Prediction of prognostic biomarkers for hepatocellular carcinoma and immune microenvironment infiltration based on single-cell sequencing and RNA-Seq integration

ALYREF associated with immune infiltration is a prognostic biomarker in hepatocellular carcinoma

AUNIP, a novel prognostic biomarker, has been shown to be associated with stromal and immune scores in oral squamous cell carcinoma (OSCC). Nonetheless, its role in other cancer types was unclear. In this study, AUNIP expression was increased in hepatocellular carcinoma (HCC) and lung adenocarcinoma (LUAD) according to data from The Cancer Genome Atlas (TCGA) database, Integrative Molecular Database of Hepatocellular Carcinoma (HCCDB), and Gene Expression Omnibus (GEO) database (GSE45436, GSE102079, GSE10072, GSE31210, and GSE43458). Further, according to copy number variation analysis, AUNIP up-regulation may be associated with copy number variation. Immunohistochemistry showed AUNIP expression was higher in HCC and LUAD compared with the normal tissues. Receiver operating characteristic (ROC) curve analysis demonstrated that AUNIP is a candidate diagnostic biomarker for HCC and LUAD. Next, TCGA, International Cancer Genome Consortium (ICGC), and GEO (GSE31210 and GSE50081) data showed that increased AUNIP expression clearly predicted poor overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in HCC and LUAD. Additionally, multivariate Cox regression analysis involving various clinical factors showed that AUNIP is an independent prognostic biomarker for HCC and LUAD. Next, the role of AUNIP in HCC and LUAD was explored via a co-expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and a gene set variation analysis (GSVA). HCC and LUAD exhibited almost identical enrichment results. More specifically, high AUNIP expression was associated with DNA replication, cell cycle, oocyte meiosis, homologous recombination, mismatch repair, the p53 signal transduction pathway, and progesterone-mediated oocyte maturation. Lastly, the Tumor Immune Estimation Resource (TIMER) tool was used to determine the correlations of AUNIP expression with tumor immune infiltration. AUNIP expression was positively correlated with the infiltration degree of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells in HCC. However, AUNIP expression was negatively correlated with the infiltration degree of B cells, CD4+ T cells, and macrophages in LUAD. In addition, AUNIP expression was correlated with immune infiltration in various other tumors. In conclusion, AUNIP, which is associated with tumor immune infiltration, is a candidate diagnostic and prognostic biomarker for HCC and LUAD.

Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. E3 ubiquitin-protein ligases play essential roles in HCC, such as regulating progression, migration, and metastasis. We aimed to explore a hub E3 ubiquitin-protein ligase gene and verify its association with prognosis and immune cell infiltration in HCC. Cell division cycle 20 (CDC20) was identified as a hub E3 ubiquitin-protein ligase in HCC by determining the intersecting genes in a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) using HCC data from the International Cancer Genome Consortium (ICGC) and the gene list of 919 E3 ubiquitin-protein ligases. DEGs and their correlations with clinicopathological features were explored in The Cancer Genome Atlas (TCGA), ICGC, and Gene Expression Omnibus (GEO) databases via the Wilcoxon signed-rank test. The prognostic value of CDC20 was illustrated by Kaplan-Meier (K-M) curves and Cox regression analyses. Subsequently, the correlation between CDC20 and immune infiltration was demonstrated via the Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). CDC20 expression was significantly higher in HCC than in normal tissues (all P < 0.05). High CDC20 expression predicted a poor prognosis and might be an independent risk factor in HCC (P < 0.05). Additionally, CDC20 was correlated with the immune infiltration of CD8 + T cells, T cells (general), monocytes, and exhausted T cells. This study reveals the potential prognostic value of CDC20 in HCC and demonstrates that CDC20 may be an immune-associated therapeutic target in HCC because of its correlation with immune infiltration.

