Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types. Sci Rep. 2015;5:13413. [PMID: 26292924 PMCID: PMC4544034 DOI: 10.1038/srep13413]

Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms

BACKGROUND

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals.

METHODS

We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

RESULTS

Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)].

CONCLUSIONS

Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

IMPACT

HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach

Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Background

Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature.

Methods

In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Furthermore, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. In this study, we have used GSE181919 dataset, we have extracted 20 primary cancer (HNSCC) samples, and 9 normal tissues samples. The primary cancer samples contained 13 HPV- and 7 HPV+ samples. The models developed in this study have been trained on 80% of the dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes.

Results

Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV- patients on the validation set. In GO enrichment analysis, it was found that most genes were involved in binding and catalytic activities.

Conclusion

A software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/.

A Gold Standard-Derived Modular Barcoding Approach to Cancer Transcriptomics

A challenge with studying cancer transcriptomes is in distilling the wealth of information down into manageable portions of information. In this resource, we develop an approach that creates and assembles cancer type-specific gene expression modules into flexible barcodes, allowing for adaptation to a wide variety of uses. Specifically, we propose that modules derived organically from high-quality gold standards such as The Cancer Genome Atlas (TCGA) can accurately capture and describe functionally related genes that are relevant to specific cancer types. We show that such modules can: (1) uncover novel gene relationships and nominate new functional memberships, (2) improve and speed up analysis of smaller or lower-resolution datasets, (3) re-create and expand known cancer subtyping schemes, (4) act as a "decoder" to bridge seemingly disparate established gene signatures, and (5) efficiently apply single-cell RNA sequencing information to other datasets. Moreover, such modules can be used in conjunction with native spreadsheet program commands to create a powerful and rapid approach to hypothesis generation and testing that is readily accessible to non-bioinformaticians. Finally, we provide tools for users to create and interpret their own modules. Overall, the flexible modular nature of the proposed barcoding provides a user-friendly approach to rapidly decoding transcriptome-wide data for research or, potentially, clinical uses.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms

Introduction

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals.

Methods

We included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

Results

Cluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]).

Conclusions

A single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

Developments and challenges in neoadjuvant therapy for locally advanced pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a significant public health challenge and is currently the fourth leading cause of cancer-related mortality in developed countries. Despite advances in cancer treatment, the 5-year survival rate for patients with PDAC remains less than 5%. In recent years, neoadjuvant therapy (NAT) has emerged as a promising treatment option for many cancer types, including locally advanced PDAC, with the potential to improve patient outcomes. To analyze the role of NAT in the setting of locally advanced PDAC over the past decade, a systematic literature search was conducted using PubMed and Web of Science. The results suggest that NAT may reduce the local mass size, promote tumor downstaging, and increase the likelihood of resection. These findings are supported by the latest evidence-based medical literature and the clinical experience of our center. Despite the potential benefits of NAT, there are still challenges that need to be addressed. One such challenge is the lack of consensus on the optimal timing and duration of NAT. Improved criteria for patient selection are needed to further identify PDAC patients likely to respond to NAT. In conclusion, NAT has emerged as a promising treatment option for locally advanced PDAC. However, further research is needed to optimize its use and to better understand the role of NAT in the management of this challenging disease. With continued advances in cancer treatment, there is hope of improving the outcomes of patients with PDAC in the future.

Candidate pathway analysis of surfactant proteins identifies CTSH and SFTA2 that influences lung cancer risk

Pulmonary surfactant is a lipoprotein synthesized and secreted by alveolar type II cells in lung. We evaluated the associations between 200,139 single nucleotide polymorphisms (SNPs) of 40 surfactant-related genes and lung cancer risk using genotyped data from two independent lung cancer genome-wide association studies. Discovery data included 18,082 cases and 13,780 controls of European ancestry. Replication data included 1,914 cases and 3,065 controls of European descent. Using multivariate logistic regression, we found novel SNPs in surfactant-related genes CTSH [rs34577742 C > T, odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.89-0.93, P = 7.64 × 10-9] and SFTA2 (rs3095153 G > A, OR = 1.16, 95% CI = 1.10-1.21, P = 1.27 × 10-9) associated with overall lung cancer in the discovery data and validated in an independent replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.80-0.96, P = 5.76 × 10-3) and SFTA2 (rs3095153 G > A, OR = 1.14, 95% CI = 1.01-1.28, P = 3.25 × 10-2). Among ever smokers, we found SNPs in CTSH (rs34577742 C > T, OR = 0.89, 95% CI = 0.85-0.92, P = 1.94 × 10-7) and SFTA2 (rs3095152 G > A, OR = 1.20, 95% CI = 1.14-1.27, P = 4.25 × 10-11) associated with overall lung cancer in the discovery data and validated in the replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.79-0.97, P = 1.64 × 10-2) and SFTA2 (rs3095152 G > A, OR = 1.15, 95% CI = 1.01-1.30, P = 3.81 × 10-2). Subsequent transcriptome-wide association study using expression weights from a lung expression quantitative trait loci study revealed genes most strongly associated with lung cancer are CTSH (PTWAS = 2.44 × 10-4) and SFTA2 (PTWAS = 2.32 × 10-6).

