RNA-seq profiling reveals PBMC RNA as a potential biomarker for hepatocellular carcinoma

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma

BACKGROUND/AIMS

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.

METHODS

We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses.

RESULTS

Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types.

CONCLUSION

Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.

The Potential of the lncRNAs ADAMTSL4-AS1, AC067931 and SOCS2-AS1 in Peripheral Blood Mononuclear Cells as Novel Diagnostic Biomarkers for Hepatitis B Virus-Associated Hepatocellular Carcinoma

Purpose

Long noncoding RNAs (lncRNAs) might be closely associated with hepatocellular carcinoma (HCC) progression and could serve as diagnostic and prognostic markers. This study aimed to investigate lncRNA-based diagnostic biomarkers for hepatitis B virus (HBV)-associated HCC.

Materials and Methods

High-throughput transcriptome sequencing was conducted on the liver tissues of 15 patients with HBV-associated liver diseases (5 with chronic hepatitis B [CHB], 5 with liver cirrhosis [LC], and 5 with HCC). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze lncRNA expressions. Potential diagnostic performance for HBV-associated HCC screening was evaluated.

Results

Through trend analysis and functional analysis, we found that 8 lncRNAs were gradually upregulated and 1 lncRNA was progressively downregulated by regulation of target mRNAs and downstream HCC-associated signaling pathways. The validation of dysregulated lncRNAs in peripheral blood mononuclear cells (PBMCs) and HCC tissues by qRT-PCR revealed that ADAMTSL4-AS1, SOCS2-AS1, and AC067931 were significantly increased in HCC compared with CHB and cirrhosis. Moreover, differentially expressed lncRNAs were aberrantly elevated in Huh7, Hep3B, HepG2, and HepG2.215 cells compared with LX2 cells. Furthermore, ADAMTSL4-AS1, SOCS2-AS1, and AC067931 were identified as novel biomarkers for HBV-associated HCC. For distinguishing HCC from CHB, ADAMTSL4-AS1, AC067931, and SOCS2-AS1 combined with alpha-fetoprotein (AFP) had an area under the curve (AUC) of 0.945 (sensitivity, 83.9%; specificity, 89.8%). Similarly, for distinguishing HCC from LC, this combination had an AUC of 0.871 (sensitivity, 91.1%; specificity, 68.2%). Furthermore, this combination showed the highest diagnostic ability to distinguish HCC from CHB and LC (AUC, 0.905; sensitivity, 91.1%; specificity, 75.3%). In particular, this combination identified AFP-negative (AFP < 20 ng/mL) (AUC = 0.814), small (AUC = 0.909), and early stage (AUC = 0.863) tumors.

Conclusion

ADAMTSL4-AS1, SOCS2-AS1, and AC067931 combined with AFP in PBMCs may serve as a noninvasive diagnostic biomarker for HBV-associated HCC, especially AFP-negative, small, and early stage HCC.

The Impact of Blood Sample Processing on Ribonucleic Acid (RNA) Sequencing

In gene quantification and expression analysis, issues with sample selection and processing can be serious, as they can easily introduce irrelevant variables and lead to ambiguous results. This study aims to investigate the extent and mechanism of the impact of sample selection and processing on ribonucleic acid (RNA) sequencing. RNA from PBMCs and blood samples was investigated in this study. The integrity of this RNA was measured under different storage times. All the samples underwent high-throughput sequencing for comprehensive evaluation. The differentially expressed genes and their potential functions were analyzed after the samples were placed at room temperature for 0h, 4h and 8h, and different feature changes in these samples were also revealed. The sequencing results showed that the differences in gene expression were higher with an increased storage time, while the total number of genes detected did not change significantly. There were five genes showing gradient patterns over different storage times, all of which were protein-coding genes that had not been mentioned in previous studies. The effect of different storage times on seemingly the same samples was analyzed in this present study. This research, therefore, provides a theoretical basis for the long-term consideration of whether sample processing should be adequately addressed.

