RAMP3 is a prognostic indicator of liver cancer and might reduce the adverse effect of TP53 mutation on survival

Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma

PURPOSE

Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited.

METHODS

The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions.

RESULTS

A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan-Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments.

CONCLUSION

The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.

scBPGRN: Integrating single-cell multi-omics data to construct gene regulatory networks based on BP neural network

The deterioration and metastasis of cancer involve various aspects of genomic changes, including genomic DNA changes, epigenetic modifications, gene expression, and other complex interactions. Therefore, integrating single-cell multi-omics data to construct gene regulatory networks containing more omics information is of great significance for understanding the pathogenesis of cancer. In this article, an algorithm integrating single-cell RNA sequencing data and DNA methylation data to construct a gene regulatory network based on the back-propagation (BP) neural network (scBPGRN) is proposed. This algorithm uses biweight extreme correlation coefficients to measure the correlation between factors and uses neural networks to calculate generalized weights to construct gene regulation networks. Finally, the node strength is calculated to identify the genes associated with cancer. We apply the scBPGRN algorithm to hepatocellular carcinoma (HCC) data. We construct a regulatory network and identify top-ranked genes, such as MYCBP, KLHL35, PRKCZ, and SERPINA6, as the key HCC-related genes. We analyze the top 100 genes, and the HCC-related genes are concentrated in the top 20. In addition, the single cell data is found to consist of two subpopulations. We also apply scBPGRN to two subpopulations. We analyze the top 50 genes in them, and the HCC-related genes are concentrated in the top 20. The consequences of functional enrichment analysis indicate that the gene regulatory network we have constructed is valid. Our results have been verified in several pieces of literature. This study provides a reference for the integration of single-cell multi-omics data to construct gene regulatory networks.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.

Identification and Validation of a Novel Tumor Microenvironment-Related Prognostic Signature of Patients With Hepatocellular Carcinoma

Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients.

Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by the weighted gene co-expression network analysis (WGCNA). A TME score-related prognostic model was constructed and patients were divided into a high-risk group and a low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with the external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram’s ability to predict prognosis was assessed by ROC, calibration curve, and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) were conducted to explore the underlying biological functions and pathways of this risk signature. Moreover, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs), and drug sensitivity were assessed. Finally, real-time PCR was used to verify the gene expression levels in normal liver cells and cancer cells.

Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p < 0.01). WGCNA demonstrated that the yellow module was significantly correlated with the TME score. The 5-genes TME risk signature was built to predict the prognosis of patients with HCC including DAB2, IL18RAP, RAMP3, FCER1G, and LHFPL2. Patients with a low-risk score have higher levels of tumor-infiltrating immune cells and higher expression of immune checkpoints, which may be more sensitive to immunotherapy.

Conclusion: It provided a theoretical basis for predicting the prognosis and personalized treatment of patients with HCC.

Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma

OBJECTIVE

Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity-related subtypes of hepatocellular carcinoma (HCC) have not been identified.

METHODS

Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8-gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis.

RESULTS

C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low-risk group, the high-risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response.

CONCLUSION

We identified HCC subtypes from the perspective of metabolism-related pathway activity and proposed a robust prognostic signature for HCC.

Cell Differentiation Trajectory in Liver Cirrhosis Predicts Hepatocellular Carcinoma Prognosis and Reveals Potential Biomarkers for Progression of Liver Cirrhosis to Hepatocellular Carcinoma

Most hepatocellular carcinoma (HCC) patients occur on a background of liver cirrhosis, the molecular mechanisms of liver cirrhosis and its progression to HCC remain to be fully elucidated. Single cell differentiation trajectory analysis has been used in cell classification and tumor molecular typing, which correlated with disease progression and patient prognosis. Here we use cell differentiation trajectory analysis to investigate the relevance of liver cirrhosis and HCC. Single-cell RNA sequencing (scRNA-seq) data of liver cirrhosis and bulk RNA-seq and clinical data of HCC were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) for analysis. HCC samples were divided into three subtypes, based on differentiation-related genes (DRGs) of liver cirrhosis, each with a different expression profile and overall survival (OS). A two- DRGs (CD34 and RAMP3) based prognostic risk scoring (RS) signature was established which could differentiate OS between high-risk and low-risk groups. And expression levels of CD34 and RAMP3 were predominantly high in endothelial cells. By integrating the RS and clinicopathological features, a nomogram was constructed and can accurately predicted the 1-year, 3-years, and 5-years OS. In conclusion, cell differentiation trajectory of liver cirrhosis can predict the prognosis of HCC, and provides new perspectives on the mechanisms of progression of liver cirrhosis to HCC.

