C8B in Complement and Coagulation Cascades Signaling Pathway is a predictor for Survival in HBV-Related Hepatocellular Carcinoma Patients

Three-dimensional genome architecture in intrahepatic cholangiocarcinoma

PURPOSE

Intrahepatic cholangiocarcinoma (ICC) is a common primary hepatic tumors with a 5-year survival rate of less than 20%. Therefore, it is crucial to elucidate the molecular mechanisms of ICC. Recently, the advance of high-throughput chromosome conformation capture (Hi-C) technology help us look insight into the three-dimensional (3D) genome structure variation during tumorigenesis. However, its function in ICC pathogenesis remained unclear.

METHODS

Hi-C and RNA-sequencing were applied to analyze 3D genome structures and gene expression in ICC and adjacent noncancerous hepatic tissue (ANHT). Furthermore, the dysregulated genes due to 3D genome changes were validated via quantitative real-time PCR and immunohistochemistry.

RESULTS

Primarily, the intrachromosomal interactions of chr1, chr2, chr3, and chr11 and the interchromosomal interactions of chr1-chr10, chr13-chr21, chr16-chr19, and chr19-chr22 were also significantly distinct between ANHT and ICC, which may potentially contribute to the activation of cell migration and invasion via the upregulation of WNT10A, EpCAM, S100A3/A6, and MAPK12. Interestingly, 56 compartment regions from 23 chromosomes underwent A to B or B to A transitions during ICC oncogenesis, which attenuated the complement pathway through the downregulation of C8A/C8B, F7, F10, and F13B. Notably, topologically associated domain (TAD) rearrangements were identified in the region containing HOPX (chr4: 57,514,154-57,522,688) and ACVR1 (chr2:158,592,958-158,732,374) in ICC, which may contribute to the hijacking of remote enhancers that were previously outside the TAD and increased expression of HOPX and ACVR1.

CONCLUSIONS

This study reveals relationship between 3D genome structural variations and gene dysregulation during ICC tumorigenesis, indicating the molecular mechanisms and potential biomarkers.

Novel prognostic model of complement and coagulation cascade-related genes correlates with immune environment and drug sensitivity in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is an immunogenic cancer characterized by high morbidity and mortality rates. The complement and coagulation systems are traditionally associated with the incidence of thrombotic complications and complement activation in cancer. However, the prognostic value of complement and coagulation-related factors (CCCR) in HCC remains undetermined. This study aims to construct a prognostic model based on the complement and coagulation cascades to evaluate its potential for immunotherapy and its relationship with drug sensitivity.

Materials and methods

We comprehensively investigated the expression profiles of CCCR genes using the TCGA, ICGC, and GTEx databases. Cox proportional hazards regression models were employed to assess prognostic value.

Results

This study presents a novel prognostic model derived from the comprehensive analysis of nine CCCR genes (C1S, C6, C7, F11, F13B, F7, SERPINE1, SERPINF2, and SERPING1) to elucidate their correlation with the tumor immune environment and drug sensitivity in patients with HCC. Our model stratified patients into high- and low-risk groups based on distinct survival outcomes. The area under the curve (AUC) values of the risk score for one-, two-, and three-year survival rates were all greater than 0.660. Additionally, we analyzed immune cell infiltration patterns, revealing a strong correlation between CCCR gene expression and the immune microenvironment, including T cell and macrophage activity. Our findings also identified potential therapeutic targets, demonstrating differential drug sensitivity profiles between the risk groups. JAK1_8709_1718 was found to be more suitable for patients with low-risk HCC.

Conclusion

Our findings provide promising insights into the clinical relevance of CCCR genes as prognostic markers and therapeutic targets. This study underscores the significance of CCCR in HCC and paves the way for improved therapeutic strategies.