Novel PBMC LncRNA signatures as diagnostic biomarkers for colorectal cancer

The expression of long non-coding RNAs (LncRNAs) in peripheral blood mononuclear cell (PBMC) and its clinical relevance in colorectal cancer (CRC) remains largely uncharacterized. To address these gaps, we investigated the expression profiles of lncRNAs in PBMC from CRC and healthy controls (HC) by RNA sequencing. The expression level of differentially expressed lncRNAs (DElncRNAs) were evaluated by quantitative PCR in PBMC samples from CRC patients and HC. A total of 447 DElncRNAs were identified, with 178 elevated lncRNAs and 269 decreased lncRNAs in PBMC from CRC patients as compared with that from HC. RT-PCR results supported a significant elevation of NEAT1:11, lnc-PDZD8-1:5 and LINC00910:16 in 98 CRC patients and 82 HC. The clinical implication of NEAT1:11, lnc-PDZD8-1:5 and LINC00910:16 as CRC diagnostic biomarker were determined by receiver operating characteristic (ROC) curve, showing sensitivity 74.5% and specificity 84.5% for joint detection the three lncRNAs. Notably, NEAT1:11 was closely related with the size and extent of primary tumor, with higher relative expression of NEAT1:11 in higher T stage (P = 0.0047). Moreover, NEAT1:11 was related with grade (P = 0.012). Collectively, PBMC from patients with CRC show significantly variable expression profiles of lncRNAs, and detection of these differential expression lncRNAs may provide useful information for basic and clinical research.

F-box protein 43 promoter methylation as a novel biomarker for hepatitis B virus-associated hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) has a high prevalence and poor prognosis worldwide. Therefore, it is urgent to find effective and timely diagnostic markers. The objective of this study was to evaluate the diagnostic value of F-box protein 43 promoter methylation in peripheral blood mononuclear cells (PBMCs) for HCC.

Method

A total of 247 participants were included in this study, comprising individuals with 123 hepatitis B virus-associated HCC, 79 chronic hepatitis B, and 45 healthy controls. F-box protein 43 methylation and mRNA levels in PBMCs were detected by MethyLight and quantitative real-time PCR.

Result

F-box protein 43 promoter methylation levels were significantly lower in HCC PBMCs than the chronic hepatitis B (P < 0.001) and healthy control PBMCs (P < 0.001). Relative mRNA expression levels of F-box protein 43 in HCC PBMCs were significantly higher than those in chronic hepatitis B (P < 0.001) and healthy control PBMCs (P < 0.001). Receiver operating characteristic analysis of F-box protein 43 promoter methylation levels yielded an area under curve (AUC) of 0.793 with 76.42% sensitivity and 68.35% specificity when differentiating HCC from chronic hepatitis. These values for the F-box protein 43 promoter methylation level were superior to those of the alpha-fetoprotein serum (AFP) level (AUC: 0.780, sensitivity: 47.97%, and specificity: 96.20%), with increments in values for the combination of F-box protein 43 promoter methylation AFP levels (AUC: 0.888, sensitivity: 76.42%, and specificity: 86.08%).

Conclusion

Hypomethylation of the F-box protein 43 promoter in PBMCs is a promising biochemical marker for HBV-associated HCC.

RNA-Sequencing Analysis Identification of Potential Biomarkers for Diagnosis of Sarcopenia

Sarcopenia is a geriatric disease associated with increased mortality and disability. Early diagnosis and intervention are required to prevent it. This study investigated biomarkers for sarcopenia by using a combination of comprehensive clinical data and messenger RNA-sequencing (RNA-seq) analysis obtained from peripheral blood mononuclear cells. We enrolled a total of 114 older adults aged 66-94 years (52 sarcopenia diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus and 62 normal older people). We used clinical data which were not included diagnosis criteria of sarcopenia, and stride length showed significance by logistic regression analysis (Bonferroni corrected p = .012, odds ratio = 0.14, 95% confidence interval [CI]: 0.05-0.40). RNA-seq analysis detected 6 differential expressed genes (FAR1, GNL2, HERC5, MRPL47, NUBP2, and S100A11). We also performed gene-set enrichment analysis and detected 2 functional modules (ie, hub genes, MYH9, and FLNA). By using any combination of the 9 candidates and basic information (age and sex), risk-prediction models were constructed. The best model by using a combination of stride length, HERC5, S100A11, and FLNA, achieved a high area under the curve (AUC) of 0.91 in a validation cohort (95% CI: 0.78-0.95). The quantitative PCR results of the 3 genes were consistent with the trend observed in the RNA-seq results. When BMI was added, the model achieved a high AUC of 0.95 (95% CI: 0.84-0.99). We have discovered potential biomarkers for the diagnosis of sarcopenia. Further refinement may lead to their future practical use in clinical use.