Transcript levels of spindle and kinetochore-associated complex 1/3 as prognostic biomarkers correlated with immune infiltrates in hepatocellular carcinoma

The spindle and kinetochore-associated protein complex (Ska) is an essential component in chromosome segregation. It comprises three proteins (Ska1, Ska2, and Ska3) with theorized roles in chromosomal instability and tumor development, and its overexpression has been widely reported in a variety of tumors. However, the prognostic significance and immune infiltration of Ska proteins in hepatocellular carcinoma (HCC) are not completely understood. The bioinformatics tools Oncomine, UALCAN, gene expression profiling interactive analysis 2 (GEPIA2), cBioPortal, GeneMANIA, Metascape, and TIMER were used to analyze differential expression, prognostic value, genetic alteration, and immune cell infiltration of the Ska protein complex in HCC patients. We found that the mRNA expression of the Ska complex was markedly upregulated in HCC. High expression of the Ska complex is closely correlated with tumor stage, patient race, tumor grade, and TP53 mutation status. In addition, high expression of the Ska complex was significantly correlated with poor disease-free survival, while the high expression levels of Ska1 and Ska3 were associated with shorter overall survival. The biological functions of the Ska complex in HCC primarily involve the amplification of signals from kinetochores, the mitotic spindle, and (via a MAD2 invasive signal) unattached kinetochores. Furthermore, the expression of the complex was positively correlated with tumor-infiltrating cells. These results may provide new insights into the development of immunotherapeutic targets and prognostic biomarkers for HCC.

EGR1 as a potential marker of prognosis in extranodal NK/T-cell lymphoma

Extranodal natural killer T-cell lymphoma (ENKTL) is an aggressive malignancy with a dismal prognosis. In the present study, gene expression profiling was performed to provide more information on ENKTL molecular signature and offer a rationale for further investigation of prognostic markers in ENKTL. NanoString nCounter Analysis encompassing 133 target genes was used to compare gene expression levels of 43 ENKTL tumor samples. The majority of the patients were under 60 years of age (79.1%); 32 (74.4%) patients had nasal type ENKTL and 23 patients (53.5%) had intermediate/high risk ENKTL based on the prognostic index for natural killer cell lymphoma (PINK). The median follow-up was 15.9 months and the median overall survival (OS) was 16.1 months (95% CI 13.0-69.8). EGR1 upregulation was consistently identified in the localized stage with a low risk of prognostic index based on the PINK. Among the six significantly relevant genes for EGR1 expression, high expression levels of genes, including CD59, GAS1, CXCR7, and RAMP3, were associated with a good survival prognosis. The in vitro test showed EGR1 modulated the transcriptional activity of the target genes including CD59, GAS1, CXCR7, and RAMP3. Downregulation of EGR1 and its target genes significantly inhibited apoptosis and decreased chemosensitivity and attenuated radiation-induced apoptosis. The findings showed EGR1 may be a candidate for prognostic markers in ENKTL. Considerable additional characterization may be necessary to fully understand EGR1.

