluated herein had strong prognostic and predictive ability for HCC and could, therefore, facilitate decision-making in the prognosis and therapeutic efficacy assessment of HCC.

Rapid Determination of 9 Tyrosine Kinase Inhibitors for the Treatment of Hepatocellular Carcinoma in Human Plasma by QuEChERS-UPLC-MS/MS

A reliable and rapid method employing QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) pretreatment coupled with ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) was successfully developed and validated for the analysis of nine tyrosine kinase inhibitors (TKIs) in human plasma. Biological samples were extracted with acetonitrile and salted out with 350 mg of anhydrous magnesium sulfate (MgSO₄), followed by purification with 40 mg of ethyl enediamine-N-propylsilane (PSA) adsorbents. All analytes and internal standards (IS) were separated on the Hypersil GOLD VANQUISH C18 (2.1 mm × 100 mm, 1.9 μM) column using the mobile phases composed of acetonitrile (phase A) and 0.1% formic acid in water (phase B) for 8.0 min. Detection was performed by selection reaction monitoring (SRM) in the positive ion electrospray mode. Lenvatinib, sorafenib, cabozantinib, apatinib, gefitinib, regorafenib, and anlotinib rendered good linearity over the range of 0.1–10 ng/ml, and 1–100 ng/ml for tivantinib and galunisertib. All linear correlation coefficients for all standard curves were ≥ 0.9966. The limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.003 to 0.11 ng/ml and 0.01–0.37 ng/ml, respectively. The method was deemed satisfactory with an accuracy of -7.34–6.64%, selectivity, matrix effect (ME) of 90.48–107.77%, recovery, and stability. The proposed method is simple, efficient, reliable, and applicable for the detection of TKIs in human plasma samples as well as for providing a reference for the clinical adjustment of drug administration regimen by monitoring the drug concentrations in the plasma of patients.