In recent in vitro experiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreover, the presence of cortisol deregulated the normal levels of the pro- and anti-inflammatory cytokines IFN-γ and IL-10. We present an individual-based model to explore the interaction dynamics between tumour and immune cells under psychological stress conditions. With our model, we explore the processes underlying the emergence of different levels of immune infiltration, with particular focus on the biological mechanisms regulated by IFN-γ and IL-10. The set-up of numerical simulations is defined to mimic the scenarios considered in the experimental study. Similarly to the experimental quantitative analysis, we compute a score that quantifies the level of immune cell infiltration into the tumour. The results of numerical simulations indicate that the motility of immune cells, their capability to infiltrate through tumour cells, their growth rate and the interplay between these cell parameters can affect the level of immune cell infiltration in different ways. Ultimately, numerical simulations of this model support a deeper understanding of the impact of biological stress-induced mechanisms on immune infiltration.

The poliovirus receptor (PVR) is a member of the immunoglobulin superfamily (Ig SF) and is essential for the promotion of cancer cell proliferation and invasion. However, the correlation between PVR expression and prognosis as well as immune infiltration in hepatocellular carcinoma (HCC) remains unclear. The expression level of PVR was quantified using the Tumor and Tumor Immunity Evaluation Resource (TIMER) and Sangerbox. The Gene Expression Omnibus (GEO) database was used to validate the PVR expression. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of using PVR as a differentiating factor according to the area under curve (AUC) score. A PVR binding protein network was built using the STRING tool. An enrichment analysis using the R package clusterProfiler was used to explore the potential function of PVR. Immune infiltration analysis was calculated with ESTIMATE algorithms. We also assessed the correlation between PVR expression and immune infiltration by the single-sample Gene Set Enrichment Analysis (ssGSEA) method from the R package GSVA and TIMER database. The results showed that PVR was commonly overexpressed in multiple types of tumors including HCC. The data of GSE64041 confirmed the same result. The ROC curve suggested that PVR could be a potential diagnostic biomarker. Additionally, high mRNA expression of PVR in HCC was significantly correlated with poor overall survival (OS) and relapse free survival (RFS). Results also indicated correlations between PVR mRNA expression with the level of infiltration immune cells including B cells, CD8+ T cells, cytotoxic cells, DCs, CD56dim NK cells, pDCs, and Th2 cells. Furthermore, the PVR level was significantly correlated with immune markers for immunosuppressive cells in HCC. In conclusion, PVR might be an important regulator of tumor immune cell infiltration and a valuable prognostic biomarker in HCC. However, additional work is needed to fully elucidate the underlying mechanisms.

Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC. Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes. HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified. This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

BackgroundC-type lectin domain family 1 member B (CLEC1B, encoding the CLEC-2 protein), a member of the C-type lectin superfamily, is a type II transmembrane receptor involved in platelet activation, angiogenesis, and immune and inflammatory responses. However, data regarding its function and clinical prognostic value in hepatocellular carcinoma (HCC) remain scarce.MethodsThe expression of CLEC1B was explored using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RT-qPCR, western blot, and immunohistochemistry assays were employed to validate the downregulation of CLEC1B. Univariate Cox regression and survival analyses were used to evaluate the prognostic value of CLEC1B. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential association between cancer hallmarks and CLEC1B expression. The TISIDB database was applied to search for the correlation between immune cell infiltration levels and CLEC1B expression. The association between CLEC1B and immunomodulators was conducted by Spearman correlation analysis based on the Sangerbox platform. Annexin V-FITC/PI apoptosis kit was used for the detection of cell apoptosis.ResultsThe expression of CLEC1B was low in various tumors and exhibited a promising clinical prognostic value for HCC patients. The expression level of CLEC1B was tightly associated with the infiltration of various immune cells in the HCC tumor microenvironment (TME) and positively correlated with a bulk of immunomodulators. In addition, CLEC1B and its related genes or interacting proteins are implicated in multiple immune-related processes and signaling pathways. Moreover, overexpression of CLEC1B significantly influenced the treatment effects of sorafenib on HCC cells.ConclusionsOur results reveal that CLEC1B could serve as a potential prognostic biomarker and may be a novel immunoregulator for HCC. However, its function in immune regulation should be further explored.