Benchmark N-glycoproteomics study of common differential tissue and serum N-glycoproteins of patients with hepatocellular carcinoma

For hepatocellular carcinoma (HCC), N-glycosylation has been proved to be widely involved in various aspects of the disease, including development, metastasis, subtyping, diagnosis and prognosis. The common practice is to discover biomarkers in situ of cancer occurrence (i.e., cancer vs. adjacent tissues) yet to clinically monitor in sera because of non-invasiveness. This study benchmarks N-glycoproteomics characterization of common differential tissue and serum N-glycoproteins of patients with HCC. Differential N-glycosylation in matched tissue and serum samples from the same patients were quantitatively characterized at the intact N-glycopeptide molecular level, and 29 common N-glycoproteins were found. Subcellular localization analysis was carried out to confirm the tissue originality. Secreted N-glycoprotein APOH was up-regulated, and transmembrane and intracellular N-glycoproteins including OSMR, GAT2, CSF-1 and MAGI3 were down-regulated.

Chemical burn wounds as a risk factor for gastric cancer: in-silico analyses-experimental research

Introduction

The present study employs bioinformatics tools to identify shared upregulated genes between chemical burns and gastric cancer.

Methods

Gene Expression Omnibus (GEO) retrieved gene sets for this investigation. GSEs with P value less than 0.05 and LOG fold change (FC) greater than 1 were valid and upregulated. Gastric cancer and chemical burn common elevated genes were found using Venn diagram online tools. In the second stage, the "string" visualized gastric cancer elevated genes network, and non-coding RNAs were deleted, and "interaction" greater than 1 was examined to choose important gene nodes. Next, they explored the String gene-interaction network for common genes. To determine the most interacting genes, Gephi (V 0.9.7) used "betweenness centrality" greater than "0" to evaluate the twenty-gene network. TISIDB and drug banks provide gene-related medications.

Results

In the present study, two genes, including ALOX5AP and SERPINB2, were obtained, with the highest centrality among chemical burns and gastric cancer shared genes. Additionally, the current study presented five drugs, including Urokinase, Tenecteplase, DG031, AM103, and Fiboflapon, which can have predicted effects on gastric cancer following chemical burns.

Conclusion

According to current in-silicon analyses, ALOX5AP and SERPINB2 are linked genetic keys between gastric chemical burn and cancer. Considering that burn is an environmental factor that leads to the upregulation of the two genes thus, the chemical burn can be related to the incidence of gastric cancer.

Antitumor Effects and the Potential Mechanism of 10-HDA against SU-DHL-2 Cells

10-hydroxy-2-decenoic acid (10-HDA), which is a unique bioactive fatty acid of royal jelly synthesized by nurse bees for larvae and adult queen bees, is recognized for its dual utility in medicinal and nutritional applications. Previous research has indicated that 10-HDA exerts antitumor effects on numerous tumor cell lines, including colon cancer cells, A549 human lung cancer cells, and human hepatoma cells. The present study extends this inquiry to lymphoma, specifically evaluating the impact of 10-HDA on the SU-DHL-2 cell line. Our findings revealed dose-dependent suppression of SU-DHL-2 cell survival, with an IC50 of 496.8 μg/mL at a density of 3 × 106 cells/well after 24 h. For normal liver LO2 cells and human fibroblasts (HSFs), the IC50 values were approximately 1000 μg/mL and over 1000 μg/mL, respectively. The results of label-free proteomics revealed 147 upregulated and 347 downregulated differentially expressed proteins that were significantly enriched in the complement and coagulation cascades pathway (adjusted p-value = 0.012), including the differentially expressed proteins prothrombin, plasminogen, plasminogen, carboxypeptidase B2, fibrinogen beta chain, fibrinogen gamma chain, and coagulation factor V. The top three hub proteins, ribosomal protein L5, tumor protein p53, and ribosomal protein L24, were identified via protein-protein interaction (PPI) analysis. This result showed that the complement and coagulation cascade pathways might play a key role in the antitumor process of 10-HDA, suggesting a potential therapeutic avenue for lymphoma treatment. However, the specificity of the effect of 10-HDA on SU-DHL-2 cells warrants further investigation.