FAT4 expression in peripheral blood mononuclear cells is associated with prognosis and immune cell infiltration in hepatocellular carcinoma

Peripheral blood mononuclear cell (PBMC) genes reflect the host immune status and could be suitable for evaluating the prognosis of patients with hepatocellular carcinoma (HCC), for which a reliable biomarker is unavailable and the host immune responses to cancer cells. This study aimed to investigate prognostically relevant genes in HCC PBMCs and assessed whether their expression represents tumor immune infiltration. Gene expression in PBMCs from patients with advanced or terminal HCC who had survived or died was examined. Correlations among FAT atypical cadherin 4 (FAT4) expression, cancer immune characteristics, and infiltrated immune cell gene marker sets were analyzed. FAT4 expression was lower in the PBMCs of patients with advanced or terminal HCC who had died than that in patients who survived. Kaplan-Meier analysis indicated that FAT4 downregulation was associated with a relatively poor prognosis while overexpression was positively correlated with immune cell infiltration, several immune cell markers, and immune checkpoint expression. Hsa-miR-93-5p represented the most probable upstream microRNA of FAT4. Thus, upregulated FAT4 in PBMCs and HCC tissues might indicate a favorable prognosis and increased immune cell infiltration, while miRNA-93-5p could be a modulator of FAT4 expression. Collectively, these findings suggest novel immunotherapy targets for HCC.

State of CD8+ T cells in progression from nonalcoholic steatohepatitis to hepatocellular carcinoma: From pathogenesis to immunotherapy

With the obesity epidemic, nonalcoholic steatohepatitis (NASH) is emerging as the fastest growing potential cause of hepatocellular carcinoma (HCC). NASH has been demonstrated to establish a tumor-prone liver microenvironment where both innate and adaptive immune systems are involved. As the most typical anti-tumor effector, the cell function of CD8+ T cells is remodeled by chronic inflammation, metabolic alteration, lipid toxicity and oxidative stress in the liver microenvironment along the NASH to HCC transition. Unexpectedly, NASH may blunt the effect of immune checkpoint inhibitor therapy against HCC due to the dysregulated CD8+ T cells. Growing evidence has supported that NASH is likely to facilitate the state transition of CD8+ T cells with changes in cell motility, effector function, metabolic reprogramming and gene transcription according to single-cell sequencing. However, the mechanistic insight of CD8+ T cell states in the NASH-driven HCC is not comprehensive. Herein, we focus on the characterization of state phenotypes of CD8+ T cells with both functional and metabolic signatures in NASH-driven fibrosis and HCC. The NASH-specific CD8+ T cells are speculated to mainly have a dualist effect, where its aberrant activated phenotype sustains chronic inflammation in NASH but subsequently triggers its exhaustion in HCC. As the exploration of CD8+ T cells on the distribution and phenotypic shifts will provide a new direction for the intervention strategies against HCC, we also discuss the implications for targeting different phenotypes of CD8+ T cells, shedding light on the personalized immunotherapy for NASH-driven HCC.

Transcriptomic identification of genes expressed in invasive S. aureus diabetic foot ulcer infection

Introduction

Infection in diabetic foot ulcers (DFUs) is one of the major complications associated with patients with diabetes. Staphylococcus aureus is the most common offending pathogen in patients with infected DFU. Previous studies have suggested the application of species-specific antibodies against S. aureus for diagnosis and monitoring treatment response. Early and accurate identification of the main pathogen is critical for management of DFU infection. Understanding the host immune response against species-specific infection may facilitate diagnosis and may suggest potential intervention options to promote healing infected DFUs. We sought to investigate evolving host transcriptome associated with surgical treatment of S. aureus- infected DFU.

