Analysis of Matched Tumor and Normal Profiles Reveals Common Transcriptional and Epigenetic Signals Shared across Cancer Types

COADREADx: A comprehensive algorithmic dissection of colorectal cancer unravels salient biomarkers and actionable insights into its discrete progression

Background

Colorectal cancer is a common condition with an uncommon burden of disease, heterogeneity in manifestation, and no definitive treatment in the advanced stages. Renewed efforts to unravel the genetic drivers of colorectal cancer progression are paramount. Early-stage detection contributes to the success of cancer therapy and increases the likelihood of a favorable prognosis. Here, we have executed a comprehensive computational workflow aimed at uncovering the discrete stagewise genomic drivers of colorectal cancer progression.

Methods

Using the TCGA COADREAD expression data and clinical metadata, we constructed stage-specific linear models as well as contrast models to identify stage-salient differentially expressed genes. Stage-salient differentially expressed genes with a significant monotone trend of expression across the stages were identified as progression-significant biomarkers. The stage-salient genes were benchmarked using normals-augmented dataset, and cross-referenced with existing knowledge. The candidate biomarkers were used to construct the feature space for learning an optimal model for the digital screening of early-stage colorectal cancers. The candidate biomarkers were also examined for constructing a prognostic model based on survival analysis.

Results

Among the biomarkers identified are: CRLF1, CALB2, STAC2, UCHL1, KCNG1 (stage-I salient), KLHL34, LPHN3, GREM2, ADCY5, PLAC2, DMRT3 (stage-II salient), PIGR, HABP2, SLC26A9 (stage-III salient), GABRD, DKK1, DLX3, CST6, HOTAIR (stage-IV salient), and CDH3, KRT80, AADACL2, OTOP2, FAM135B, HSP90AB1 (top linear model genes). In particular the study yielded 31 genes that are progression-significant such as ESM1, DKK1, SPDYC, IGFBP1, BIRC7, NKD1, CXCL13, VGLL1, PLAC1, SPERT, UPK2, and interestingly three members of the LY6G6 family. Significant monotonic linear model genes included HIGD1A, ACADS, PEX26, and SPIB. A feature space of just seven biomarkers, namely ESM1, DHRS7C, OTOP3, AADACL2, LPHN3, GABRD, and LPAR1, was sufficient to optimize a RandomForest model that achieved > 98% balanced accuracy (and performant recall) of cancer vs. normal on external validation. Design of an optimal multivariate model based on survival analysis yielded a prognostic panel of three stage-IV salient genes, namely HOTAIR, GABRD, and DKK1. Based on the above sparse signatures, we have developed COADREADx, a web-server for potentially assisting colorectal cancer screening and patient risk stratification. COADREADx provides uncertainty measures for its predictions and needs clinical validation. It has been deployed for experimental non-commercial use at: https://apalanialab.shinyapps.io/coadreadx/.

Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers

Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived "healthy" normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min - 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact "differential" expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.

A Nine-Gene Expression Signature Distinguished a Patient with Chronic Lymphocytic Leukemia Who Underwent Prolonged Periodic Fasting

Background and Objectives: This study aimed to investigate the causes of continuous deep fluctuations in the absolute lymphocyte count (ALC) in an untreated patient with Chronic Lymphocytic Leukemia (CLL), who has had a favorable prognosis since the time of diagnosis. Up until now, the patient has voluntarily chosen to adopt a predominantly vegetarian and fruitarian diet, along with prolonged periods of total fasting (ranging from 4 to 39 days) each year. Materials and Methods: For this purpose, we decided to analyze the whole transcriptome profiling of peripheral blood (PB) CD19+ cells from the patient (#1) at different time-points vs. the same cells of five other untreated CLL patients who followed a varied diet. Consequently, the CLL patients were categorized as follows: the 1st group comprised patient #1 at 20 different time-points (16 time-points during nutrition and 4 time-points during fasting), whereas the 2nd group included only one time point for each of the patients (#2, #3, #4, #5, and #6) as they followed a varied diet. We performed microarray experiments using a powerful tool, the Affymetrix Human Clariom™ D Pico Assay, to generate high-fidelity biomarker signatures. Statistical analysis was employed to identify differentially expressed genes and to perform sample clustering. Results: The lymphocytosis trend in patient #1 showed recurring fluctuations since the time of diagnosis. Interestingly, we observed that approximately 4-6 weeks after the conclusion of fasting periods, the absolute lymphocyte count was reduced by about half. The gene expression profiling analysis revealed that nine genes were statistically differently expressed between the 1st group and the 2nd group. Specifically, IGLC3, RPS26, CHPT1, and PCDH9 were under expressed in the 1st group compared to the 2nd group of CLL patients. Conversely, IGHV3-43, IGKV3D-20, PLEKHA1, CYBB, and GABRB2 were over-expressed in the 1st group when compared to the 2nd group of CLL patients. Furthermore, clustering analysis validated that all the samples from patient #1 clustered together, showing clear separation from the samples of the other CLL patients. Conclusions: This study unveiled a small gene expression signature consisting of nine genes that distinguished an untreated CLL patient who followed prolonged periods of total fasting, maintaining a gradual growth trend of lymphocytosis, compared to five untreated CLL patients with a varied diet. Future investigations focusing on patient #1 could potentially shed light on the role of prolonged periodic fasting and the implication of this specific gene signature in sustaining the lymphocytosis trend and the favorable course of the disease.