High-throughput N-glycoproteomics with fast liquid chromatographic separation

N-glycosylation is a common protein post translation modification, which has tremendous structure diversity and wide yet delicate regulation of protein structures and functions. Mass spectrometry-based N-glycoproteomics has become a state-of-the-art pipeline for both qualitative and quantitative characterization of N-glycosylation at the intact N-glycopeptide level, providing comprehensive information of peptide backbones, N-glycosites, monosaccharide compositions, sequence and linkage structures. For high-throughput analysis of large-cohort clinic samples, fast and high-performance separation is indispensable. Here we report our development of 1-h liquid chromatography gradient N-glycoproteomics method and accordingly optimized MS parameters. In the benchmark analysis of cancer and paracancerous tissue of hepatocellular carcinoma, 5,218 intact N-glycopeptides were identified, where 422 site- and structure-specific differential N-glycosylation on 145 N-glycoproteins was observed. The method, representing substantial increase of throughput, can be adopted for fast and efficient analysis of N-glycoproteomes at large scale.

Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.

Intact glycopeptides identified by LC-MS/MS as biomarkers for response to chemotherapy of locally advanced cervical cancer

Objective

For locally advanced cervical cancer (LACC), patients who respond to chemotherapy have a potential survival advantage compared to nonresponsive patients. Thus, it is necessary to explore specific biological markers for the efficacy of chemotherapy, which is beneficial to personalized treatment.

Methods

In the present study, we performed a comprehensive screening of site-specific N-glycopeptides in serum glycoproteins to identify glycopeptide markers for predicting the efficacy of chemotherapy, which is beneficial to personalized treatment. In total, 20 serum samples before and after neoadjuvant chemotherapy (NACT) from 10 LACC patients (NACT response, n=6) and NACT nonresponse, n=4) cases) were analyzed using LC-MS/MS, and 20 sets of mass spectrometry (MS) data were collected using liquid chromatography coupled with high-energy collisional dissociation tandem MS (LC-HCD-MS/MS) for quantitative analysis on the novel software platform, Byos. We also identified differential glycopeptides before and after chemotherapy in chemo-sensitive and chemo-resistant patients.

Results

In the present study, a total of 148 glycoproteins, 496 glycosylation sites and 2279 complete glycopeptides were identified in serum samples of LACC patients. Before and after chemotherapy, there were 13 differentially expressed glycoproteins, 654 differentially expressed glycopeptides and 93 differentially expressed glycosites in the NACT responsive group, whereas there were 18 differentially expressed glycoproteins, 569 differentially expressed glycopeptides and 99 differentially expressed glycosites in the NACT nonresponsive group. After quantitative analysis, 6 of 570 glycopeptides were identified as biomarkers for predicting the sensitivity of neoadjuvant chemotherapy in LACC. The corresponding glycopeptides included MASP1, LUM, ATRN, CO8A, CO8B and CO6. The relative abundances of the six glycopeptides, including MASP1, LUM, ATRN, CO8A, CO8B and CO6, were significantly higher in the NACT-responsive group and were significantly decreased after chemotherapy. High levels of these six glycopeptides may indicate that chemotherapy is effective. Thus, these glycopeptides are expected to serve as biomarkers for predicting the efficacy of neoadjuvant chemotherapy in locally advanced cervical cancer.

Conclusion

The present study revealed that the N-glycopeptide of MASP1, LUM, ATRN, CO8A, CO8B and CO6 may be potential biomarkers for predicting the efficacy of chemotherapy for cervical cancer.