Mutant APC reshapes Wnt signaling plasma membrane nanodomains by altering cholesterol levels via oncogenic β-catenin

Although the role of the Wnt pathway in colon carcinogenesis has been described previously, it has been recently demonstrated that Wnt signaling originates from highly dynamic nano-assemblies at the plasma membrane. However, little is known regarding the role of oncogenic APC in reshaping Wnt nanodomains. This is noteworthy, because oncogenic APC does not act autonomously and requires activation of Wnt effectors upstream of APC to drive aberrant Wnt signaling. Here, we demonstrate the role of oncogenic APC in increasing plasma membrane free cholesterol and rigidity, thereby modulating Wnt signaling hubs. This results in an overactivation of Wnt signaling in the colon. Finally, using the Drosophila sterol auxotroph model, we demonstrate the unique ability of exogenous free cholesterol to disrupt plasma membrane homeostasis and drive Wnt signaling in a wildtype APC background. Collectively, these findings provide a link between oncogenic APC, loss of plasma membrane homeostasis and CRC development.

Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide

The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available.

The Revelation of Continuously Organized, Co-Overexpressed Protein-Coding Genes with Roles in Cellular Communications in Breast Cancer

Many human cancers, including breast cancer, are polygenic and involve the co-dysregulation of multiple regulatory molecules and pathways. Though the overexpression of genes and amplified chromosomal regions have been closely linked in breast cancer, the notion of the co-upregulation of genes at a single locus remains poorly described. Here, we describe the co-overexpression of 34 continuously organized protein-coding genes with diverse functions at 8q.24.3(143437655-144326919) in breast and other cancer types, the CanCord34 genes. In total, 10 out of 34 genes have not been reported to be overexpressed in breast cancer. Interestingly, the overexpression of CanCord34 genes is not necessarily associated with genomic amplification and is independent of hormonal or HER2 status in breast cancer. CanCord34 genes exhibit diverse known and predicted functions, including enzymatic activities, cell viability, multipotency, cancer stem cells, and secretory activities, including extracellular vesicles. The co-overexpression of 33 of the CanCord34 genes in a multivariant analysis was correlated with poor survival among patients with breast cancer. The analysis of the genome-wide RNAi functional screening, cell dependency fitness, and breast cancer stem cell databases indicated that three diverse overexpressed CanCord34 genes, including a component of spliceosome PUF60, a component of exosome complex EXOSC4, and a ribosomal biogenesis factor BOP1, shared roles in cell viability, cell fitness, and stem cell phenotypes. In addition, 17 of the CanCord34 genes were found in the microvesicles (MVs) secreted from the mesenchymal stem cells that were primed with MDA-MB-231 breast cancer cells. Since these MVs were important in the chemoresistance and dedifferentiation of breast cancer cells into cancer stem cells, these findings highlight the significance of the CanCord34 genes in cellular communications. In brief, the persistent co-overexpression of CanCord34 genes with diverse functions can lead to the dysregulation of complementary functions in breast cancer. In brief, the present study provides new insights into the polygenic nature of breast cancer and opens new research avenues for basic, preclinical, and therapeutic studies in human cancer.

Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers

•

The matrisome plays a critical role in the progression of cancer, but the matrisomes of gynecological cancers have not been well characterized.

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We built an in silico analysis pipeline to analyze publicly available bulk RNA-seq datasets of cervical, endometrial, and uterine cancers.

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Using a machine learning approach, we identified genes and gene networks that held inferential significance for cancer stage and patient survival.

•

Cervical, endometrial, and uterine cancers are highly distinct from one another and should be analyzed separately.

Increasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as holding inferential significance for cancer stage and patient survival can be evaluated as potential drug targets and incorporated into in vitro models of disease.

Engineered colorectal cancer tissue recapitulates key attributes of a patient-derived xenograft tumor line

The development of physiologically relevantin vitrocolorectal cancer (CRC) models is vital for advancing understanding of tumor biology. Although CRC patient-derived xenografts (PDXs) recapitulate key patient tumor characteristics and demonstrate high concordance with clinical outcomes, the use of thisin vivomodel is costly and low-throughput. Here we report the establishment and in-depth characterization of anin vitrotissue-engineered CRC model using PDX cells. To form the 3D engineered CRC-PDX (3D-eCRC-PDX) tissues, CRC PDX tumors were expandedin vivo, dissociated, and the isolated cells encapsulated within PEG-fibrinogen hydrogels. Following PEG-fibrinogen encapsulation, cells remain viable and proliferate within 3D-eCRC-PDX tissues. Tumor cell subpopulations, including human cancer and mouse stromal cells, are maintained in long-term culture (29 days); cellular subpopulations increase ratiometrically over time. The 3D-eCRC-PDX tissues mimic the mechanical stiffness of originating tumors. Extracellular matrix protein production by cells in the 3D-eCRC-PDX tissues resulted in approximately 57% of proteins observed in the CRC-PDX tumors also being present in the 3D-eCRC-PDX tissues on day 22. Furthermore, we show congruence in enriched gene ontology molecular functions and Hallmark gene sets in 3D-eCRC-PDX tissues and CRC-PDX tumors compared to normal colon tissue, while prognostic Kaplan-Meier plots for overall and relapse free survival did not reveal significant differences between CRC-PDX tumors and 3D-eCRC-PDX tissues. Our results demonstrate high batch-to-batch consistency and strong correlation between ourin vitrotissue-engineered PDX-CRC model and the originatingin vivoPDX tumors, providing a foundation for future studies of disease progression and tumorigenic mechanisms.

B4GALT5 high expression associated with poor prognosis of hepatocellular carcinoma

BACKGROUND

B4GALT5 is postulated to be an important protein in sugar metabolism that catalyzes the synthesis of lactosylceramide (LacCer). However, its role in hepatocellular carcinoma (HCC) remains unknown.