ObjectivesThe composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an important guide to clinically significant treatments for cancer, such as immune checkpoint inhibition therapy and targeted therapy. We used multiple machine learning (ML) models to predict differences in immune infiltration in clear cell renal cell carcinoma (ccRCC), with computed tomography (CT) imaging pictures serving as a model for machine learning. We also statistically analysed and compared the results of multiple typing models and explored an excellent non-invasive and convenient method for treatment of ccRCC patients and explored a better, non-invasive and convenient prediction method for ccRCC patients.MethodsThe study included 539 ccRCC samples with clinicopathological information and associated genetic information from The Cancer Genome Atlas (TCGA) database. The Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to obtain the immune cell infiltration results as well as the cluster analysis results. ssGSEA-based analysis was used to obtain the immune cell infiltration levels, and the Boruta algorithm was further used to downscale the obtained positive/negative gene sets to obtain the immune infiltration level groupings. Multifactor Cox regression analysis was used to calculate the immunotherapy response of subgroups according to Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and subgraph algorithm to detect the difference in survival time and immunotherapy response of ccRCC patients with immune infiltration. Radiomics features were screened using LASSO analysis. Eight ML algorithms were selected for diagnostic analysis of the test set. Receiver operating characteristic (ROC) curve was used to evaluate the performance of the model. Draw decision curve analysis (DCA) to evaluate the clinical personalized medical value of the predictive model.ResultsThe high/low subtypes of immune infiltration levels obtained by optimisation based on the Boruta algorithm were statistically different in the survival analysis of ccRCC patients. Multifactorial immune infiltration level combined with clinical factors better predicted survival of ccRCC patients, and ccRCC with high immune infiltration may benefit more from anti-PD-1 therapy. Among the eight machine learning models, ExtraTrees had the highest test and training set ROC AUCs of 1.000 and 0.753; in the test set, LR and LightGBM had the highest sensitivity of 0.615; LR, SVM, ExtraTrees, LightGBM and MLP had higher specificities of 0.789, 1.000, 0.842, 0.789 and 0.789, respectively; and LR, ExtraTrees and LightGBM had the highest accuracy of 0. 719, 0.688 and 0.719 respectively. Therefore, the CT-based ML achieved good predictive results in predicting immune infiltration in ccRCC, with the ExtraTrees machine learning algorithm being optimal.ConclusionThe use of radiomics model based on renal CT images can be noninvasively used to predict the immune infiltration level of ccRCC as well as combined with clinical information to create columnar plots predicting total survival in people with ccRCC and to predict responsiveness to ICI therapy, findings that may be useful in stratifying the prognosis of patients with ccRCC and guiding clinical practitioners to develop individualized regimens in the treatment of their patients.