DNA methylation entropy as a measure of stem cell replication and aging

BACKGROUND

Epigenetic marks are encoded by DNA methylation and accumulate errors as organisms age. This drift correlates with lifespan, but the biology of how this occurs is still unexplained. We analyze DNA methylation with age in mouse intestinal stem cells and compare them to nonstem cells.

RESULTS

Age-related changes in DNA methylation are identical in stem and nonstem cells, affect most prominently CpG islands and correlate weakly with gene expression. Age-related DNA methylation entropy, measured by the Jensen-Shannon Distribution, affects up to 25% of the detectable CpG sites and is a better measure of aging than individual CpG methylation. We analyze this entropy as a function of age in seven other tissues (heart, kidney, skeletal muscle, lung, liver, spleen, and blood) and it correlates strikingly with tissue-specific stem cell division rates. Thus, DNA methylation drift and increased entropy with age are primarily caused by and are sensors for, stem cell replication in adult tissues.

CONCLUSIONS

These data have implications for the mechanisms of tissue-specific functional declines with aging and for the development of DNA-methylation-based biological clocks.

Gamma-aminobutyric Acid Type A Receptor Subunit Delta as a Potential Therapeutic Target in Gastric Cancer

BACKGROUND

Novel therapeutic targets are needed to improve the poor prognosis of patients with advanced gastric cancer. The aim of this study was to identify a novel therapeutic target for the treatment of GC and to investigate the potential therapeutic value of an antibody raised against the target.

METHODS

We identified gamma-aminobutyric acid type A receptor subunit delta as a candidate therapeutic target by differential transcriptome analysis of metastatic GC tissue and adjacent nontumor tissues. GABRD mRNA levels were analyzed in 230 pairs of gastric tissue by quantitative reverse-transcription polymerase chain reaction. GABRD function was assessed in proliferation, invasion, and apoptosis assays in human GC cell lines expressing control or GABRD-targeting small interfering RNA (siRNA). Mouse anti-human polyclonal GABRD antibodies were generated and assessed for inhibition of GC cell growth in vitro and in a mouse xenograft model of peritoneal GC dissemination.

RESULTS

High GABRD mRNA expression level in primary human GC tissue was associated with poor prognosis. Expression of siGABRD in GC cell lines significantly decreased cell proliferation and invasion and increased apoptosis compared with control siRNA expression. Anti-GABRD polyclonal antibodies inhibited GC cell proliferation in vitro and decreased peritoneal tumor nodule size in the mouse xenograft model.

CONCLUSION

We identified GABRD as novel regulator of GC cell growth and function. GABRD is upregulated in GC tissue and is associated with poor prognosis, suggesting that it may be a potential therapeutic target for GC.

Transcriptome Analyses Identify Deregulated MYC in Early Onset Colorectal Cancer

Despite a global decrease in colorectal cancer (CRC) incidence, the prevalence of early-onset colorectal cancer (EOCRC), or those occurring in individuals before the age of 50, has steadily increased over the past several decades. When compared to later onset colorectal cancer (LOCRC) in individuals over 50, our understanding of the genetic and molecular underpinnings of EOCRCs is limited. Here, we conducted transcriptomic analyses of patient-matched normal colonic segments and tumors to identify gene expression programs involved in carcinogenesis. Amongst differentially expressed genes, we found increased expression of the c-MYC proto-oncogene (MYC) and its downstream targets in tumor samples. We identified tumors with high and low differential MYC expression and found patients with high-MYC tumors were older and overweight or obese. We also detected elevated expression of the PVT1 long-non-coding RNA (lncRNA) in most tumors and found gains in copy number for both MYC and PVT1 gene loci in 35% of tumors evaluated. Our transcriptome analyses indicate that EOCRC can be sub-classified into groups based on differential MYC expression and suggest that deregulated MYC contributes to CRCs that develop in younger patients.

Therapeutic targets and functions of curcumol against COVID-19 and colon adenocarcinoma

Since 2019, the coronavirus disease (COVID-19) has caused 6,319,395 deaths worldwide. Although the COVID-19 vaccine is currently available, the latest variant of the virus, Omicron, spreads more easily than earlier strains, and its mortality rate is still high in patients with chronic diseases, especially cancer patients. So, identifying a novel compound for COVID-19 treatment could help reduce the lethal rate of the viral infection in patients with cancer. This study applied network pharmacology and systematic bioinformatics analysis to determine the possible use of curcumol for treating colon adenocarcinoma (COAD) in patients infected with COVID-19. Our results showed that COVID-19 and COAD in patients shared a cluster of genes commonly deregulated by curcumol. The clinical pathological analyses demonstrated that the expression of gamma-aminobutyric acid receptor subunit delta (GABRD) was associated with the patients' hazard ratio. More importantly, the high expression of GABRD was associated with poor survival rates and the late stages of COAD in patients. The network pharmacology result identified seven-core targets, including solute carrier family 6 member 3, gamma-aminobutyric acid receptor subunit pi, butyrylcholinesterase, cytochrome P450 3A4, 17-beta-hydroxysteroid dehydrogenase type 2, progesterone receptor, and GABRD of curcumol for treating patients with COVID-19 and COAD. The bioinformatic analysis further highlighted their importance in the biological processes and molecular functions in gland development, inflammation, retinol, and steroid metabolism. The findings of this study suggest that curcumol could be an alternative compound for treating patients with COVID-19 and COAD.