METHOD

We characterized the expression of B4GALT5 in HCC tissue compared to normal tissue, and explored its function of B4GALT5 in HCC by enrichment analysis based on its co-expressed gene set. Next, we checked whether B4GALT5 expression is correlated to immune infiltration level and clinical prognosis in hepatocellular carcinoma. Finally, we verified the expression of B4GALT5 using clinical samples evaluated by RT-PCR, and conducted in vitro experiments with B4GALT5-knockdown HCC cells to investigate the function of B4GALT5 in the HCC cell proliferation, migration and invasion.

RESULTS

We found B4GALT5 mRNA and protein expression levels were significantly high in HCC tissue compared to normal tissue. The enrichment analysis of the gene sets that co-expressed with B4GALT5 showed specificity in HCC-related pathways and functions. Also, the expression pattern of B4GALT5 was significantly related to the immune infiltration level, especially CD4+ T cell and macrophage cells. B4GALT5 higher mRNA expression was associated with poor overall survival (OS) in HCC patients. Furthermore, In vitro experiments showed that depletion of B4GALT5 significantly inhibited HCC cell proliferation, migration and invasion. This study revealed the function and its mediated pathways of B4GALT5 in HCC, indicating that B4GALT5 may serve as a prognostic biomarker of HCC.

Exaggerated false positives by popular differential expression methods when analyzing human population samples

When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.

Chromosomal imbalances detected via RNA-sequencing in 28 cancers

MOTIVATION

RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA.

RESULTS

We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings.

AVAILABILITY AND IMPLEMENTATION

The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Summary

Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.

Availability and implementation

www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.

Supplementary information

Supplementary data are available at https://github.com/skr1/Imagene.git.

An Ensemble Model for Tumor Type Identification and Cancer Origins Classification

Tissue biopsy can be wildly used in cancer diagnosis. However, manually classifying the cancerous status of biopsies and tissue origin of tumors for cancerous ones requires skilled specialists and sophisticated equipment. As a result, a data-based model is urgently needed. In this paper, we propose a data-based ensemble model for tumor type identification and cancer origins classification. Our model is an ensemble model that combines different models based on mRNA groups which serve distinct functions. The experiment on the TCGA dataset exhibits a promising result on both tasks - 98% on tumor type identification and 96.1% on cancer origin classification. We also test our model on external validation datasets, which prove the robustness of our model.

Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species

Understanding relationships between spontaneous cancer in companion (pet) canines and humans can facilitate biomarker and drug development in both species. Towards this end we developed an experimental-bioinformatic protocol that analyzes canine transcriptomics data in the context of existing human data to evaluate comparative relevance of canine to human cancer. We used this protocol to characterize five canine cancers: melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, in 60 dogs. We applied an unsupervised, iterative clustering method that yielded five co-expression modules and found that each cancer exhibited a unique module expression profile. We constructed cancer models based on the co-expression modules and used the models to successfully classify the canine data. These canine-derived models also successfully classified human tumors representing the same cancers, indicating shared cancer biology between canines and humans. Annotation of the module genes identified cancer specific pathways relevant to cells-of-origin and tumor biology. For example, annotations associated with melanin production (PMEL, GPNMB, and BACE2), synthesis of bone material (COL5A2, COL6A3, and COL12A1), synthesis of pulmonary surfactant (CTSH, LPCAT1, and NAPSA), ribosomal proteins (RPL8, RPS7, and RPLP0), and epigenetic regulation (EDEM1, PTK2B, and JAK1) were unique to melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, respectively. In total, 152 biomarker candidates were selected from highly expressing modules for each cancer type. Many of these biomarker candidates are under-explored as drug discovery targets and warrant further study. The demonstrated transferability of classification models from canines to humans enforces the idea that tumor biology, biomarker targets, and associated therapeutics, discovered in canines, may translate to human medicine.

Exosome component 1 cleaves single-stranded DNA and sensitizes human kidney renal clear cell carcinoma cells to poly(ADP-ribose) polymerase inhibitor

Targeting DNA repair pathway offers an important therapeutic strategy for Homo sapiens (human) cancers. However, the failure of DNA repair inhibitors to markedly benefit patients necessitates the development of new strategies. Here, we show that exosome component 1 (EXOSC1) promotes DNA damages and sensitizes human kidney renal clear cell carcinoma (KIRC) cells to DNA repair inhibitor. Considering that endogenous source of mutation (ESM) constantly assaults genomic DNA and likely sensitizes human cancer cells to the inhibitor, we first analyzed the statistical relationship between the expression of individual genes and the mutations for KIRC. Among the candidates, EXOSC1 most notably promoted DNA damages and subsequent mutations via preferentially cleaving C site(s) in single-stranded DNA. Consistently, EXOSC1 was more significantly correlated with C>A transversions in coding strands than these in template strands in human KIRC. Notably, KIRC patients with high EXOSC1 showed a poor prognosis, and EXOSC1 sensitized human cancer cells to poly(ADP-ribose) polymerase inhibitors. These results show that EXOSC1 acts as an ESM in KIRC, and targeting EXOSC1 might be a potential therapeutic strategy.