Methods

This study compared the transcriptome profile of 21 patients with S. aureus- infected DFU who underwent initial foot salvage therapy with irrigation and debridement followed by intravenous antibiotic therapy. Blood samples were collected at the recruitment (0 weeks) and 8 weeks after therapy to isolate peripheral blood mononuclear cells (PBMCs). We analyzed the PBMC expression of transcriptomes at two different time points (0 versus 8 weeks). Subjects were further divided into two groups at 8 weeks: healed (n = 17, 80.95%) versus non-healed (n = 4, 19.05%) based on the wound healing status. DESeq2 differential gene analysis was performed.

Results and discussion

An increased expression of IGHG1, IGHG2, IGHG3, IGLV3-21, and IGLV6-57 was noted during active infection at 0 weeks compared with that at 8 weeks. Lysine- and arginine-rich histones (HIST1H2AJ, HIST1H2AL, HIST1H2BM, HIST1H3B, and HIST1H3G) were upregulated at the initial phase of active infection at 0 weeks. CD177 and RRM2 were also upregulated at the initial phase of active infection (0 weeks) compared with that at 8 weeks of follow-up. Genes of heat shock protein members (HSPA1A, HSPE1, and HSP90B1) were high in not healed patients compared with that in healed patients 8 weeks after therapy. The outcome of our study suggests that the identification of genes evolution based on a transcriptomic profiling could be a useful tool for diagnosing infection and assessing severity and host immune response to therapies.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Decision tree algorithm predicts hepatocellular carcinoma among chronic hepatitis C patients following viral eradication

Successful eradication of the hepatitis C virus (HCV) cannot eliminate the risk of hepatocellular carcinoma (HCC). Next-generation RNA sequencing provides comprehensive genomic insights into the pathogenesis of HCC. Artificial intelligence has opened a new era in precision medicine. This study integrated clinical features and genetic biomarkers to establish a machine learning-based HCC model following viral eradication. A prospective cohort of 55 HCV patients with advanced fibrosis, who achieved a sustained virologic response after antiviral therapy, was enrolled. The primary outcome was the occurrence of HCC. The genomic signatures of peripheral blood mononuclear cells (PBMC) were determined by RNA sequencing at baseline and 24 weeks after end-of-treatment. Machine learning algorithms were implemented to extract the predictors of HCC. HCC occurred in 8 of the 55 patients, with an annual incidence of 2.7%. Pretreatment PBMC DEFA1B, HBG2, ADCY4, and posttreatment TAS1R3, ABCA3, and FOSL1 genes were significantly downregulated, while the pretreatment ANGPTL6 gene was significantly upregulated in the HCC group compared to that in the non-HCC group. A gene score derived from the result of the decision tree algorithm can identify HCC with an accuracy of 95.7%. Gene score = TAS1R3 (≥0.63 FPKM, yes/no = 0/1) + FOSL1 (≥0.27 FPKM, yes/no = 0/1) + ABCA3 (≥2.40 FPKM, yes/no = 0/1). Multivariate Cox regression analysis showed that this gene score was the most important predictor of HCC (hazard ratio = 2.38, 95% confidence interval [CI] = 1.06-5.36, P = 0.036). Combining the gene score and fibrosis-4 index, a nomogram was constructed to predict the probability of HCC with an area under the receiver operating characteristic curve up to 0.950 (95% CI = 0.888-1.000, P = 7.0 × 10-5). Decision curve analysis revealed that the nomogram had a net benefit in HCC detection. The calibration curve showed that the nomogram had optimal concordance between the predicted and actual HCC probabilities. In conclusion, down-regulated posttreatment PBMC TAS1R3, ABCA3, and FOSL1 expression were significantly correlated with HCC development after HCV eradication. Decision-tree-based algorithms can refine the assessment of HCC risk for personalized HCC surveillance.