Clinical Data and Biocalculation Methods of GABRD Determine the Clinical Characteristics and Immune Relevance of Colorectal Cancer

Background

The aim of this study was to clarify the expression of gamma-aminobutyric acid type A receptor delta subunit (GABRD) gene in pan-cancer and its correlation with patient prognosis, and to investigate the function and possible mechanism of GABRD in colorectal cancer (CRC).

Methods

The Cancer Genome Atlas (TCGA) data were used to analyze the expression differences of GABRD in pan-cancer, and the correlation between GABRD and clinical prognosis of various tumors was analyzed by Cox regression method. According to the expression level of GABRD, Gene Function Annotation (GO) and Kyoto Encyclopedia of Genomes (KEGG) functional enrichment analysis were performed on the differentially expressed genes. Expression of GABRD gene and 44 marker genes of three types of RNA modification (m1A (10), m5C (13), m6A (21)) genes in different tumors was observed. Pearson correlation of GABRD gene and marker genes of five immune pathways was measured.

Results

: TCGA data analysis showed that GABRD was significantly upregulated in various tumor tissues, especially COAD and READCOAD. Survival analysis showed that GABRD was a prognostic protective factor in CRC (p < 0.001). The results of survival nomogram showed that GABRD, age, and tumor (T) lymph node (N) distant metastasis (M) stage were independent prognostic factors, and the survival model C-index was 0.724 (0.644-1). Gene enrichment and functional analysis showed that GABRD may be related to protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response. The expression of GABRD was positively correlated in m1A-, m5C-, and m6A-related genes. The GABRD gene was found in B cell, T cell CD4, T cell CD8, neutrophil, macrophage in TCGA-COAD (N = 282), and TCGA-COADREAD (N = 373). The infiltration level and DC was significantly positively correlated (p < 0.05). Also, the Pearson correlation coefficient is the largest.

Conclusion

The involvement of GABRD in the occurrence and development of CRC may be related to protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response. GABRD can be used as a molecular marker for the prognosis of CRC.

DEPDC1B collaborates with GABRD to regulate ESCC progression

Background

Esophageal squamous cell carcinoma (ESCC) is the leading cause of cancer-related death worldwide with a poor prognosis. Given that DEPDC1B plays a key role in multiple cancers, the role of this molecule in ESCC was explored to identify potential targets for ESCC patients.

Method

The expression level of DEPDC1B in ESCC was revealed based on the TCGA database and immunohistochemical experiments on clinical tissues. The correlation between DEPDC1B and survival of ESCC patients was analyzed by Kaplan–Meier method. Small hairpin RNA (shRNA)-mediated silencing of DEPDC1B expression in ESCC cells and performed a series of in vitro and in vivo functional validations.

Result

DEPDC1B was overexpressed in ESCC. High expression of DEPDC1B was significantly negatively correlated with overall survival in patients with ESCC. Moreover, knockdown of DEPDC1B inhibited ESCC cell proliferation, clone formation, migration, tumor formation and promoted apoptosis. Furthermore, knockdown of DEPDC1B leaded to significant downregulation of GABRD in ESCC cells. Meanwhile, GABRD expression was upregulated in ESCC, and its silencing can inhibit the proliferation and migration of the tumor cells. Interestingly, there was a protein interaction between DEPDC1B and GABRD. Functionally, GABRD knockdown partially reversed the contribution of DEPDC1B to ESCC progression. In addition, GABRD regulated ESCC progression may depend on PI3K/AKT/mTOR signaling pathway.

Conclusion

DEPDC1B collaborated with GABRD to regulate ESCC progression, and inhibition of this signaling axis may be a potential therapeutic target for ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02593-z.

GABRB2, a key player in neuropsychiatric disorders and beyond

The GABA receptors represent the main inhibitory system in the central nervous system that ensure synaptogenesis, neurogenesis, and the regulation of neuronal plasticity and learning. GABAA receptors are pentameric in structure and belong to the Cys-loop superfamily. The GABRB2 gene, located on chromosome 5q34, encodes the β2 subunit that combines with the α and γ subunits to form the major subtype of GABAA receptors, which account for 43% of all GABAA receptors in the mammalian brain. Each subunit probably consists of an extracellular N-terminal domain, four membrane-spanning segments, a large intracellular loop between TM3 and TM4, and an extracellular C-terminal domain. Alternative splicing of the RNA transcript of the GABRB2 gene gives rise at least to four long and short isoforms with dissimilar electrophysiological properties. Furthermore, GABRB2 is imprinted and subjected to epigenetic regulation and positive selection. It has been associated with schizophrenia first in Han Chinese, and subsequently validated in other populations. Gabrb2 knockout mice also exhibited schizophrenia-like behavior and neuroinflammation that were ameliorated by the antipsychotic drug risperidone. GABRB2 was also associated with other neuropsychiatric disorders including bipolar disorder, epilepsy, autism spectrum disorder, Alzheimer's disease, frontotemporal dementia, substance dependence, depression, internet gaming disorder, and premenstrual dysphoric disorder. Recently, it has been postulated that GABRB2 might be a potential marker for different cancer types. As GABRB2 has a pivotal role in the central nervous system and is increasingly recognized to contribute to human diseases, further understanding of its structure and function may expedite the generation of new therapeutic approaches.