Cancer-Associated Fibroblasts Suppress Cancer Development: The Other Side of the Coin

Cancer-associated fibroblasts (CAFs) are the main stromal components of cancer, representing a group of heterogeneous cells. Many studies indicate that CAFs promote tumor development. Besides, evidence of the tumor suppression effects of CAFs keeps on merging. In the tumor microenvironment, multiple stimuli can activate fibroblasts. Notably, this does not necessarily mean the activated CAFs become strong tumor promoters immediately. The varying degree of CAFs activation makes quiescent CAFs, tumor-restraining CAFs, and tumor-promoting CAFs. Quiescent CAFs and tumor-restraining CAFs are more present in early-stage cancer, while comparatively, more tumor-promoting CAFs present in advanced-stage cancer. The underlying mechanism that balances tumor promotion or tumor inhibition effects of CAFs is mostly unknown. This review focus on the inhibitory effects of CAFs on cancer development. We describe the heterogeneous origin, markers, and metabolism in the CAFs population. Transgenetic mouse models that deplete CAFs or deplete CAFs activation signaling in the tumor stroma present direct evidence of CAFs protective effects against cancer. Moreover, we outline CAFs subpopulation and CAFs derived soluble factors that act as a tumor suppressor. Single-cell RNA-sequencing on CAFs population provides us new insight to classify CAFs subsets. Understanding the full picture of CAFs will help translate CAFs biology from bench to bedside and develop new strategies to improve precision cancer therapy.

Metabolic Regulator IAPP (Amylin) Is Required for BRAF and RAS Oncogene-Induced Senescence

Cellular senescence is characterized by a prolonged and predominantly irreversible cell-cycle arrest state, which is linked to loss of tissue function and aging in mammals. Moreover, in response to aberrant oncogenic signals such as those from oncogenic RAS or BRAF, senescence functions as an intrinsic tumor suppressor mechanism restraining tumor progression. In addition to this durable proliferative block, senescent cells adopt altered morphologies, transcriptional profiles, and metabolism, while often possessing unusual heterochromatin formation termed senescence-associated heterochromatic foci. To uncover genes that are required to permit proliferation in the face of sustained oncogene signaling, we conducted an shRNA-based genetic screen in primary cells expressing inducible BRAF. Here we show that depletion of a known glycolysis regulator, islet amylin polypeptide (IAPP also known as amylin), prevents RAS and BRAF oncogene-induced senescence (OIS) in human cells. Importantly, depletion of IAPP resulted in changes of the cells' metabolome and this metabolic reprogramming was associated with widespread alterations in chromatin modifications compared with senescent cells. Conversely, exogenous treatment of IAPP-depleted cells with amylin restored OIS. Together, our results demonstrate that the metabolic regulator IAPP is important regulator of OIS. Moreover, they suggest that IAPP analog treatment or activation of IAPP signaling in RAS/BRAF mutant tumors may have therapeutic potential through senescence induction. IMPLICATIONS: These findings demonstrate that IAPP is a novel metabolic regulator of oncogene-induced senescence and use of IAPP analogs may be therapeutically effective to restore growth arrest to BRAF and/or RAS mutant cancers.

Transcriptional and epigenetic landscape of Ca2+-signaling genes in hepatocellular carcinoma

Calcium (Ca²⁺) signaling has a major role in regulating a wide range of cellular mechanisms, including gene expression, proliferation, metabolism, cell death, muscle contraction, among others. Recent evidence suggests that ~ 1600 genes are related to the Ca²⁺ signaling. Some of these genes' expression is altered in several pathological conditions, including different cancer types, and epigenetic mechanisms are involved. However, their expression and regulation in hepatocellular carcinoma (HCC) and the liver are barely known. Here, we aimed to explore the expression of genes involved in the Ca²⁺-signaling in HCC, liver regeneration, and hepatocyte differentiation, and whether their expression is regulated by epigenetic mechanisms such as DNA methylation and histone posttranslational modifications (HPM). Results show that several Ca²⁺-signaling genes' expression is altered in HCC samples; among these, a subset of twenty-two correlate with patients' survival. DNA methylation correlates with eight of these genes' expression, and Guadecitabine, a hypomethylating agent, regulates the expression of seven down-regulated and three up-regulated genes in HepG2 cells. The down-regulated genes displayed a marked decrease of euchromatin histone marks, whereas up-regulated genes displayed gain in these marks. Additionally, the expression of these genes is modulated during liver regeneration and showed similar profiles between in vitro differentiated hepatocytes and liver-derived hepatocytes. In conclusion, some components of the Ca²⁺-signaling are altered in HCC and displayed a correlation with patients' survival. DNA methylation and HMP are an attractive target for future investigations to regulate their expression. Ca²⁺-signaling could be an important regulator of cell proliferation and differentiation in the liver.