Systems Pharmacology-Based Strategy to Investigate the Mechanism of Ruangan Lidan Decoction for Treatment of Hepatocellular Carcinoma

epatocellular carcinoma (HCC) is one of the leading contributors to cancer mortality worldwide. Currently, the prevention and treatment of HCC remains a major challenge. As a traditional Chinese medicine (TCM) formula, Ruangan Lidan decoction (RGLD) has been proved to own the effect of relieving HCC symptoms. However, due to its biological effects and complex compositions, its underlying mechanism of actions (MOAs) have not been fully clarified yet. In this study, we proposed a pharmacological framework to systematically explore the MOAs of RGLD against HCC. We firstly integrated the active ingredients and potential targets of RGLD. We next highlighted 25 key targets that played vital roles in both RGLD and HCC disease via a protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Furthermore, an ingredient-target network of RGLD consisting of 216 ingredients with 306 targets was constructed, and multilevel systems pharmacology analyses indicated that RGLD could act on multiple biological processes related to the pathogenesis of HCC, such as cellular response to hypoxia and cell proliferation. Additionally, integrated pathway analysis of RGLD uncovered that RGLD might treat HCC through regulating various pathways, including MAPK signaling pathway, PI3K/Akt signaling pathway, TNF signaling pathway, and ERBB signaling pathway. Survival analysis results showed that HCC patients with low expression of VEGFA, HIF1A, CASP8, and TOP2A were related with a higher survival rate than those with high expression, indicating the potential clinical significance for HCC. Finally, molecular docking results of core ingredients and targets further proved the feasibility of RGLD in the treatment of HCC. Overall, this study indicates that RGLD may treat HCC through multiple mechanisms, which also provides a potential paradigm to investigate the MOAs of TCM prescription.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

Bioinformatics searching of diagnostic markers and immune infiltration in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is one of the most common endocrine diseases in reproductive-aged women, and it affects numerous women worldwide. This study aimed to identify potential diagnostic markers and explore the infiltration of immune cells in PCOS, contributing to the development of potential therapeutic drugs for this disease. We identified five key genes: CBLN1 (AUC = 0.924), DNAH5 (AUC = 0.867), HMOX1 (AUC = 0.971), SLC26A8 (AUC = 0,933), and LOC100507250 (AUC = 0.848) as diagnostic markers of PCOS. Compared with paired normal group, naïve B cells, gamma delta T cells, resting CD4 memory T cells, and activated CD4 memory T cells were significantly decreased in PCOS while M2 macrophages were significantly increased. Significant correlations were presented between the five key genes and the components of immune infiltrate. The results of CMap suggest that four drugs, ISOX, apicidin, scriptaid, and NSC-94258, have the potential to reverse PCOS. The present study helps provide novel insights for the prevention and treatment of PCOS, and immune cell infiltration plays a role that cannot be ignored in the occurrence and progression of the disease.

Transcriptomic Analyses Reveal Long Non-Coding RNA in Peripheral Blood Mononuclear Cells as a Novel Biomarker for Diagnosis and Prognosis of Hepatocellular Carcinoma

Novel biomarkers are highly required for the diagnosis and predicting prognosis of hepatocellular carcinoma (HCC). In this study, we investigated the profiles of long non-coding RNAs (lncRNAs) obtained from the peripheral blood mononuclear cells (PBMCs) of patients with HCC and PBMCs from a co-culture model using transcriptomic analysis. The differentially expressed lncRNAs (DElncRNAs) were then characterized and integrated as cancer-induced lncRNAs. Among them, three up-regulating DElncRNAs including MIR4435-2HG, SNHG9 and lnc-LCP2-1 and one down-regulating, lnc-POLD3-2, were identified. The functional analysis showed that these enriched lncRNAs were mainly associated with carcinogenesis and immune responses. Following further validation in PBMCs samples (100 HBV-related HCC, 100 chronic hepatitis B and 100 healthy controls), MIR4435-2HG, lnc-POLD3-2 and their combination were revealed to be sensitive biomarkers in discriminating HCC from non-HCC (AUROC = 0.78, 0.80, and 0.87, respectively), particularly among individuals with normal serum alpha-fetoprotein levels. Additionally, high circulating SNHG9 expression was shown to be an independent prognostic factor of overall survival in patients with HCC. These results indicate that determining these lncRNAs in PBMCs could serve as novel diagnostic and prognostic biomarkers for HBV-related HCC.

