Identification of novel biomarkers for hepatocellular carcinoma using transcriptome analysis

ENAH transcriptionally activated by YY1 promotes growth and invasion of laryngocarcinoma cells through PI3K/AKT signaling

BACKGROUND

Laryngocarcinoma is a common malignancy in the upper respiratory tract. Enabled homolog (ENAH) is an actin-binding protein that is associated with the development of various cancers. However, its role and mechanism in laryngocarcinoma remain unknown.

METHODS

The ENAH level in laryngocarcinoma was examined in silico, in vitro and in vivo. The prognostic analysis of the ENAH level was assessed on laryngocarcinoma patients. Gain- and loss-of-function assays were conducted in AMC-HN-8 and TU686 cells. Sh-ENAH-containing AMC-HN-8 cells were implanted into naked mice. The role and mechanism of ENAH in laryngocarcinoma were investigated by CCK-8, transwell, immunofluorescence, dual luciferase, RT-qPCR, immunohistochemistry, and western blotting experiments.

RESULTS

The ENAH level was upregulated in laryngocarcinoma, which predicted a poor prognosis in laryngocarcinoma patients. Gain- and loss-of-function results showed that ENAH promoted proliferation, invasion and EMT of laryngocarcinoma cells. Moreover, ENAH was transcriptionally activated by YY1, and YY1/ENAH axis enhanced these malignant progresses of laryngocarcinoma cells. Besides, ENAH activated the PI3K/AKT pathway, and 740Y-P abolished the accelerative role of ENAH in proliferation, invasion and EMT of laryngocarcinoma cells. Furthermore, knockdown of ENAH reduced tumor size and weight, and the expression level of vimentin and PI3K/AKT pathway in tumor-bearing mice.

CONCLUSION

ENAH transcriptionally activated by YY1 promotes cell growth, invasion and EMT of laryngocarcinoma through the activation of PI3K/AKT signaling.

Unveiling miRNA-Gene Regulatory Axes as Promising Biomarkers for Liver Cirrhosis and Hepatocellular Carcinoma

Liver cirrhosis, a severe scarring condition of the liver with the potential to progress to hepatocellular carcinoma (HCC), necessitates the development of reliable biomarkers for early detection due to the asymptomatic nature of its early stages. Recent discoveries in microRNAs (miRNAs) hold promise for a noninvasive test, with the potential to significantly improve patient outcomes. Building upon these promising findings, this study investigates gene expression data, identifying distinct sets of DEGs and DEMs using GEO2R. Subsequently, a gene-miRNA network was constructed using Cytoscape to explore potential interactions between DEMs and their target genes (DEGs). Boxplot analysis was carried out to identify and validate differences in gene expression between healthy and diseased tissues. This analysis revealed four significantly differentially expressed genes: CAV1, PEA15, EMP1, and ENAH. Notably, subsequent survival analysis demonstrated that EMP1 and ENAH significantly impact overall patient survival. Intriguingly, the constructed network identified several potential regulatory axes: hsa-miR-191-5p/ENAH, hsa-miR-3158-3p/ENAH, hsa-miR-371a-5p/ENAH, and hsa-miR-6753-5p/EMP1. Crucially, a direct comparison of DEGs and DEMs between liver cirrhosis and HCC pinpointed AGO3, NCOA3, and TNPO1, along with their regulatory elements, as potential key drivers of HCC development in cirrhotic patients, underscoring their importance as targets for early diagnostic and therapeutic strategies. Finally, immunohistochemical (IHC) analysis not only validates our findings but also reiterates the novelty of the identified genes. Overall, elucidating the role of these novel genes and regulatory elements could pave the way for an earlier and more accurate diagnosis of liver diseases.