MI_DenseNetCAM: A Novel Pan-Cancer Classification and Prediction Method Based on Mutual Information and Deep Learning Model

The Pan-Cancer Atlas consists of original sequencing data from various sources, provides the opportunity to perform systematic studies on the commonalities and differences between diverse cancers. The analysis for the pan-cancer dataset could help researchers to identify the key factors that could trigger cancer. In this paper, we present a novel pan-cancer classification method, referred to MI_DenseNetCAM, to identify a set of genes that can differentiate all tumor types accurately. First, the Mutual Information (MI) was utilized to eliminate noise and redundancy from the pan-cancer datasets. Then, the gene data was further converted to 2D images. Next, the DenseNet model was adopted as a classifier and the Guided Grad-CAM algorithm was applied to identify the key genes. Extensive experimental results on the public RNA-seq data sets with 33 different tumor types show that our method outperforms the other state-of-the-art classification methods. Moreover, gene analysis further demonstrated that the genes selected by our method were related to the corresponding tumor types.

Curing pancreatic cancer

The distinct biology of pancreatic cancer with aggressive and early invasive tumor cells, a tumor promoting microenvironment, late diagnosis, and high therapy resistance poses major challenges on clinicians, researchers, and patients. In current clinical practice, a curative approach for pancreatic cancer can only be offered to a minority of patients and even for those patients, the long-term outcome is grim. This bitter combination will eventually let pancreatic cancer rise to the second leading cause of cancer-related mortalities. With surgery being the only curative option, complete tumor resection still remains the center of pancreatic cancer treatment. In recent years, new developments in neoadjuvant and adjuvant treatment have emerged. Together with improved perioperative care including complication management, an increasing number of patients have become eligible for tumor resection. Basic research aims to further increase these numbers by new methods of early detection, better tumor modelling and personalized treatment options. This review aims to summarize the current knowledge on clinical and biologic features, surgical and non-surgical treatment options, and the improved collaboration of clinicians and basic researchers in pancreatic cancer that will hopefully result in more successful ways of curing pancreatic cancer.

Benchmarking association analyses of continuous exposures with RNA-seq in observational studies

Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression-DESeq2, edgeR and limma-as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering and generation of empirical null distribution of association P-values, and we apply the pipeline to compute empirical P-values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison and the computation of quantile empirical P-values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical P-values. We provide the proposed pipeline with fast algorithms in an R package Olivia, and implemented it to study the associations of measures of sleep disordered breathing with RNA-seq in peripheral blood mononuclear cells in participants from the Multi-Ethnic Study of Atherosclerosis.

Non-coding RNAs related to angiogenesis in gynecological cancer

Gynecological cancer affects the female reproductive system, including ovarian, uterine, endometrial, cervical, vulvar, and vaginal tumors. Non-coding RNAs (ncRNAs), and in particular microRNAs, function as regulatory molecules, which can control gene expression in a post-transcriptional manner. Normal physiological processes like cellular proliferation, differentiation, and apoptosis, and pathological processes such as oncogenesis and metastasis are regulated by microRNAs. Numerous reports have shown a direct role of microRNAs in the modulation of angiogenesis in gynecological cancer, via targeting pro-angiogenic factors and signaling pathways. Understanding the molecular mechanism involved in the regulation of angiogenesis by microRNAs may lead to new treatment options. Recently the regulatory role of some long non-coding RNAs in gynecological cancer has also been explored, but the information on this function is more limited. The aim of this article is to explore the pathways responsible for angiogenesis, and to what extent ncRNAs may be employed as biomarkers or therapeutic targets in gynecological cancer.

A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

lncRNA ZNF667-AS1 (NR_036521.1) inhibits the progression of colorectal cancer via regulating ANK2/JAK2 expression

Long noncoding RNAs (lncRNAs) participate in many biological processes by affecting gene expression at the posttranscriptional level. lncRNAs are dysregulated in colorectal cancer (CRC) and this dysregulation is closely related to tumorigenesis, metastasis, and prognosis. Although many lncRNAs have been identified in CRC, the relation between ZNF667 antisense RNA 1 (head to head; ZNF667-AS1, accession: NR_036521.1) and CRC remains unclear. In this study, a total of 2,218 differentially expressed genes and 428 differentially expressed lncRNAs were identified between tumor and pericarcinous tissues. They were mainly enriched in cancer pathways, chemokine signaling, phosphoinositide 3-kinase-protein kinase B signaling pathway, and others. Key lncRNAs, including ZNF667-AS1, and their corresponding genes, such as ankyrin 2 (ANK2), were downregulated in CRC tumor tissues. In addition, downregulated ZNF667-AS1 (NR_036521.1) expression is associated with poor prognosis and disease progression. Overexpression of ZNF667-AS1 (NR_036521.1) inhibited the proliferation, migration, and invasion of VOLO cells both in vitro and in vivo. Moreover, Janus kinase 2 (JAK2) and ANK2 were significantly down- and upregulated in the overexpressed ZNF667-AS1 VOLO cells compared to those in the negative-control group. Knockdown of ANK2 or overexpression of JAK2 significantly counteracted the inhibitory effects of overexpression of ZNF667-AS1 on LOVO cell proliferation and migration. Taken together, it is indicated in our research that ZNF667-AS1 interaction with ANK2/JAK2 maybe important in CRC progression. Overexpression of ZNF667-AS1 could inhibit the proliferation, migration, and invasion of CRC cells, which may be related with the high ANK2 and low JAK2 levels.

Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence

BACKGROUND

Long noncoding RNA (lncRNA) have been implicated in the etiology of alcohol use. Since lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step toward understanding lncRNA functions in alcohol use and addiction. Thus, we sought to profile lncRNA expression in the nucleus accumbens (NAc) in a large postmortem alcohol brain sample.

METHODS

LncRNA and protein-coding gene (PCG) expressions in the NAc from 41 subjects with alcohol dependence (AD) and 41 controls were assessed via a regression model. Weighted gene coexpression network analysis was used to identify lncRNA and PCG networks (i.e., modules) significantly correlated with AD. Within the significant modules, key network genes (i.e., hubs) were also identified. The lncRNA and PCG hubs were correlated via Pearson correlations to elucidate the potential biological functions of lncRNA. The lncRNA and PCG hubs were further integrated with GWAS data to identify expression quantitative trait loci (eQTL).

RESULTS

At Bonferroni adj. p-value ≤ 0.05, we identified 19 lncRNA and 5 PCG significant modules, which were enriched for neuronal and immune-related processes. In these modules, we further identified 86 and 315 PCG and lncRNA hubs, respectively. At false discovery rate (FDR) of 10%, the correlation analyses between the lncRNA and PCG hubs revealed 3,125 positive and 1,860 negative correlations. Integration of hubs with genotype data identified 243 eQTLs affecting the expression of 39 and 204 PCG and lncRNA hubs, respectively.

CONCLUSIONS

Our study identified lncRNA and gene networks significantly associated with AD in the NAc, coordinated lncRNA and mRNA coexpression changes, highlighting potentially regulatory functions for the lncRNA, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.

Genetic Features of Lung Adenocarcinoma with Ground- Glass Opacity: What Causes the Invasiveness of Lung Adenocarcinoma?

Background

Lung adenocarcinoma (LUAD) with ground-glass opacity (GGO) can become aggravated, but the reasons for this aggravation are not fully understood. The goal of this study was to analyze the genetic features and causes of progression of GGO LUAD.

Methods

LUAD tumor samples and normal tissues were analyzed using an Illumina HiSeq 4000 system. After the tumor mutational burden (TMB) was calculated, the identified mutations were classified as those found only in GGO LUAD, those present only in non- GGO LUAD, and those common to both tissue types. Ten high-frequency genes were selected from each domain, after which protein interaction network analysis was conducted.

Results

Overall, 227 mutations in GGO LUAD, 212 in non-GGO LUAD, and 48 that were common to both tumor types were found. The TMB was 8.8 in GGO and 7.8 in non-GGO samples. In GGO LUAD, mutations of FCGBP and SFTPA1 were identified. FOXQ1, IRF5, and MAGEC1 mutations were common to both types, and CDC27 and NOTCH4 mutations were identified in the non-GGO LUAD. Protein interaction network analysis indicated that IRF5 (common to both tissue types) and CDC27 (found in the non-GGO LUAD) had significant biological functions related to the cell cycle and proliferation.

Conclusion

In conclusion, GGO LUAD exhibited a higher TMB than non-GGO LUAD. No clinically meaningful mutations were found to be specific to GGO LUAD, but mutations involved in the epithelial-mesenchymal transition or cell cycle were found in both tumor types and in non-GGO tissue alone. These findings could explain the non-invasiveness of GGO-type LUAD.

Cancer classification of single-cell gene expression data by neural network

MOTIVATION

Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq).

RESULTS

We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples.

AVAILABILITY AND IMPLEMENTATION

Cancer classification by neural network.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rapid preliminary purity evaluation of tumor biopsies using deep learning approach

Tumor biopsy is one of the most widely used materials in cancer diagnoses and molecular studies, where the purity of the biopsies (i.e., proportion of cells that are cancerous) is crucial for both applications. However, conventional approaches for tumor biopsy purity evaluation require experienced pathologists and/or various materials/experiments therefore were time-consuming and error prone. Rapid, easy-to-perform and cost-effective methods are thus still of demand. Recent studies had demonstrated that molecular signatures were informative to this task. Previously, we had developed GeneCT, a deep learning-based cancerous status and tissue-of-origin classifier for pan-tumor/tissue biopsies. In the current work, we applied GeneCT on datasets collected from various groups, where the experimental protocols and cancer types differed from each other. We found that GeneCT showed high accuracies on most datasets; for samples with unexpected results, in-depth investigations suggested that they might suffer from imperfect purity. In silico mixture experiments further showed that GeneCT classification was highly indicative in predicting the purity of the tumor biopsies. Considering that transcriptome profiling is a common and inexpensive experiment in molecular cancer studies, our deep learning-based GeneCT could thus serve as a valuable tool for rapid, preliminary tumor biopsy purity assessment.

Upregulated expression of MMP family genes is associated with poor survival in patients with esophageal squamous cell carcinoma via regulation of proliferation and epithelial‑mesenchymal transition

Matrix metalloproteinases (MMPs) are involved in the cleavage of several components of the extracellular matrix and serve important roles in tumor growth, metastasis and invasion. Previous studies have focused on the expression of one or several MMPs in esophageal squamous cell carcinoma (ESCC); however, in the present study, the transcriptomics of all 23 MMPs were systematically investigated with a focus on the prognostic value of the combination of MMPs. In this study, 8 overlapping differentially expressed genes of the MMP family were identified based on data obtained from Gene Expression Omnibus and The Cancer Genome Atlas. The prognostic value of these MMPs were investigated; the receiver operating characteristic curves, survival curves and nomograms showed that the combination of 6 selected MMPs possessed a good predictive ability, which was more accurate than the prediction model based on Tumor‑Node‑Metastasis stage. Gene set enrichment analysis and gene co‑expression analysis were performed to investigate the potential mechanism of action of MMPs in ESCC. The MMP family was associated with several signaling pathways, such as epithelial‑mesenchymal transition (EMT), Notch, TGF‑β, mTOR and P53. Cell Counting Kit‑8, colony formation, wound healing assays and western blotting were used to determine the effect of BB‑94, a pan‑MMP inhibitor, on proliferation and migration of ESCC cells. BB‑94 treatment decreased ESCC cell growth, migration and EMT. Therefore, MMPs may serve both as diagnostic and prognostic biomarkers of ESCC, and MMP inhibition may be a promising preventive and therapeutic strategy for patients with ESCC.