Identifying Potential New Gene Expression-Based Biomarkers in the Peripheral Blood Mononuclear Cells of Hepatitis B-Related Hepatocellular Carcinoma

OBJECTIVE

The analysis of the gene expression of peripheral blood mononuclear cells (PBMCs) is important to clarify the pathogenesis of hepatocellular carcinoma (HCC) and the detection of suitable biomarkers. The purpose of this investigation was to use RNA-sequencing to screen the appropriate differentially expressed genes (DEGs) in the PBMCs for the HCC.

METHODS

The comprehensive transcriptome of extracted RNA of PBMC (n = 20) from patients with chronic hepatitis B (CHB), liver cirrhosis, and early stage of HCC (5 samples per group) was carried out using RNA-sequencing. All raw RNA-sequencing data analyses were performed using conventional RNA-sequencing analysis tools. Next, gene ontology (GO) analyses were carried out to elucidate the biological processes of DEGs. Finally, relative transcript abundance of selected DEGs was verified using qRT-PCR on additional validation groups.

RESULTS

Specifically, 13, 1262, and 1450 DEGs were identified for CHB, liver cirrhosis, and HCC, when compared with the healthy controls. GO enrichment analysis indicated that HCC is closely related to the immune response. Seven DEGs (TYMP, TYROBP, CD14, TGFBI, LILRA2, GNLY, and GZMB) were common to HCC, cirrhosis, and CHB when compared to healthy controls. The data revealed that the expressions of these 7 DEGs were consistent with those from the RNA-sequencing results. Also, the expressions of 7 representative genes that had higher sensitivity were obtained by receiver operating characteristic analysis, which indicated their important diagnostic accuracy for HBV-HCC.

CONCLUSION

This study provides us with new horizons into the biological process and potential prospective clinical diagnosis and prognosis of HCC in the near future.

Whole Blood Transcriptional Fingerprints of High-Grade Glioma and Longitudinal Tumor Evolution under Carbon Ion Radiotherapy

Simple Summary

Particle therapy with carbon ions is a promising novel option for the treatment of recurrent high-grade glioma (rHGG). Lack of initial and sequential biopsies limits the investigation of rHGG evolution under therapy. We hypothesized that peripheral blood transcriptome derived from liquid biopsies (lbx) as a minimal invasive method may provide a useful decision support for identification of glioma grade and provide novel means for longitudinal molecular monitoring of tumor evolution under carbon ion irradiation (CIR). We demonstrate feasibility and report patient, tumor and treatment fingerprints in whole blood transcriptomes of rHGG patients with pre-CIR and three post-CIR time points.

Abstract

Purpose: To assess the value of whole blood transcriptome data from liquid biopsy (lbx) in recurrent high-grade glioma (rHGG) patients for longitudinal molecular monitoring of tumor evolution under carbon ion irradiation (CIR). Methods: Whole blood transcriptome (WBT) analysis (Illumina HumanHT-12 Expression BeadChips) was performed in 14 patients with rHGG pre re-irradiation (reRT) with CIR and 3, 6 and 9 weeks post-CIR (reRT grade III:5, 36%, IV:9, 64%). Patients were irradiated with 30, 33, 36 GyRBE (n = 5, 6, 3) in 3GyRBE per fraction. Results: WTB analysis showed stable correlation with treatment characteristics and patients tumor grade, indicating a preserved tumor origin specific as well as dynamic transcriptional fingerprints of peripheral blood cells. Initial histopathologic tumor grade was indirectly associated with TMEM173 (STING), DNA-repair (ATM, POLD4) and hypoxia related genes. DNA-repair, chromatin remodeling (LIG1, SMARCD1) and immune response (FLT3LG) pathways were affected post-CIR. Longitudinal WTB fingerprints identified two distinct trajectories of rHGG evolution, characterized by differential and prognostic CRISPLD2 expression pre-CIR. Conclusions: Lbx based WTB analysis holds the potential for molecular stratification of rHGG patients and therapy monitoring. We demonstrate the feasibility of the peripheral blood transcriptome as a sentinel organ for identification of patient, tumor characteristics and CIR specific fingerprints in rHGG.