HMGN1 loss sensitizes lung cancer cells to chemotherapy

The high mobility group nucleosome binding (HMGN) family, constitutes a large family of non-histone protein family known to bind the acidic patch of the nucleosomes with various key cellular functions. Several studies have highlighted the pivotal roles of HMGNs in the pathogenic process of various cancer types. However, the roles of HMGN family in lung adenocarcinoma (LUAD) have not been fully elucidated. Herein, integrative analyses of multiple-omics data revealed that HMGNs frequently exhibit dysregulation in LUAD. Subsequent analysis of the clinical relevance of HMGN1 demonstrated its association with poor prognosis in LUAD and its potential as a diagnostic marker to differentiate LUAD from healthy controls. Additionally, functional enrichment analysis suggested that HMGN1 was mainly involved in DNA repair. To corroborate these findings, cellular experiments were conducted, confirming HMGN1's crucial involvement in homologous recombination repair and its potential to enhance the sensitivity of LUAD cells to standard chemotherapeutic drugs. This study proposes HMGN1 as a novel prognostic biomarker and a promising target for chemotherapy in lung adenocarcinoma.

5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

HMGN5 Escorts Oncogenic STAT3 Signaling by Regulating the Chromatin Landscape in Breast Cancer Tumorigenesis

Cancer progression is highly dependent on the ability of cancer cell tumor formation, in which epigenetic modulation plays an essential role. However, the epigenetic factors promoting breast tumor formation are less known. Screened from three-dimensional (3D)-sphere tumor formation model, HMGN5 that regulates chromatin structures became the candidate therapeutic target in breast cancer, though its role is obscure. HMGN5 is highly expressed in 3D-spheres of breast cancer cells and clinical tumors, also an unfavorable prognostic marker in patients. Furthermore, HMGN5 controls tumor formation and metastasis of breast cancer cells in vitro and in vivo. Mechanistically, HMGN5 is governed by active STAT3 transcriptionally and further escorts STAT3 to shape the oncogenic chromatin landscape and transcriptional program. More importantly, interference of HMGN5 by nanovehicle-packaged siRNA effectively inhibits tumor growth in breast cancer cell-derived xenograft mice model.

IMPLICATIONS

Our findings reveal a novel feed-forward circuit between HMGN5 and STAT3 in promoting breast cancer tumorigenesis and suggest HMGN5 as a novel epigenetic therapeutic target in STAT3-hyperactive breast cancer.

Genomic Analyses for Selective Signatures and Genes Involved in Hot Adaptation Among Indigenous Chickens From Different Tropical Climate Regions

Climate change, especially weather extremes like extreme cold or extreme hot, is a major challenge for global livestock. One of the animal breeding goals for sustainable livestock production should be to breed animals with excellent climate adaptability. Indigenous livestock and poultry are well adapted to the local climate, and they are good resources to study the genetic footprints and mechanism of the resilience to weather extremes. In order to identify selection signatures and genes that might be involved in hot adaptation in indigenous chickens from different tropical climates, we conducted a genomic analysis of 65 indigenous chickens that inhabit different climates. Several important unique positively selected genes (PSGs) were identified for each local chicken group by the cross-population extended haplotype homozygosity (XP-EHH). These PSGs, verified by composite likelihood ratio, genetic differentiation index, nucleotide diversity, Tajima’s D, and decorrelated composite of multiple signals, are related to nerve regulation, vascular function, immune function, lipid metabolism, kidney development, and function, which are involved in thermoregulation and hot adaptation. However, one common PSG was detected for all three tropical groups of chickens via XP-EHH but was not confirmed by other five types of selective sweep analyses. These results suggest that the hot adaptability of indigenous chickens from different tropical climate regions has evolved in parallel by taking different pathways with different sets of genes. The results from our study have provided reasonable explanations and insights for the rapid adaptation of chickens to diverse tropical climates and provide practical values for poultry breeding.

HMGN4 plays a key role in STAT3-mediate oncogenesis of triple-negative breast cancer

High-mobility group nucleosome-binding domain 4 (HMGN4) exerts biological functions by regulating gene transcription through binding with nucleosome. As a new epigenetic regulator discovered in 2001, its biological functions have not been clarified. HMGN4 belongs to HMGNs family, in which HMGN1, 2, and 5 have been reported to play roles in oncogenesis of various cancers. However, it is reported that HMGN4 was associated with thyroid and liver cancer. In this study, we discovered for the first time that HMGN4 was highly expressed in human triple-negative breast cancer (TNBC), based on the analysis of the TCGA database. Moreover, we found that HMGN4 controlled the proliferation of human TNBC cells both in vitro and in vivo. Mechanistically, the positive correlation occurred between HMGN4 and STAT3 downstream genes while HMGN4 played an indispensable role in constitutively active STAT3 (STAT3C) induced colony formation. Interestingly, we reported that STAT3 regulated HMGN4 transcription as its transcriptional factor by ChIP and HMGN4 promoter-luc assays. That is to say, there is a feed-forward signaling circuit between HMGN4 and STAT3, which might control TNBC cell growth. Finally, we proved that the interference of HMGN4 by nanovehicle-packaged siRNA may be a potentially effective approach in TNBC treatment. In summary, our findings not only identified a novel regulator in TNBC cell proliferation but also revealed the mechanism by which HMGN4 acted as a downstream gene of STAT3 to participate in the STAT3 pathway, which indicated that HMGN4 was likely to be a potential novel target for anti-TNBC therapy.