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulated RNA-seq data has often yielded inconsistent results. The spike-in data, which were generated from the same bulk RNA sample, only represent technical variability, making the test results less reliable. We compared the performance of 12 differential expression analysis methods for RNA-seq data, including recent variants in widely used software packages, using both RNA spike-in and simulation data for negative binomial (NB) model. Performance of edgeR, DESeq2, and ROTS was particularly different between the two benchmark tests. Then, each method was tested under most extensive simulation conditions especially demonstrating the large impacts of proportion, dispersion, and balance of differentially expressed (DE) genes. DESeq2, a robust version of edgeR (edgeR.rb), voom with TMM normalization (voom.tmm) and sample weights (voom.sw) showed an overall good performance regardless of presence of outliers and proportion of DE genes. The performance of RNA-seq DE gene analysis methods substantially depended on the benchmark used. Based on the simulation results, suitable methods were suggested under various test conditions.

[Establishment and gene expression analysis of drug-resistant cell lines in hepatocellular carcinoma induced by sorafenib]

OBJECTIVE

To establish the drug-resistant cell lines of hepatocellular carcinoma (HCC) induced by sorafenib, and to screen out the high expression genes in drug-resistant cell lines of HCC induced by sorafenib, then to explore the genes related to sorafenib resistance in hepatocellular carcinoma.

METHODS

The human PLC and Huh7 cell lines were obtained, then the PLC and Huh7 drug-resistant cell lines were induced with sorafenib by using intermittent induction in vitro. CCK8 assay was used to detect the IC50 value of sorafenib for evaluation of drug sensitivity of hepatocellular carcinoma cell lines in PLC and Huh7. All the up regulated genes in PLC and Huh7 drug-resistant cell lines induced by sorafenib were screened out using high-throughput cDNA sequencing (RNA-Seq), Ualcan database was used to analyze the correlations between the up regulated genes in PLC and Huh7 drug-resistant cell lines induced and four clinical biological characteristics of hepatocellular carcinoma, including the gene expressions between normal samples and tumor samples, tumor stage, tumor grade, and patient overall survival, to find the genes that might be involved in the mechanism of sorafenib resistance of hepatocellular carcinoma.

RESULTS

All the up regulated genes detected by the using high-throughput cDNA sequencing (RNA-Seq) in PLC and Huh7 drug-resistant cell lines were further screened out by following conditions:(1) genes co-expressed in PLC and Huh7 drug-resistant cells induced by sorafenib, (2) the fold change was more than 4 times and the difference was statistically significant (P <0.05), the top 12 up regulated genes in PLC and Huh7 drug-resistant cell lines were found, which were TPSG1, CBX4, CLC, CLEC18C, LGI4, F2RL1, S100A6, HABP2, C15ORF48, ZG16, FOLH1, and EPCAM. Compared with the correlations between the twelve genes and the clinical biological characteristics by Ualcan database, the potentially significant gene CBX4 was screened out.

CONCLUSION

The human PLC and Huh7 drug-resistant cell lines of hepatocellular carcinoma induced by sorafenib were successfully established. CBX4, the gene related to sorafenib resistance in hepatocellular carcinoma, was screened out by the high-throughput cDNA sequencing (RNA-Seq) and further analysis using Ualcan database, which is providing a powerful basis for further research on the mechanism of sorafenib resistance of hepatocellular carcinoma.

Pan-cancer analysis reveals synergistic effects of CDK4/6i and PARPi combination treatment in RB-proficient and RB-deficient breast cancer cells

DNA damage results in mutations and plays critical roles in cancer development, progression, and treatment. Targeting DNA damage response in cancers by inhibiting poly-(ADP-ribose) polymerases (PARPs) offers an important therapeutic strategy. However, the failure of PARP inhibitors to markedly benefit patients suggests the necessity for developing new strategies to improve their efficacy. Here, we show that the expression of cyclin-dependent kinase 4/6 (CDK4/6) complex members significantly correlates with mutations (as proxies of DNA damages), and that the combination of CDK4/6 and PARP inhibitors shows synergy in both RB-proficient and RB-deficient breast cancer cells. As PARPs constitute sensors of DNA damage and are broadly involved in multiple DNA repair pathways, we hypothesized that the combined inhibition of PARPs and DNA repair (or repair-related) pathways critical for cancer (DRPCC) should show synergy. To identify druggable candidate DRPCC(s), we analyzed the correlation between the genome-wide expression of individual genes and the mutations for 27 different cancer types, assessing 7146 exomes and over 1,500,000 somatic mutations. Pathway enrichment analyses of the top-ranked genes correlated with mutations indicated “cell cycle pathway” as the top candidate DRPCC. Additionally, among functional cell-cycle complexes, the CDK4/6 complex showed the most significant negative correlation with mutations, also suggesting that combined CDK4/6 and PARP inhibition might exhibit synergy. Furthermore, combination treatment showed synergy in not only RB-proficient but also RB-deficient breast cancer cells in a reactive oxygen species-dependent manner. These findings suggest a potential therapeutic strategy to improve the efficacy of PARP and CDK4/6 inhibitors in cancer treatment.