PLA2G16 is a mutant p53/KLF5 transcriptional target and promotes glycolysis of pancreatic cancer

PLA2G16 is a member of the phospholipase family that catalyses the generation of lysophosphatidic acids (LPAs) and free fatty acids (FFAs) from phosphatidic acid. In the current study, we explored the functional role of PLA2G16 in pancreatic adenocarcinoma (PAAD) and the genetic/epigenetic alterations leading to its dysregulation. Bioinformatic analysis was performed using data from The Cancer Genome Atlas (TCGA), Genotype‐Tissue Expression (GTEx) and the Human Protein Atlas (HPA). Then, PANC‐1 and MIA‐PaCa‐2 cells harbouring TP53 mutations were used for cellular and animal studies. Results showed that PL2G16 expression was significantly up‐regulated in PAAD tissue and was associated with unfavourable survival. PLA2G16 inhibition suppressed pancreatic cell growth in vitro and in vivo and also inhibited aerobic glycolysis. Bioinformatic analysis indicated that KLF5 was positively correlated with PLA2G16 expression in PAAD tumours with TP53 mutation. TP53 or KLF5 inhibition significantly reduced PLA2G16 expression at both mRNA and protein levels. Dual‐luciferase and chromatin Immunoprecipitation‐quantitative polymerase chain reaction assays showed that KLF5 directly bound to the PLA2G16 promoter and activated its transcription. Co‐immunoprecipitation assay indicated that mutant p53 had a physical interaction with KLF5. Inhibition of mutant p53 impaired the transcriptional activating effects of KLF5. In PAAD cases in TCGA, PLA2G16 expression was positively correlated with its copy number (Pearson's r = 0.51, P < 0.001), but was strongly and negatively correlated with the methylation level of cg09518969 (Pearson's r = −0.64, P < 0.001), a 5’‐cytosine‐phosphodiester bond‐guanine‐3’ site within its gene locus. In conclusion, this study revealed a novel mutant p53/KLF5‐PLA2G16 regulatory axis on tumour growth and glycolysis in PAAD.

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles

Background

Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.

Methods

The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage (stage-II, Stage-III and stage-IV) and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.

Results

In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.

Conclusion

Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

RAMP3 deficiency enhances postmenopausal obesity and metabolic disorders

There is a marked increase in the incidence of visceral adiposity and insulin resistance among women following menopause. Adrenomedullin (AM) is an endogenous peptide first identified as a vasodilator, but now known to exert a variety of physiological effects. RAMP3 is a receptor activity-modifying protein that binds to the AM receptor (calcitonin receptor-like receptor). As expression of both AM and RAMP3 is reportedly activated by estrogen, we hypothesized that RAMP3 is crucially involved in the pathophysiology of postmenopausal obesity. To test this idea, we compared the effects of ovariectomy (OVX) and a high-fat diet for 10 weeks (a model of postmenopausal obesity) between RAMP3 knockout (RAMP3-/-) and wild-type mice. RAMP3-/- OVX mice exhibited greater obesity and adipose tissue weight gain as compared to wild-type OVX mice. RAMP3-/- OVX mice also exhibited higher serum insulin levels. In periuterine WAT from RAMP3-/- OVX mice, expression of lipolysis-related factors was lower and expression of inflammation-related factors was higher than in wild-type OVX mice. Hepatic steatosis was also exacerbated in RAMP3-/- OVX. Notably, expression of the membrane-type estrogen receptor GPR30 was downregulated in periuterine WAT from RAMP3-/- OVX mice. These findings raise the possibility that a GPR30-RAMP3 interaction is involved in the pathophysiology of postmenopausal obesity and suggest RAMP3 plays a key role in the regulation of energy metabolism and exerts a hepatoprotective effect in this model of postmenopausal obesity. RAMP3 may thus be a useful therapeutic target for treatment of postmenopausal obesity and metabolic disorders.