BACKGROUNDMetabolic reprogramming has been identified as a core hallmark of cancer. Solute carrier family 2 is a major glucose carrier family. It consists of 14 members, and we mainly study solute carrier family 2 member 1 (SLC2A1) and solute carrier family 2 member 2 (SLC2A2) here. SLC2A1, mainly existing in human erythrocytes, brain endothelial cells, and normal placenta, was found to be increased in hepatocellular carcinoma (HCC), while SLC2A2, the major transporter of the normal liver, was decreased in HCC.AIMTo identify if SLC2A1 and SLC2A2 were associated with immune infiltration in addition to participating in the metabolic reprogramming in HCC.METHODSThe expression levels of SLC2A1 and SLC2A2 were tested in HepG2 cells, HepG215 cells, and multiple databases. The clinical characteristics and survival data of SLC2A1 and SLC2A2 were examined by multiple databases. The correlation between SLC2A1 and SLC2A2 was analyzed by multiple databases. The functions and pathways in which SLC2A1, SLC2A2, and frequently altered neighbor genes were involved were discussed in String. Immune infiltration levels and immune marker genes associated with SLC2A1 and SLC2A2 were discussed from multiple databases.RESULTSThe expression level of SLC2A1 was up-regulated, but the expression level of SLC2A2 was down-regulated in HepG2 cells, HepG215 cells, and liver cancer patients. The expression levels of SLC2A1 and SLC2A2 were related to tumor volume, grade, and stage in HCC. Interestingly, the expression levels of SLC2A1 and SLC2A2 were negatively correlated. Further, high SLC2A1 expression and low SLC2A2 expression were linked to poor overall survival and relapse-free survival. SLC2A1, SLC2A2, and frequently altered neighbor genes played a major role in the occurrence and development of tumors. Notably, SLC2A1 was positively correlated with tumor immune infiltration, while SLC2A2 was negatively correlated with tumor immune infiltration. Particularly, SLC2A2 methylation was positively correlated with lymphocytes.CONCLUSIONSLC2A1 and SLC2A2 are independent therapeutic targets for HCC, and they are quintessential marker molecules for predicting and regulating the number and status of immune cells in HCC.

Exosomal IGFALS as a prognostic biomarker in hepatocellular Carcinoma: Associations with immune infiltration and clinical outcomes.

Purpose: Our study is designed to develop and certify a promising prognostic signature for hepatocellular carcinoma (HCC).Materials and methods: We retrospectively analyzed mRNA expression profiles and clinicopathological data fetched from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. We formulated a prognostic seven-gene signature composed of differentially expressed mRNAs (DEmRNAs) between HCC and nonneoplastic tissues through univariate Cox regression analysis. The receiver operating characteristic (ROC) curve, survival analysis and multivariate Cox regression analysis as well as nomograms were utilized to assess the prognostic performance of the seven-gene signature.Results: The risk score based on a seven-gene signature categorized HCC subjects into a high- and low-risk group. There was significantly discrepant overall survival (OS) between patients in both groups and the corresponding ROC curve revealed a satisfactory predictive performance in HCC survival in both TCGA and GSE76427 cohort. Multivariate Cox regression analysis demonstrated that a seven-gene signature was an independently prognostic factor for HCC. Nomograms combining this prognostic signature with significant clinical characteristics conferred a crucial reference to predict the 1-,3- and 5 years OS.Conclusions: Our study defined a promising seven-gene signature and nomogram model to forecast the OS of HCC patients, which is instrumental in clinical decision and personalized therapy.

Identification of SLITRK6 as a Novel Biomarker in hepatocellular carcinoma by comprehensive bioinformatic analysis

This study aims to investigate the prognostic significance and potential biological functions of the MTORC1 signaling pathway-associated gene POLR3G in Hepatocellular carcinoma (HCC). A prognostic risk model for HCC was developed by integrating HCC-related datasets and associated clinical data obtained from The Cancer Genome Atlas (TCGA) database. The GSVA website was employed to analyze the model genes across pan-cancer datasets, focusing on copy number variations (CNV), single nucleotide variations (SNV), methylation differences, drug sensitivity and immune cell infiltration profiles. Subsequently, we examined the expression levels and prognostic significance of POLR3G in HCC. Utilizing Spearman correlation analysis, we identified genes associated with POLR3G. Furthermore, Gene Set Enrichment Analysis (GSEA) was employed to elucidate the potential signaling pathways in which POLR3G may be involved. The relationship between POLR3G expression and immune cell abundance in HCC samples was assessed using the ssGSEA algorithm. Finally, the impact of POLR3G on HCC cell proliferation was validated through CCK-8 and EDU cell proliferation assays. Through univariate Cox regression analysis and LASSO regression analysis, we established a prognostic risk model for HCC comprising 13 genes. The analysis revealed that individuals categorized in the low-risk group had a markedly improved overall survival probability relative to those in the high-risk group. POLR3G exhibited a markedly elevated expression in HCC tissues when compared to adjacent normal tissues. The expression of POLR3G was correlated with tumor grade, and elevated POLR3G expression was associated with poor prognosis in HCC patients. Furthermore, the expression level of POLR3G was found to be correlated with the level of immune cell infiltration. Knockdown of POLR3G significantly inhibited the proliferative capacity of hepatocellular carcinoma cells. The findings suggest that POLR3G may serve as a potential biomarker influencing the prognosis of hepatocellular carcinoma patients by modulating the tumor immune microenvironment.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10238-025-01887-6.