MicroRNA Analysis of Human Stroke Brain Tissue Resected during Decompressive Craniectomy/Stroke-Ectomy Surgery

BACKGROUND

Signaling pathways mediated by microRNAs (miRNAs) have been identified as one of the mechanisms that regulate stroke progression and recovery. Recent investigations using stroke patient blood and cerebrospinal fluid (CSF) demonstrated disease-specific alterations in miRNA expression. In this study, for the first time, we investigated miRNA expression signatures in freshly removed human stroke brain tissue.

METHODS

Human brain samples were obtained during craniectomy and brain tissue resection in severe stroke patients with life-threatening brain swelling. The tissue samples were subjected to histopathological and immunofluorescence microscopy evaluation, next generation miRNA sequencing (NGS), and bioinformatic analysis.

RESULTS

miRNA NGS analysis detected 34 miRNAs with significantly aberrant expression in stroke tissue, as compared to non-stroke samples. Of these miRNAs, 19 were previously identified in stroke patient blood and CSF, while dysregulation of 15 miRNAs was newly detected in this study. miRNA direct target gene analysis and bioinformatics approach demonstrated a strong association of the identified miRNAs with stroke-related biological processes and signaling pathways.

CONCLUSIONS

Dysregulated miRNAs detected in our study could be regarded as potential candidates for biomarkers and/or targets for therapeutic intervention. The results described herein further our understanding of the molecular basis of stroke and provide valuable information for the future functional studies in the experimental models of stroke.

Research for Expression and Prognostic Value of GABRD in Colon Cancer and Coexpressed Gene Network Construction Based on Data Mining

Colon cancer is one of the top five cancers with the highest incidence rate in the world. In order to better understand the pathogenesis and progression of colon cancer, it is still necessary to investigate the abnormally expressed genes in cancer tissue. In this study, the Oncomine database was used for expression analysis, and it was found that the expression level of gamma-aminobutyric acid type A receptor subunit delta (GABRD) gene was upregulated in colon cancer tissue compared with that in normal tissue. UALCAN was used to analyze the expression of GABRD in different groups of age, gender, cancer stage, N stage, and histological subtype. Then, it was also found that the expression of GABRD in each subgroup of colon cancer tissue was all high compared with that in normal tissue. LinkedOmics was used to screen out the differentially expressed genes related to GABRD expression in colon cancer. GO annotation and KEGG pathway enrichment analyses found that the correlated genes may be related to breast cancer, human papillomavirus infection, Notch signaling pathway, and other pathways. Thereafter, GSEA was performed to obtain GABRD-related kinases, miRNAs, and transcription factors, and gene interaction networks were constructed. It was found that GABRD may be involved in cell cycle regulation. Finally, websites like GEPIA were used to detect the predictive ability of GABRD on the prognosis of patients with colon cancer. Kaplan-Meier analysis suggested that the upregulation of GABRD expression was related to the poor prognosis of patients with colon cancer. Overall, in this study, the potential role and prognostic ability of GABRD in colon cancer were explored through data mining, which can be a clue for further research on GABRD.

Identification of Genes Universally Differentially Expressed in Gastric Cancer

Owing to the remarkable heterogeneity of gastric cancer (GC), population-level differentially expressed genes (DEGs) identified using case-control comparison cannot indicate the dysregulated frequency of each DEG in GC. In this work, first, the individual-level DEGs were identified for 1,090 GC tissues without paired normal tissues using the RankComp method. Second, we directly compared the gene expression in a cancer tissue to that in paired normal tissue to identify individual-level DEGs among 448 paired cancer-normal gastric tissues. We found 25 DEGs to be dysregulated in more than 90% of 1,090 GC tissues and also in more than 90% of 448 GC tissues with paired normal tissues. The 25 genes were defined as universal DEGs for GC. Then, we measured 24 paired cancer-normal gastric tissues by RNA-seq to validate them further. Among the universal DEGs, 4 upregulated genes (BGN, E2F3, PLAU, and SPP1) and 1 downregulated gene (UBL3) were found to be cancer genes already documented in the COSMIC or F-Census databases. By analyzing protein-protein interaction networks, we found 12 universally upregulated genes, and we found that their 284 direct neighbor genes were significantly enriched with cancer genes and key biological pathways related to cancer, such as the MAPK signaling pathway, cell cycle, and focal adhesion. The 13 universally downregulated genes and 16 direct neighbor genes were also significantly enriched with cancer genes and pathways related to gastric acid secretion. These universal DEGs may be of special importance to GC diagnosis and treatment targets, and they may make it easier to study the molecular mechanisms underlying GC.

GABRD promotes progression and predicts poor prognosis in colorectal cancer

Little is known about the functional roles of gamma-aminobutyric acid type A receptor subunit delta (GABRD) in colorectal cancer (CRC). The expression of GABRD between CRCs and adjacent normal tissues (NTs), metastasis and primary tumors was compared using public transcriptomic datasets. A tissue microarray and immunohistochemical staining (IHC) were used to determine the clinical and prognostic significance of the GABRD in CRC. We used gain-of-function and loss-of-function experiments to investigate the in vitro roles of GABRD in cultured CRC cells. We characterized the potential mechanism of GABRD’s activities in CRC using a Gene Set Enrichment Analysis (GSEA) with The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset. We found that the GABRD expression was significantly increased in CRCs compared to that in NTs, but was similar between metastasis and primary tumors. Overexpression of GABRD was significantly associated with later pTNM stages and unfavorable patient survival. Overexpression of GABRD accelerated while knock-down of GABRD inhibited cell growth and migration. Mechanistically, the function of GABRD might be ascribed to its influence on major oncogenic events such as epithelial–mesenchymal transition (EMT), angiogenesis, and hedgehog signaling. Collectively, GABRD could be a novel prognostic predictor for CRC that deserves further investigation.