A novel plasma proteomic-based model for predicting liver fibrosis in HIV/HBV co-infected adults

To establish a plasma model to predict the risk of liver fibrosis in HIV/HBV co-infected individuals. Quantitative liquid chromatography-tandem mass spectrometry(LC-MS/MS) was used to identify differentially expressed proteins (DEPs) in plasma collected from HIV/HBV co-infected individuals with and without liver fibrosis. In total, 97 DEPs were identified, among which 11 were further validated as potential biomarkers, with immunoglobulin and complement components being the most common proteins. These markedly altered proteins were found to mediate pathophysiological pathways, including humoral immune response, complement and coagulation cascades, and complement activation. A visual logistic model, in which immunoglobulin heavy variable 3-20 (IGHV3-20), immunoglobulin heavy variable 1-24 (IGHV1-24), and macrophage colony-stimulating factor 1 receptor (CSF1R) proteins were included, has been established to predict liver fibrosis in HIV/HBV co-infected individuals. The preliminary conclusion showed that the combination of IGHV3-20, IGFHV1-24, and CSF1R is expected to become a predictive model for liver fibrosis in the context of HIV/HBV co-infection and a further validation should be performed.

Integrated analysis of transcriptomics and metabolomics in human hepatocellular carcinoma HepG2215 cells after YAP1 knockdown

Yes-associated protein 1 (YAP1) plays a critical role in hepatocellular carcinoma (HCC). Inhibition of YAP1 expression suppresses HCC progression, but the underlying mechanism is still unclear. In this study, we studied the effects and molecular mechanisms of YAP1 knockdown on the growth and metabolism in human HCC HepG2215 cells. Inhibition of YAP1 expression inhibits the proliferation and metastasis in HepG2215 cells, and differentially expressed genes (DEGs) and metabolites were identified in shYAP1-HepG2215 cells. Further, 805 DEGs, mainly associated with metabolism and particularly lipid metabolism, were identified by transcriptome sequencing analyses in shYAP1-HepG2215 cells. YAP1 knockdown increased albumin (ALB) levels by Protein-protein interaction (PPI) network analyses in HepG2215 cells. Metabolomic profiling identified 37 metabolites with significant differences in the shYAP1 group, and amino acid metabolism generally decreased in the shYAP1 group. Comprehensive analysis of transcriptomics and metabolomics revealed that the ATP-binding cassette (ABC) transporters play a central role after YAP1 knockdown in HepG2215 cells. Therefore, YAP1 knockdown inhibited HCC growth, which affected the metabolism of lipids and amino acids by regulating the expression of ALB and ABC transporters in HepG2215 cells.

A new acidic microenvironment related lncRNA signature predicts the prognosis of liver cancer patients

BACKGROUND

The acidic microenvironment (AME), like hypoxia, inflammation, or immunoreaction, is a hallmark of the tumor microenvironment (TME). This work aimed to develop a prediction signature dependent on AME-associated lncRNAs in order to predict the prognosis of LC individuals.

METHODS

We downloaded RNA-seq information and the corresponding clinical and predictive data from The Cancer Genome Atlas (TCGA) dataset and conducted univariate and multivariate Cox regression analyses to identify AME-associated lncRNAs for the construction of a prediction signature The Kaplan-Meier technique was utilized to determine the overall survival (OS) rate of the high (H)-risk and low (L)-risk groups. Using gene set enrichment analysis (GSEA) the functional variations between the H- and L-risk groups were investigated. The association between the prediction signature and immunological state was investigated using single-sample GSEA (ssGSEA). Additionally, the association between the predicted signature and the therapeutic response of LC individuals was evaluated. Lastly, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model.