Exploring the underlying molecular mechanism of liver cancer cells under hypoxia based on RNA sequencing

Background

The aim of our study was to use the differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) to illustrate the underlying mechanism of hypoxia in liver cancer.

Methods

In this study, a cell model of hypoxia was established, and autophagy activity was measured with western blotting and transmission electron microscopy. The effect of hypoxia conditions on the invasion of liver cancer cell was evaluated. RNA sequencing was used to identify DEmRNAs and DEmiRNAs to explore the mechanism of hypoxia in liver cancer cells.

Results

We found that autophagy activation was triggered by hypoxia stress and hypoxia might promote liver cancer cell invasion. In addition, a total of 407 shared DEmRNAs and 57 shared DEmiRNAs were identified in both HCCLM3 hypoxia group and SMMC-7721 hypoxia group compared with control group. Furthermore, 278 DEmRNAs and 24 DEmiRNAs were identified as cancer hypoxia-specific DEmRNAs and DEmiRNAs. Finally, we obtained 19 DEmiRNAs with high degree based on the DEmiRNA-DEmRNA interaction network. Among them, hsa-miR-483-5p, hsa-miR-4739, hsa-miR-214-3p and hsa-miR-296-5p may be potential gene signatures related to liver cancer hypoxia.

Conclusions

Our study may help to understand the potential molecular mechanism of hypoxia in liver cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01055-9.

Potential Biomarkers for Liver Cancer Diagnosis Based on Multi-Omics Strategy

Liver cancer is the fourth leading cause of cancer-related death worldwide. Hepatocellular carcinoma (HCC) accounts for about 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients are eligible for curative therapy mainly due to the lack of early-detection strategies, highlighting the significance of reliable and accurate biomarkers. The integration of multi-omics became an important tool for biomarker screening and unique alterations in tumor-associated genes, transcripts, proteins, post-translational modifications and metabolites have been observed. We here summarized the novel biomarkers for HCC diagnosis based on multi-omics technology as well as the clinical significance of these potential biomarkers in the early detection of HCC.

Enabled homolog (ENAH) regulated by RNA binding protein splicing factor 3b subunit 4 (SF3B4) exacerbates the proliferation, invasion and migration of hepatocellular carcinoma cells via Notch signaling pathway

Enabled homolog (ENAH) is an actin-binding protein that implicated in multiple malignant tumors. High ENAH expression has been verified to be associated with poor prognosis in hepatocellular carcinoma (HCC). We aimed to reveal the role of ENAH in HCC and the potential mechanism. ENAH expression in HCC tissues and the prognostic correlation were analyzed by GEPIA2 database. RT-qPCR and Western blot were used to test ENAH expression in HCC cells. Following ENAH silencing, cell proliferation was estimated by CCK-8 and colony formation assays. Transwell and wound healing assays were to assess cell invasion and migration. ENCORI database was to analyze the correlation between ENAH and splicing factor 3b subunit 4 (SF3B4) in HCC tissues, which was then verified by RIP and actinomycin D assay. Then, the expression of Notch signaling-related proteins was detected by Western blotting after ENAH knockdown. Afterward, Notch1 was overexpressed to validate whether ENAH impacted the biological events of HCC cells through mediating Notch signaling. Results revealed that ENAH expression was elevated in HCC tissues and cells and associated with poor prognosis. ENAH deficiency mitigated proliferation, invasion and migration of HCC cells. Mechanistically, ENAH was positively correlated with SF3B4 in HCC tissues. SF3B4 could bind to ENAH mRNA and stabilized ENAH. Besides, ENAH activated Notch signaling. Notch1 up-regulation reversed the influence of ENAH knockdown on biological events of HCC cells. Collectively, ENAH regulated by SF3B4 promoted the development of HCC through activating Notch signaling, which identified ENAH as a potent molecular target for HCC therapy and prognosis.