A Targeted Bioinformatics Assessment of Adrenocortical Carcinoma Reveals Prognostic Implications of GABA System Gene Expression

Adrenocortical carcinoma (ACC) is a rare but deadly cancer for which few treatments exist. Here, we have undertaken a targeted bioinformatics study of The Cancer Genome Atlas (TCGA) ACC dataset focusing on the 30 genes encoding the γ-aminobutyric acid (GABA) system—an under-studied, evolutionarily-conserved system that is an emerging potential player in cancer progression. Our analysis identified a subset of ACC patients whose tumors expressed a distinct GABA system transcriptome. Transcript levels of ABAT (encoding a key GABA shunt enzyme), were upregulated in over 40% of tumors, and this correlated with several favorable clinical outcomes including patient survival; while enrichment and ontology analysis implicated two cancer-related biological pathways involved in metastasis and immune response. The phenotype associated with ABAT upregulation revealed a potential metabolic heterogeneity among ACC tumors associated with enhanced mitochondrial metabolism. Furthermore, many GABA_A receptor subunit-encoding transcripts were expressed, including two (GABRB2 and GABRD) prognostic for patient survival. Transcripts encoding GABA_B receptor subunits and GABA transporters were also ubiquitously expressed. The GABA system transcriptome of ACC tumors is largely mirrored in the ACC NCI-H295R cell line, suggesting that this cell line may be appropriate for future functional studies investigating the role of the GABA system in ACC cell growth phenotypes and metabolism.

A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients

Background

As a common malignant tumor in the colon, colon cancer (CC) has high incidence and recurrence rates. This study is designed to build a prognostic model for CC.

Methods

The gene expression dataset, microRNA‐seq dataset, copy number variation (CNV) dataset, DNA methylation dataset, and transcription factor (TF) dataset of CC were downloaded from UCSC Xena database. Using limma package, the differentially methylated genes (DMGs), and differentially expressed genes (DEGs) and miRNAs (DEMs) were identified. Based on random forest method, prognostic model for each omics dataset were constructed. After the omics features related to prognosis were selected using logrank test, the prognostic model based on multi‐omics features was built. Finally, the clinical phenotypes correlated with prognosis were screened using Kaplan–Meier survival analysis, and the nomogram model was established.

Results

There were 1625 DEGs, 268 DEMs, and 386 DMGs between the tumor and normal samples. A total of 105, 29, 159, five, and six genes/sites significantly correlated with prognosis were identified in the gene expression dataset (GABRD), miRNA‐seq dataset (miR‐1271), CNV dataset (RN7SKP247), DNA methylation dataset (cg09170112 methylation site [located in SFSWAP]), and TF dataset (SIX5), respectively. The prognostic model based on multi‐omics features was more effective than those based on single omics dataset. The number of lymph nodes, pathologic_M stage, and pathologic_T stage were the clinical phenotypes correlated with prognosis, based on which the nomogram model was constructed.

Conclusion

The prognostic model based on multi‐omics features and the nomogram model might be valuable for the prognostic prediction of CC.

Distinct diagnostic and prognostic values of γ-aminobutyric acid type A receptor family genes in patients with colon adenocarcinoma

In the present study, the significance of GABA_A genes in colon adenocarcinoma (COAD) were investigated from the view of diagnosis and prognosis. All data were achieved from The Cancer Genome Atlas. Overall survival was analyzed by the Kaplan-Meier analyses and Cox regression model and the hazard ratios and 95% confidence interval were calculated for computation. The Database for Annotation, Visualization and Integrated Discovery, and the Biological Networks Gene Ontology (BiNGO) softwares were applied to assess the biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway analysis to predict the biological function of GABA_A genes. The associated Gene Ontology and KEGG pathways were conducted by Gene Set Enrichment Analysis (GSEA). From receiver operating characteristics curves analysis, it was found that the expression of GABR, γ-aminobutyric acid type A receptor GABRA2, GABRA3, GABRB2, GABRB3, GABRG2, GABRG3, GABRD, GABRE were correlated with COAD occurrence [P<0.0001, area under the curve (AUC)>0.7]. The low expression of the GABRB1, GABRD, GABRP and GABRQ in genes after tumor staging adjustment were positively correlated with the overall survival rate [P=0.049, hazard ratio (HR)=1.517, 95% confidence interval (CI)=1.001–2.297; P=0.006, HR=1.807, 95% CI=1.180–2.765; P=0.005, HR=1.833, 95% CI=1.196–2.810; P=0.034, HR=1.578, 95% CI=1.036–2.405). GSEA showed enrichment of cell matrix adhesion, integrin binding, angiogenesis, endothelial growth factor and endothelial migration regulation in patients with COAD with GABRD overexpression. GABRB1, GABRD, GABRP and GABRQ were associated with the prognostic factors of COAD. The expression levels of GABRA2, GABRA3, GABRB2, GABRB3, GABRG2, GABRD and GABRE may allow differentiation between tumor tissues and adjacent normal tissues.