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.

Transcriptome analysis of ankylosed primary molars with infraocclusion

Primary molar ankylosis with infraocclusion can retard dental arch development and cause dental asymmetry. Despite its widespread prevalence, little is known about its molecular etiology and pathogenesis. To address this, RNA sequencing was used to generate transcriptomes of furcal bone from infraoccluded (n = 7) and non-infraoccluded (n = 9) primary second molars, all without succeeding biscuspids. Of the 18 529 expressed genes, 432 (2.3%) genes were differentially expressed between the two groups (false discovery rate < 0.05). Hierarchical clustering and principal component analysis showed clear separation in gene expression between infraoccluded and non-infraoccluded samples. Pathway analyses indicated that molar ankylosis is associated with the expression of genes consistent with the cellular inflammatory response and epithelial cell turnover. Independent validation using six expressed genes by immunohistochemical analysis demonstrated that the corresponding proteins are strongly expressed in the developing molar tooth germ, in particular the dental follicle and inner enamel epithelium. The descendants of these structures include the periodontal ligament, cementum, bone and epithelial rests of Malassez; tissues that are central to the ankylotic process. We therefore propose that ankylosis involves an increased inflammatory response associated with disruptions to the developmental remnants of the dental follicle and epithelial rests of Malassez.

Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival

PURPOSE

To provide a better understanding of the interplay between the immune system and brain metastases to advance therapeutic options for this life-threatening disease.

EXPERIMENTAL DESIGN

Tumor-infiltrating lymphocytes (TIL) were quantified by semiautomated whole-slide analysis in brain metastases from 81 lung adenocarcinomas. Multi-color staining enabled phenotyping of TILs (CD3, CD8, and FOXP3) on a single-cell resolution. Molecular determinants of the extent of TILs in brain metastases were analyzed by transcriptomics in a subset of 63 patients. Findings in lung adenocarcinoma brain metastases were related to published multi-omic primary lung adenocarcinoma The Cancer Genome Atlas data (n = 230) and single-cell RNA-sequencing (scRNA-seq) data (n = 52,698).

RESULTS

TIL numbers within tumor islands was an independent prognostic marker in patients with lung adenocarcinoma brain metastases. Comparative transcriptomics revealed that expression of three surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) was closely associated with TIL numbers. Their expression was not only prognostic in brain metastasis but also in primary lung adenocarcinoma. Correlation with scRNA-seq data revealed that brain metastases with high expression of surfactant genes might originate from tumor cells resembling alveolar type 2 cells. Methylome-based estimation of immune cell fractions in primary lung adenocarcinoma confirmed a positive association between lymphocyte infiltration and surfactant expression. Tumors with a high surfactant expression displayed a transcriptomic profile of an inflammatory microenvironment.

CONCLUSIONS

The expression of surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) defines an inflamed subtype of lung adenocarcinoma brain metastases characterized by high abundance of TILs in close vicinity to tumor cells, a prolonged survival, and a tumor microenvironment which might be more accessible to immunotherapeutic approaches.

Cancer mutational burden is shaped by G4 DNA, replication stress and mitochondrial dysfunction

A hallmark of cancer is genomic instability, which can enable cancer cells to evade therapeutic strategies. Here we employed a computational approach to uncover mechanisms underlying cancer mutational burden by focusing upon relationships between 1) translocation breakpoints and the thousands of G4 DNA-forming sequences within retrotransposons impacting transcription and exemplifying probable non-B DNA structures and 2) transcriptome profiling and cancer mutations. We determined the location and number of G4 DNA-forming sequences in the Genome Reference Consortium Human Build 38 and found a total of 358,605 covering ∼13.4 million bases. By analyzing >97,000 unique translocation breakpoints from the Catalogue Of Somatic Mutations In Cancer (COSMIC), we found that breakpoints are overrepresented at G4 DNA-forming sequences within hominid-specific SVA retrotransposons, and generally occur in tumors with mutations in tumor suppressor genes, such as TP53. Furthermore, correlation analyses between mRNA levels and exome mutational loads from The Cancer Genome Atlas (TCGA) encompassing >450,000 gene-mutation regressions revealed strong positive and negative associations, which depended upon tissue of origin. The strongest positive correlations originated from genes not listed as cancer genes in COSMIC; yet, these show strong predictive power for survival in most tumor types by Kaplan-Meier estimation. Thus, correlation analyses of DNA structure and gene expression with mutation loads complement and extend more traditional approaches to elucidate processes shaping genomic instability in cancer. The combined results point to G4 DNA, activation of cell cycle/DNA repair pathways, and mitochondrial dysfunction as three major factors driving the accumulation of somatic mutations in cancer cells.