RESULTS

We generated a signature comprised of seven AME-associated lncRNAs (LINC01116, AC002511.2, LINC00426, ARHGAP31-AS1, LINC01060, TMCC1-AS1, AC012065.1). The H-risk group had a worse prognosis than the L- risk group. The AME-associated lncRNA signature might determine the prognosis of individuals with LC independently. The AME-related lncRNA signature shows a greater predictive effectiveness than clinic-pathological factors, with an area under the receiver operating characteristic (ROC) curve of 0.806%. When participants were categorized based on several clinico-pathological characteristics, the OS of high-risk individuals was shorter compared to low-risk patients. GSEA demonstrated that the metabolism of different acids and the PPAR signaling pathway are closely associated with low-risk individuals. The prognostic signature was substantially associated with the immunological status of LC individuals, as determined by ssGSEA. High risk individuals were more sensitive to some immunotherapies (including anti-TNFSF4 anti-SIRPA, anti-CD276 and anti-TNFSF15) and some conventional chemotherapy drugs (including lapatinib and paclitaxel). Finally, the expression levels of the seven lncRNAs comprising the signature were tested by qRT-PCR.

CONCLUSIONS

A basis for the mechanism of AME-associated lncRNAs in LC is provided by the prediction signature, which also offers clinical therapeutic recommendations for LC individuals.

An update on the role of complement in hepatocellular carcinoma

As a main producer of complement, the environment in the liver is greatly affected by the complement system. Although the complement system is considered to have the ability of nonself discrimination, remarkable studies have revealed the tight association between improper complement activation in tumour initiation and progression. As complement activation predominantly occurs within the liver, the protumourigenic role of the complement system may contribute to the development of hepatocellular carcinoma (HCC). Improvement in the understanding of the molecular targets involved in complement-mediated tumour development, metastasis, and tumour-promoting inflammation in HCC would certainly aid in the development of better treatments. This minireview is focused on recent findings of the protumourigenic role of the complement system in HCC.

A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data

With the development of high-throughput technologies, the accumulation of large amounts of multidimensional genomic data provides an excellent opportunity to study the multilevel biological regulatory relationships in cancer. Based on the hypothesis of competitive endogenous ribonucleic acid (RNA) (ceRNA) network, lncRNAs can eliminate the inhibition of microRNAs (miRNAs) on their target genes by binding to intracellular miRNA sites so as to improve the expression level of these target genes. However, previous studies on cancer expression mechanism are mostly based on individual or two-dimensional data, and lack of integration and analysis of various RNA-seq data, making it difficult to verify the complex biological relationships involved. To explore RNA expression patterns and potential molecular mechanisms of cancer, a network-regularized sparse orthogonal-regularized joint non-negative matrix factorization (NSOJNMF) algorithm is proposed, which combines the interaction relations among RNA-seq data in the way of network regularization and effectively prevents multicollinearity through sparse constraints and orthogonal regularization constraints to generate good modular sparse solutions. NSOJNMF algorithm is performed on the datasets of liver cancer and colon cancer, then ceRNA co-modules of them are recognized. The enrichment analysis of these modules shows that >90% of them are closely related to the occurrence and development of cancer. In addition, the ceRNA networks constructed by the ceRNA co-modules not only accurately mine the known correlations of the three RNA molecules but also further discover their potential biological associations, which may contribute to the exploration of the competitive relationships among multiple RNAs and the molecular mechanisms affecting tumor development.

N7-Methylguanosine-Related lncRNAs: Integrated Analysis Associated With Prognosis and Progression in Clear Cell Renal Cell Carcinoma

N7-Methylguanosine (m7G) and long non-coding RNAs (lncRNAs) have been widely reported to play an important role in cancer. However, there is little known about the relationship between m7G-related lncRNAs and clear cell renal cell carcinoma (ccRCC). To find new potential biomarkers and construct an m7G-related lncRNA prognostic signature for ccRCC, we retrieved transcriptome data and clinical data from The Cancer Genome Atlas (TCGA), and divided the entire set into train set and test set with the ratio of 1:1 randomly. The m7G-related lncRNAs were identified by Pearson correlation analysis (|coefficients| > 0.4, and p < 0.001). Then we performed the univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a 12 m7G-related lncRNA prognostic signature. Next, principal component analysis (PCA), the Kaplan–Meier method, time-dependent receiver operating characteristics (ROC) were made to verify and evaluate the risk signature. A nomogram based on the risk signature and clinical parameters was developed and showed high accuracy and reliability for predicting the overall survival (OS). Functional enrichment analysis (GO, KEGG and GSEA) was used to investigate the potential biological pathways. We also performed the analysis of tumor mutation burden (TMB), immunological analysis including immune scores, immune cell infiltration (ICI), immune function, tumor immune escape (TIE) and immunotherapeutic drug in our study. In conclusion, using the 12 m7G-related lncRNA risk signature as a prognostic indicator may offer us insight into the oncogenesis and treatment response prediction of ccRCC.