The cytochrome P450 enzyme CYP24A1 increases proliferation of mutant KRAS-dependent lung adenocarcinoma independent of its catalytic activity

We previously reported that overexpression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1) increases lung cancer cell proliferation by activating RAS signaling and that CYP24A1 knockdown inhibits tumor growth. However, the mechanism of CYP24A1-mediated cancer cell proliferation remains unclear. Here, we conducted cell synchronization and biochemical experiments in lung adenocarcinoma cells, revealing a link between CYP24A1 and anaphase-promoting complex (APC), a key cell cycle regulator. We demonstrate that CYP24A1 expression is cell cycle-dependent; it was higher in the G₂-M phase and diminished upon G₁ entry. CYP24A1 has a functional destruction box (D-box) motif that allows binding with two APC adaptors, CDC20-homologue 1 (CDH1) and cell division cycle 20 (CDC20). Unlike other APC substrates, however, CYP24A1 acted as a pseudo-substrate, inhibiting CDH1 activity and promoting mitotic progression. Conversely, overexpression of a CYP24A1 D-box mutant compromised CDH1 binding, allowing CDH1 hyperactivation, thereby hastening degradation of its substrates cyclin B1 and CDC20, and accumulation of the CDC20 substrate p21, prolonging mitotic exit. These activities also occurred with a CYP24A1 isoform 2 lacking the catalytic cysteine (Cys-462), suggesting that CYP24A1's oncogenic potential is independent of its catalytic activity. CYP24A1 degradation reduced clonogenic survival of mutant KRAS-driven lung cancer cells, and calcitriol treatment increased CYP24A1 levels and tumor burden in Lsl-KRAS^G12D mice. These results disclose a catalytic activity-independent growth-promoting role of CYP24A1 in mutant KRAS-driven lung cancer. This suggests that CYP24A1 could be therapeutically targeted in lung cancers in which its expression is high.

ER-Negative Breast Cancer Is Highly Responsive to Cholesterol Metabolite Signalling

Interventions that alter cholesterol have differential impacts on hormone receptor positive- and negative-breast cancer risk and prognosis. This implies differential regulation or response to cholesterol within different breast cancer subtypes. We evaluated differences in side-chain hydroxycholesterol and liver X nuclear receptor signalling between Oestrogen Receptor (ER)-positive and ER-negative breast cancers and cell lines. Cell line models of ER-positive and ER-negative disease were treated with Liver X Receptor (LXR) ligands and transcriptional activity assessed using luciferase reporters, qPCR and MTT. Publicly available datasets were mined to identify differences between ER-negative and ER-positive tumours and siRNA was used to suppress candidate regulators. Compared to ER-positive breast cancer, ER-negative breast cancer cells were highly responsive to LXR agonists. In primary disease and cell lines LXRA expression was strongly correlated with its target genes in ER-negative but not ER-positive disease. Expression of LXR’s corepressors (NCOR1, NCOR2 and LCOR) was significantly higher in ER-positive disease relative to ER-negative, and their knock-down equalized sensitivity to ligand between subtypes in reporter, gene expression and viability assays. Our data support further evaluation of dietary and pharmacological targeting of cholesterol metabolism as an adjunct to existing therapies for ER-negative and ER-positive breast cancer patients.

Novel significant stage-specific differentially expressed genes in hepatocellular carcinoma

BACKGROUND

Liver cancer is among top deadly cancers worldwide with a very poor prognosis, and the liver is a vulnerable site for metastases of other cancers. Early diagnosis is crucial for treatment of the predominant liver cancers, namely hepatocellular carcinoma (HCC). Here we developed a novel computational framework for the stage-specific analysis of HCC.

METHODS

Using publicly available clinical and RNA-Seq data of cancer samples and controls and the AJCC staging system, we performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in the log fold-change of gene expression in cancer samples relative to control. To identify genes that were stage-specific controlling for confounding differential expression in other stages, we developed a set of six pairwise contrasts between the stages and enforced a p-value threshold (< 0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. The monotonicity of gene expression with cancer progression was analyzed with a linear model using the cancer stage as a numeric variable.

RESULTS

Our analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI, NCAPG2, GNMT and XRCC2, and 35 stage-IV specific genes including GABRD, PGAM2, PECAM1 and CXCR2P1. Overexpression of DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD was found to be signifincantly monotonically upregulated across stages. Our work has revealed 1977 genes with significant monotonic patterns of expression across cancer stages. NDUFA4L2, CRHBP and PIGU were top genes with monotonic changes of expression across cancer stages that could represent promising targets for therapy. Comparison with gene signatures from the BCLC staging system identified two genes, HSP90AB1 and ARHGAP42. Gene set enrichment analysis indicated overrepresented pathways specific to each stage, notably viral infection pathways in HCC initiation.

CONCLUSIONS

Our study identified novel significant stage-specific differentially expressed genes which could enhance our understanding of the molecular determinants of hepatocellular carcinoma progression. Our findings could serve as biomarkers that potentially underpin diagnosis as well as pinpoint therapeutic targets.

Identification of genes with universally upregulated or downregulated expressions in colorectal cancer

BACKGROUND AND AIM

Differentially expressed (DE) genes detected at the population-level through case-control comparison provide no information on the dysregulation frequencies of DE genes in a cancer. In this work, we aimed to identify the genes with universally upregulated or downregulated expressions in colorectal cancer (CRC).

METHODS

We firstly clarified that DE genes in an individual cancer tissue should be the disease-relevant genes, which are dysregulated in the cancer tissue in comparison with its own previously normal state. Then, we identified DE genes at the individual level for 2233 CRC samples collected from multiple data sources using the RankComp algorithm.