Background : Hepatocellular carcinoma (HCC) is one of the most threatening malignancies because of the limited availability of radical therapeutic options. Thus, identification of prognostic biomarkers as well as molecular therapeutic targets of HCC should be very important for HCC patients. Fanconi anemia complementation group E (FANCE) is a DNA repair-related gene and it’s deletion is one of causes of Fanconi anemia. Recent studies reported that FANCD2 which is activated by FA complex involving FANCE shows high expression in HCC and expression of FANCD2 is in direct proportion to the grade of malignancy of HCC (Anticancer Res, 2017). And other studies reported that FANCE shows high expression in breast cancer (J Mol Biol, 2015). However clinical significance of FANCE expression in HCC is unknown. Objective : To clarify the clinical significance of FANCE expression in HCC. Material and method: Firstly, we assessed the relation between mRNA expression of FANCE and prognosis using large scale HCC gene data sets (The Cancer Genome Atlas; TCGA, Gene Expression Omnibus; GEO, and European Genome-phenome Archive; EGA). Secondly, the mRNA expression of FANCE was measured in 72 surgically resected HCC in our hospital during the period from 2000 to 2004 by RT-qPCR (normalized by internal control GAPDH), and we compared the expression of FANCE in between tumors and normal tissues. Thirdly, we assessed the associations between expression of FANCE and clinicopathological factors. Finally, we investigated the localization of FANCE by immunohistochemical staining data of THE HUMAN PROTEIN ATLAS. Result: In large scale HCC gene data sets and our samples, tumor tissues have higher mRNA expression of FANCE than normal tissues(t test. p&amp;lt;0.001). The high expression of FANCE was significantly associated with poor prognosis (Kaplan-Meier method, Log rank test. p&amp;lt;0.05). In the clinicopathological analysis, FANCE expression was not associated with any clinicopathological factors except age (p&amp;lt;0.05). THE HUMAN PROTEIN ATLAS showed that FANCE is expressed in the nuclei of HCC cells. Discussion: FANCE was overexpressed in HCC cells, and the high expression of FANCE was associated with poor prognosis. So high expression of FANCE could be a prognostic biomarker in HCC. Now we are performing knockdown experiment for FANCE to clarify the biological significance of FANCE expression in HCC.Conclusion: FANCE could be a prognostic biomarker in HCC. Citation Format: Junichi Takahashi, Takaaki Masuda, Yosuke Kuroda, Akihiro Kitagawa, Yushi Motomura, Kensuke Koike, Dai Shimizu, Shotaro Kuramitsu, Atsushi Fujii, Miwa Noda, Kuniaki Sato, Yusuke Tsuruda, Hajime Otsu, Hidetoshi Eguchi, Keishi Sugimachi, Masaki Mori, Koshi Mimori. Clinical significance of Fanconi anemia complementation group E(FANCE)DNA repair-related gene expression in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3173.

BackgroundThyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers.MethodsThree Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene FN1 using functional enrichment and protein-protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between FN1 and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing FN1 expression, using an in vitro cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between CD276 and FN1.ResultsFN1 expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4+ T cells, as well as with CD276 expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in FN1 expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with FN1 expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and the expression of CD276 in vitro. The correlation between FN1 and CD276 was further confirmed by immunohistochemical.ConclusionOur findings show that FN1 expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that FN1 expression be used as an immunity-related biomarker and therapeutic target in THCA.