Novel Molecular Targets for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of death from cancer globally. Indeed, only a few treatments are available, most of which are effective only for the early stages of the disease. Therefore, there is an urgent needing for potential markers for a specifically targeted therapy. Candidate proteins were selected from datasets of The Human Protein Atlas, in order to identify specific tumor-associated proteins overexpressed in HCC samples associated with poor prognosis. Potential epitopes were predicted from such proteins, and homology with peptides derived from viral proteins was assessed. A multiparametric validation was performed, including recognition by PBMCs from HCC-patients and healthy donors, showing a T-cell cross-reactivity with paired epitopes. These results provide novel HCC-specific tumor-associated antigens (TAAs) for immunotherapeutic anti-HCC strategies potentially able to expand pre-existing virus-specific CD8⁺ T cells with superior anticancer efficacy.

Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities

Predicting beneficial and valuable miRNA-disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA-disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA-disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.

Identifying the biomarkers and pathways associated with hepatocellular carcinoma based on an integrated analysis approach

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide. The molecular mechanism underlying HCC is still unclear. In this study, we conducted a comprehensive analysis to explore the genes, pathways and their interactions involved in HCC.

METHODS

We analysed the gene expression datasets corresponding to 488 samples from 10 studies on HCC and identified the genes differentially expressed in HCC samples. Then, the genes were compared against Phenolyzer and GeneCards to screen those potentially associated with HCC. The features of the selected genes were explored by mapping them onto the human protein-protein interaction network, and a subnetwork related to HCC was constructed. Hub genes in this HCC specific subnetwork were identified, and their relevance with HCC was investigated by survival analysis.

RESULTS

We identified 444 differentially expressed genes (177 upregulated and 267 downregulated) related to HCC. Functional enrichment analysis revealed that pathways like p53 signalling and chemical carcinogenesis were eriched in HCC genes. In the subnetwork related to HCC, five disease modules were detected. Further analysis identified six hub genes from the HCC specific subnetwork. Survival analysis showed that the expression levels of these genes were negatively correlated with survival rate of HCC patients.

CONCLUSIONS

Based on a systems biology framework, we identified the genes, pathways, as well as the disease specific network related to HCC. We also found novel biomarkers whose expression patterns were correlated with progression of HCC, and they could be candidates for further investigation.

Deregulated 14-3-3ζ and methionine adenosyltransferase α1 interplay promotes liver cancer tumorigenesis in mice and humans

Methionine adenosyltransferase 1A (MAT1A) is a tumor suppressor downregulated in hepatocellular carcinoma and cholangiocarcinoma, two of the fastest rising cancers worldwide. We compared MATα1 (protein encoded by MAT1A) interactome in normal versus cancerous livers by mass spectrometry to reveal interactions with 14-3-3ζ. The MATα1/14-3-3ζ complex was critical for the expression of 14-3-3ζ. Similarly, the knockdown and small molecule inhibitor for 14-3-3ζ (BV02), and ChIP analysis demonstrated the role of 14-3-3ζ in suppressing MAT1A expression. Interaction between MATα1 and 14-3-3ζ occurs directly and is enhanced by AKT2 phosphorylation of MATα1. Blocking their interaction enabled nuclear MATα1 translocation and inhibited tumorigenesis. In contrast, overexpressing 14-3-3ζ lowered nuclear MATα1 levels and promoted tumor progression. However, tumor-promoting effects of 14-3-3ζ were eliminated when liver cancer cells expressed mutant MATα1 unable to interact with 14-3-3ζ. Taken together, the reciprocal negative regulation that MATα1 and 14-3-3ζ exert is a key mechanism in liver tumorigenesis.

Targeting Antisense lncRNA PRKAG2-AS1, as a Therapeutic Target, Suppresses Malignant Behaviors of Hepatocellular Carcinoma Cells

Objective: Increasing evidence highlights antisense long non-coding RNAs (lncRNAs) as promising therapeutic targets for cancers. Herein, this study focused on the clinical implications and functions of a novel antisense lncRNA PRKAG2-AS1 in hepatocellular carcinoma (HCC).