Identification of gene signature in RNA-Seq hepatocellular carcinoma data by Pareto-optimal cluster algorithm

Aim

This study aimed to detect gene signatures in RNA-sequencing (RNA-seq) data using Pareto-optimal cluster size identification.

Background

RNA-seq has emerged as an important technology for transcriptome profiling in recent years. Gene expression signatures involving tens of genes have been proven to be predictive of disease type and patient response to treatment.

Methods

Data related to the liver cancer RNA-seq dataset, which included 35 paired hepatocellular carcinoma (HCC) and non-tumor tissue samples, was used in this study. The differentially expressed genes (DEGs) were identified after performing pre-filtering and normalization. After that, a multi-objective optimization technique, namely multi-objective optimization for collecting cluster alternatives (MOCCA), was used to discover the Pareto-optimal cluster size for these DEGs. Then, the k-means clustering method was performed on the RNA-seq data. The best cluster, as a signature for the disease, was found by calculating the average Spearman's correlation score of all genes in the module in a pair-wise manner. All analyses were performed in the R 4.1.1 package in virtual space with 100 Gb of RAM memory.

Results

Using MOCCA, eight Pareto-optimal clusters were obtained. Ultimately, two clusters with the greatest average Spearman's correlation coefficient scores were chosen as gene signatures. Eleven prognostic genes involved in HCC's abnormal metabolism were identified. In addition, three differentially expressed pathways were identified between tumor and non-tumor tissues.

Conclusion

These identified metabolic prognostic genes help us to provide more powerful prognostic information and enhance survival prediction for HCC patients. In addition, Pareto-optimal cluster size identification is suggested for gene signature in other RNA-Seq data.

Comparative Analysis of Serum Proteins Between Hepatitis B Virus Genotypes B and C Infection by DIA-Based Quantitative Proteomics

Purpose

In clinical practice, the clinicopathological profiles and outcomes of patients infected with hepatitis B virus (HBV) are different between genotypes B and C. However, little is known about the potential mechanism and differences in specific biological pathways associated with the different genotype. This study aimed to compare the serum protein profile between patients infected with HBV genotype B and those infected with HBV genotype C.

Patients and Methods

A total of 54 serum samples from patients with chronic HBV genotype B infection and those with chronic HBV genotype C infection, and healthy controls were used for the proteomic analysis (n = 18 samples in per group). Serum proteomic profiles were analyzed using data-independent acquisition (DIA)-based liquid chromatography-mass spectrometry to identify differentially expressed proteins (up- or downregulation of at least 1.5-fold) between serum samples from HBV patients infected with HBV genotype B and those infected with genotype C.

Results

We identified 1010 proteins, 53 of which were differentially expressed between the serum samples of the healthy controls and those of HBV genotype B infected patients, and 59 that were differentially expressed between the samples of the healthy controls and those of HBV genotype C infected patients. Furthermore, our results indicated that two proteins identified as being differentially expressed (VWF and C8B) have potential as biomarkers for distinguishing genotype B infected HBV patients from those infected with genotype C.

Conclusion

The results of our DIA-based quantitative proteomic analysis revealed that HBV genotypes B and C are associated with different molecular profiles and may provide fundamental information for further detailed investigations of the molecular mechanism underlying these differences.