RESULTS

We found 26 genes that were upregulated or downregulated in almost all the 2233 CRC samples and validated the results using 124 CRC tissues with paired adjacent normal tissues. Especially, we found that two genes (AJUBA and EGFL6), upregulated universally in CRC tissues, were extremely lowly expressed in normal colorectal tissues, which were considered to be oncogenes in CRC oncogenesis and development. Oppositely, we found that one gene (LPAR1), downregulated universally in CRC tissues, was silenced in CRC tissues but highly expressed in normal colorectal tissues, which were considered to be tumor suppressor genes in CRC. Functional evidences revealed that these three genes may induce CRC through deregulating pathways for ribosome biogenesis, cell proliferation, and cell cycle.

CONCLUSIONS

In conclusion, the individual-level DE genes analysis can help us find genes dysregulated universally in CRC tissues, which may be important diagnostic biomarkers and therapy targets.

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

Background

Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resources such as TCGA and TARGET do not provide matched tissue samples for every cancer or cancer subtype. The recent GTEx project has profiled samples from healthy individuals, providing an excellent resource for this field, yet the feasibility of using GTEx samples as the reference remains unanswered.

Methods

We analyze RNA-Seq data processed from the same computational pipeline and systematically evaluate GTEx as a potential reference resource. We use those cancers that have adjacent normal tissues in TCGA as a benchmark for the evaluation. To correlate tumor samples and normal samples, we explore top varying genes, reduced features from principal component analysis, and encoded features from an autoencoder neural network. We first evaluate whether these methods can identify the correct tissue of origin from GTEx for a given cancer and then seek to answer whether disease expression signatures are consistent between those derived from TCGA and from GTEx.

Results

Among 32 TCGA cancers, 18 cancers have less than 10 matched adjacent normal tissue samples. Among three methods, autoencoder performed the best in predicting tissue of origin, with 12 of 14 cancers correctly predicted. The reason for misclassification of two cancers is that none of normal samples from GTEx correlate well with any tumor samples in these cancers. This suggests that GTEx has matched tissues for the majority cancers, but not all. While using autoencoder to select proper normal samples for disease signature creation, we found that disease signatures derived from normal samples selected via an autoencoder from GTEx are consistent with those derived from adjacent samples from TCGA in many cases. Interestingly, choosing top 50 mostly correlated samples regardless of tissue type performed reasonably well or even better in some cancers.

Conclusions

Our findings demonstrate that samples from GTEx can serve as reference normal samples for cancers, especially those do not have available adjacent tissue samples. A deep-learning based approach holds promise to select proper normal samples.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0463-6) contains supplementary material, which is available to authorized users.

CamurWeb: a classification software and a large knowledge base for gene expression data of cancer

BACKGROUND

The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated disease. State of the art rule-based classifiers are designed to extract a single classification model, possibly composed of few relevant genes. Conversely, we aim to create a large knowledge base composed of many rule-based models, and thus determine which genes could be potentially involved in the analyzed tumor. This comprehensive and open access knowledge base is required to disseminate novel insights about cancer.

RESULTS

We propose CamurWeb, a new method and web-based software that is able to extract multiple and equivalent classification models in form of logic formulas ("if then" rules) and to create a knowledge base of these rules that can be queried and analyzed. The method is based on an iterative classification procedure and an adaptive feature elimination technique that enables the computation of many rule-based models related to the cancer under study. Additionally, CamurWeb includes a user friendly interface for running the software, querying the results, and managing the performed experiments. The user can create her profile, upload her gene expression data, run the classification analyses, and interpret the results with predefined queries. In order to validate the software we apply it to all public available RNA sequencing datasets from The Cancer Genome Atlas database obtaining a large open access knowledge base about cancer. CamurWeb is available at http://bioinformatics.iasi.cnr.it/camurweb .

CONCLUSIONS

The experiments prove the validity of CamurWeb, obtaining many classification models and thus several genes that are associated to 21 different cancer types. Finally, the comprehensive knowledge base about cancer and the software tool are released online; interested researchers have free access to them for further studies and to design biological experiments in cancer research.

Support vector machine classifier for prediction of the metastasis of colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers and a major cause of mortality. The present study aimed to identify potential biomarkers for CRC metastasis and uncover the mechanisms underlying the etiology of the disease. The five datasets GSE68468, GSE62321, GSE22834, GSE14297 and GSE6988 were utilized in the study, all of which contained metastatic and non-metastatic CRC samples. Among them, three datasets were integrated via meta-analysis to identify the differentially expressed genes (DEGs) between the two types of samples. A protein-protein interaction (PPI) network was constructed for these DEGs. Candidate genes were then selected by the support vector machine (SVM) classifier based on the betweenness centrality (BC) algorithm. A CRC dataset from The Cancer Genome Atlas database was used to evaluate the accuracy of the SVM classifier. Pathway enrichment analysis was carried out for the SVM-classified gene signatures. In total, 358 DEGs were identified by meta‑analysis. The top ten nodes in the PPI network with the highest BC values were selected, including cAMP responsive element binding protein 1 (CREB1), cullin 7 (CUL7) and signal sequence receptor 3 (SSR3). The optimal SVM classification model was established, which was able to precisely distinguish between the metastatic and non-metastatic samples. Based on this SVM classifier, 40 signature genes were identified, which were mainly enriched in protein processing in endoplasmic reticulum (e.g., SSR3), AMPK signaling pathway (e.g., CREB1) and ubiquitin mediated proteolysis (e.g., FBXO2, CUL7 and UBE2D3) pathways. In conclusion, the SVM-classified genes, including CREB1, CUL7 and SSR3, precisely distinguished the metastatic CRC samples from the non-metastatic ones. These genes have the potential to be used as biomarkers for the prognosis of metastatic CRC.

Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes

BACKGROUND

Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.

RESULTS

We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.

CONCLUSIONS

Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.

Reactive oxygen species (ROS) are a key determinant of cancer's metabolic phenotype

Cancer cells exhibit a wide range of metabolic phenotypes, ranging from strict aerobic glycolysis to increased mitochondrial respiration. The cause and utility of this metabolic variation is poorly understood. Given that cancer cells experience heavy selection within their microenvironment, survival requires metabolic adaptation to both extracellular and intracellular conditions. Herein, we suggest that reactive oxygen species (ROS) are a key determinant of cancer's metabolic phenotype. Intracellular ROS levels can be modified by an assortment of critical parameters including oxygenation, glucose availability and growth factors. ROS act as integrators of environmental information as well as downstream effectors of signaling pathways. Maintaining ROS within a narrow range allows malignant cells to enhance growth and invasion while limiting their apoptotic susceptibility. Cancer cells actively modify their metabolism to optimize intracellular ROS levels and thereby improve survival. Furthermore, we highlight distinct metabolic phenotypes in response to oxidative stress and their tumorigenic drivers.

Genes involved in development and differentiation are commonly methylated in cancers derived from multiple organs: a single-institutional methylome analysis using 1007 tissue specimens

The aim of this study was to clarify the significance of DNA methylation alterations shared by cancers derived from multiple organs. We analyzed single-institutional methylome data by single-CpG-resolution Infinium assay for 1007 samples of non-cancerous tissue (N) and corresponding cancerous tissue (T) obtained from lung, stomach, kidney, breast and liver. Principal component analysis revealed that N samples of each organ showed distinct DNA methylation profiles, DNA methylation profiles of N samples of each organ being inherited by the corresponding T samples and DNA methylation profiles of T samples being more similar to those of N samples in the same organ than those of T samples in other organs. In contrast to such organ and/or carcinogenetic factor-specificity of DNA methylation profiles, when compared with the corresponding N samples, 231 genes commonly showed DNA hypermethylation in T samples in four or more organs. Gene ontology enrichment analysis showed that such commonly methylated genes were enriched among “transcriptional factors” participating in development and/or differentiation, which reportedly show bivalent histone modification in embryonic stem cells. Pyrosequencing and quantitative reverse transcription-PCR revealed an inverse correlation between DNA methylation levels and mRNA expression levels of representative commonly methylated genes, such as ALX1, ATP8A2, CR1 and EFCAB1, in tissue samples. These data suggest that disruption of the differentiated state of precancerous cells via alterations of expression, independent of differences in organs and/or carcinogenetic factors, may be a common feature of DNA methylation alterations during carcinogenesis in multiple organs.

Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets

A very low 5-year survival rate among hepatocellular carcinoma (HCC) patients is mainly due to lack of early stage diagnosis, distant metastasis and high risk of postoperative recurrence. Hence ascertaining novel biomarkers for early diagnosis and patient specific therapeutics is crucial and urgent. Here, we have performed a comprehensive analysis of the expression data of 423 HCC patients (373 tumors and 50 controls) downloaded from The Cancer Genome Atlas (TCGA) followed by pathway enrichment by gene ontology annotations, subtype classification and overall survival analysis. The differential gene expression analysis using non-parametric Wilcoxon test revealed a total of 479 up-regulated and 91 down-regulated genes in HCC compared to controls. The list of top differentially expressed genes mainly consists of tumor/cancer associated genes, such as AFP, THBS4, LCN2, GPC3, NUF2, etc. The genes over-expressed in HCC were mainly associated with cell cycle pathways. In total, 59 kinases associated genes were found over-expressed in HCC, including TTK, MELK, BUB1, NEK2, BUB1B, AURKB, PLK1, CDK1, PKMYT1, PBK, etc. Overall four distinct HCC subtypes were predicted using consensus clustering method. Each subtype was unique in terms of gene expression, pathway enrichment and median survival. Conclusively, this study has exposed a number of interesting genes which can be exploited in future as potential markers of HCC, diagnostic as well as prognostic and subtype classification may guide for improved and specific therapy.

Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival

Cancer cells undergo a multifaceted rewiring of cellular metabolism to support their biosynthetic needs. Although the major determinants of this metabolic transformation have been elucidated, their broad biological implications and clinical relevance are unclear. Here we systematically analyse the expression of metabolic genes across 20 different cancer types and investigate their impact on clinical outcome. We find that cancers undergo a tissue-specific metabolic rewiring, which converges towards a common metabolic landscape. Of note, downregulation of mitochondrial genes is associated with the worst clinical outcome across all cancer types and correlates with the expression of epithelial-to-mesenchymal transition gene signature, a feature of invasive and metastatic cancers. Consistently, suppression of mitochondrial genes is identified as a key metabolic signature of metastatic melanoma and renal cancer, and metastatic cell lines. This comprehensive analysis reveals unexpected facets of cancer metabolism, with important implications for cancer patients' stratification, prognosis and therapy.

Cancer cells reprogramme their metabolism with unclear clinical implications. Here, the authors analyse the expression of metabolic genes across 20 types of solid cancers and find that clinical aggressiveness, poor survival and metastasis are associated with the deregulation of mitochondrial metabolism.