Methods: PRKAG2-AS1 expression was examined in a cohort of 138 HCC patients by RT-qPCR. Overall survival (OS) and disease-free survival (DFS) analyses were presented based on PRKAG2-AS1 expression, followed by ROCs. After silencing PRKAG2-AS1, cell proliferation was assessed via CCK-8, colony formation and EdU staining assays. Migrated and invasive capacities were assessed by wound healing and transwell assays. The relationships between PRKAG2-AS1, miR-502-3p and BICD2 were validated by luciferase reporter, RIP and RNA pull-down assays. The expression and prognostic value of BICD2 were analyzed in TCGA database.

Results: PRKAG2-AS1 was up-regulated in HCC than normal tissue specimens. High PRKAG2-AS1 expression was indicative of poorer OS and DFS time. Area under the curves (AUCs) for OS and DFS were 0.8653 and 0.7891, suggesting the well predictive efficacy of PRKAG2-AS1 expression. Targeting PRKAG2-AS1 distinctly inhibited proliferation, migration, and invasion in HCC cells. PRKAG2-AS1 was mainly expressed in cytoplasm of HCC cells. PRKAG2-AS1 may directly bind to the sites of miR-502-3p. Up-regulation of BICD2 was found in HCC tissues and associated with unfavorable prognosis. BICD2 was confirmed to be a downstream target of miR-502-3p. PRKAG2-AS1 could regulate miR-502-3p/BICD2 axis.

Conclusion: Our findings identified a novel lncRNA PRKAG2-AS1 that was associated with clinical implications and malignant behaviors. Thus, PRKAG2-AS1 could become a promising therapeutic target.

Changes of protein levels in human urine reflect the dysregulation of signaling pathways of chronic kidney disease and its complications

The increasing prevalence of chronic kidney disease (CKD) seriously is threatening human health and overall quality of life. The discovery of biomarkers of pathogenesis of CKD and the associated complications are very important for CDK diagnosis and treatment. In this paper, urine protein biomarkers were investigated because urine sample collection is convenient and non-invasive. We analyzed the protein concentrations in the urine of CKD patients and extracted abnormal protein signals comparing with the healthy control groups. The enriched signaling pathways that may characterize CKD pathology were identified from these proteins. We applied surface-enhanced laser desorption and ionization time of flight mass spectrometry technology to detect different protein peaks in urine samples from patients with CKD and healthy controls. We searched the proteins corresponding to protein peaks through the UniProt database and identified the signaling pathways of CKD and its complications by using the NIH DAVID database. 42 low abundance proteins and 46 high abundance proteins in the urine samples from CKD patients were found by comparing with healthy controls. Seven KEGG pathways related to CKD and its complications were identified from the regulated proteins. These pathways included chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation, cardiac muscle contraction, Alzheimer's disease, Parkinson's disease, and salivary secretion. In CKD stages 2, 3, 4, and 5, five proteins showed significantly differential abundances. The differential protein signals and regulated signaling pathways will provide new insight for the pathogenesis of CKD and its complications. These altered proteins may also be used as novel biomarkers for the noninvasive and convenient diagnosis methods of CKD and its complications through urine testing in the future.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation) – to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD

PURPOSE

This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD).

MATERIALS AND METHODS

Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens.

RESULTS

A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues.

CONCLUSION

In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in "oxidation reduction," "steroid hormone biosynthesis," "fatty acid metabolic process," and "linoleic acid metabolism." Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.

LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. LncRNA small nucleolar RNA host gene 14 (SNHG14) functions as an oncogene in a variety of cancers. However, the role of SNHG14 in HCC remains elusive. The aim of this study is to unravel the functional role and regulatory mechanism of SNHG14 in HCC. A cohort of 40 HCC tumor tissues and paired adjacent normal tissues were collected. Histopathological changes were analyzed by hematoxylin and eosin and immunohistochemistry. qRT-PCR and western blotting were performed to determine the levels of SNHG14, PABPC1, and PTEN signaling molecules. CCK-8, immunofluorescence, and colony formation assays were conducted to monitor cell proliferation. Wound healing and tube formation assays were employed to determine cell migration and angiogenesis. ChIP assay was performed to investigate the enrichment of H3K27 acetylation in PABPC1 promoter. Xenograft mice model was constructed to further verify the SNHG14/PABPC1 axis in vivo. SNHG14 was highly expressed in HCC tissues and cells, which promoted cell proliferation, migration, and angiogenesis in Hep3B and HepG2 cells. PABPC1 functioned as a downstream effector of SNHG14. SNHG14 dramatically induced upregulation of PABPC1 via H3K27 acetylation. In addition, SNHG14/PABPC1 promoted cell proliferation and angiogenesis via PTEN signaling pathway in vitro and in vivo. SNHG14 promoted cell proliferation and angiogenesis via upregulating PABPC1 through H3K27 acetylation and modulating PTEN signaling in the tumorigenesis of HCC.

Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma

In this study, we identified prognostic biomarkers in ovarian carcinoma by integrating multi-omics DNA copy number variation (CNV) and methylation variation (MET) data. CNV, MET, and messenger RNA (mRNA) expression were examined in 351 ovarian carcinoma patients. Genes for which expression was correlated with DNA copy-number or DNA methylation were identified; three ovarian carcinoma gene subtypes were defined based on these correlations. Overall survival and B cell scores were lower, while the macrophage cell score was higher, in the DNA imprinting centre 1 (iC1) subtype than in the iC2 and iC3 subtypes. Comparison of CNV, MET, and mRNA expression among the subtypes identified two genes, ubiquitin B (UBB) and interleukin 18 binding protein (IL18BP), that were associated with prognosis. Mutation spectrum results based on subtype indicated that UBB and IL18BP expression may be influenced by mutation loci. Mutation levels were higher in iC1 samples than in iC2 or iC3 samples, indicating that the iC1 subtype is associated with disease progression. This integrated multi-omics analysis of genomics, epigenomics, and transcriptomics provides new insight into the molecular mechanisms of ovarian carcinoma and may help identify biomolecular markers for early disease diagnosis.

The Underlying Mechanisms of Noncoding RNAs in the Chemoresistance of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal human malignancies. Chemotherapeutic agents, such as sorafenib and lenvatinib, can improve the outcomes of HCC patients. Nevertheless, chemoresistance has become a major hurdle in the effective treatment of HCC. Noncoding RNAs (ncRNAs), including mircoRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have been demonstrated to participate in the onset and progression of HCC. Moreover, multiple lines of evidence have indicated that ncRNAs also play a pivotal role in HCC drug resistance. ncRNAs can regulate drug efflux and metabolism, glucose metabolism, cellular death pathways, and malignant characteristics in HCC. A deeper understanding of the molecular mechanisms responsible for ncRNA-mediated drug resistance in HCC will provide new opportunities for improving the treatment of HCC. In this review, we summarize recent findings on the molecular mechanisms by which ncRNAs regulate HCC chemoresistance, as well as their potential clinical implications in overcoming HCC chemoresistance.

Advances in the early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers globally. In contrast to the declining death rates observed for all other common cancers such as breast, lung, and prostate cancers, the death rates for HCC continue to increase by ~2–3% per year because HCC is frequently diagnosed late and there is no curative therapy for an advanced HCC. The early diagnosis of HCC is truly a big challenge. Over the past years, the early diagnosis of HCC has relied on surveillance with ultrasonography (US) and serological assessments of alpha-fetoprotein (AFP). However, the specificity and sensitivity of US/AFP is not satisfactory enough to detect early onset HCC. Recent technological advancements offer hope for early HCC diagnosis. Herein, we review the progress made in HCC diagnostics, with a focus on emerging imaging techniques and biomarkers for early disease diagnosis.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

Current status of hepatocellular carcinoma detection: screening strategies and novel biomarkers

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide. Delayed diagnosis is a major factor responsible for the poor prognosis of HCC. Several advances have been made in the field of liver imaging with the use of novel imaging contrasts, improving current imaging techniques with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI), introduction of new technologies such as contrast liver ultrasound, and development of novel biomarkers with the goal of early detection of HCC and improving outcomes of patients with HCC. This review focuses on current surveillance strategies and development of biomarkers with the goal of early detection of HCC.