KH-Type Splicing Regulatory Protein Controls Colorectal Cancer Cell Growth and Modulates the Tumor Microenvironment

KH-type splicing regulatory protein (KHSRP) is a multifunctional nucleic acid binding protein implicated in key aspects of cancer cell biology: inflammation and cell-fate determination. However, the role KHSRP plays in colorectal cancer (CRC) tumorigenesis remains largely unknown. Using a combination of in silico analysis of large data sets, ex vivo analysis of protein expression in patients, and mechanistic studies using in vitro models of CRC, we investigated the oncogenic role of KHSRP. We demonstrated KHSRP expression in the epithelial and stromal compartments of both primary and metastatic tumors. Elevated expression was found in tumor versus matched normal tissue, and these findings were validated in larger independent cohorts in silico. KHSRP expression was a prognostic indicator of worse overall survival (hazard ratio, 3.74; 95% CI, 1.43-22.97; P = 0.0138). Mechanistic data in CRC cell line models supported a role of KHSRP in driving epithelial cell proliferation in both a primary and metastatic setting, through control of the G1/S transition. In addition, KHSRP promoted a proangiogenic extracellular environment by regulating the secretion of oncogenic proteins involved in diverse cellular processes, such as migration and response to cellular stress. Our study provides novel mechanistic insight into the tumor-promoting effects of KHSRP in CRC.

Generating topological protein interaction scores and data visualization with TopS

Detecting subnetworks in large networks is of great interest. Recently, we developed a topological score framework for the analysis of protein interaction networks and implemented it as a web application, called TopS. Given a multivariate data presented as a matrix, TopS generates topological scores between any column and row in the matrix aiming to identify overwhelming preference interactions. This information can be further used into visualization tools such as clusters and networks to investigate how networks benefit from these interactions. We present a web tool called TopS that aims to have an intuitive user interface. Users can upload data from a simple delimited CSV file that can be created in a spreadsheet program. As an output, user is provided with a scoring matrix as tab-delimited file that can be interchanged with other software, heatmap and clustering figures in pdf format. Here we demonstrate the current capabilities of TopS using an existing dataset generated for the study of the human Sin3 chromatin remodeling complex.

Wx: a neural network-based feature selection algorithm for transcriptomic data

Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. However, the amount of information produced by NGS has made it difficult for researchers to choose the optimal set of genes. We have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method. The stand-alone and web versions of the Wx algorithm are available at https://github.com/deargen/DearWXpub and https://wx.deargendev.me/, respectively.

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.

Histone variants: critical determinants in tumour heterogeneity

Malignant cell transformation could be considered as a series of cell reprogramming events driven by oncogenic transcription factors and upstream signalling pathways. Chromatin plasticity and dynamics are critical determinants in the control of cell reprograming. An increase in chromatin dynamics could therefore constitute an essential step in driving oncogenesis and in generating tumour cell heterogeneity, which is indispensable for the selection of aggressive properties, including the ability of cells to disseminate and acquire resistance to treatments. Histone supply and dosage, as well as histone variants, are the best-known regulators of chromatin dynamics. By facilitating cell reprogramming, histone under-dosage and histone variants should also be crucial in cell transformation and tumour metastasis. Here we summarize and discuss our knowledge of the role of histone supply and histone variants in chromatin dynamics and their ability to enhance oncogenic cell reprogramming and tumour heterogeneity.

Clinical significance of miRNA‑1 and its potential target gene network in lung squamous cell carcinoma

Previous studies demonstrated that miRNA-1 (miR-1) is downregulated in certain human cancer and serves a crucial role in the progression of cancer. However, there are only a few previous studies examining the association between miR-1 and lung squamous cell carcinoma (LUSC) and the regulatory mechanism of miR-1 in LUSC remains unclear. Therefore, the present study investigated the clinical significance and determined the potential molecular mechanism of miR-1 in LUSC. The expression of miR-1 and its clinical significance in LUSC was examined by conducting a meta-analysis of 12 studies using Stata 14, MetaDiSc1.4 and SPSS version 23. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the potential target genes of miR-1 gathered from Gene Expression Omnibus and ArrayExpress. Meta-analysis demonstrated that miR-1 was significantly downregulated in LUSC [standardized mean difference: −1.44; 95% confidence interval (CI): −2.08, −0.81], and the area under the curve was 0.9096 (Q*=0.8416) with sensitivity of 0.71 (95% CI: 0.66, 0.76) and specificity of 0.88 (95% CI: 0.86, 0.90). The pooled positive likelihood ratio and negative likelihood ratio were 4.93 (95% CI: 2.54, 9.55) and 0.24 (95% CI: 0.10, 0.54), respectively. Bioinformatics analysis demonstrated that miR-1 may be involved in the progression of LUSC via the ‘cell cycle’, ‘p53 signaling pathway’, ‘Fanconi anemia pathway’, ‘homologous recombination’, ‘glycine, serine and threonine metabolism’ and ‘oocyte meiosis’. In summary, miR-1 was significantly downregulated in LUSC, suggesting a novel and promising non-invasive biomarker for diagnosing LUSC, and miR-1 was involved in LUSC progression via a number of significant pathways.

Noncoding RNAs as Molecular Targets of Resveratrol Underlying Its Anticancer Effects

Cancer is a significant disease burden worldwide. Chemotherapy is the mainstay of cancer treatment. Clinically used chemotherapeutic agents may elicit severe side effects. Remarkably, most of cancer cells develop chemoresistance after a period of treatment. Therefore, it is imperative to seek more effective agents without side effects. In recent years, increasing research efforts have attempted to identify natural agents that may be used alone or in combination with traditional therapeutics for cancer management. Resveratrol is a natural polyphenolic phytoalexin that can be found in various foods including blueberries, peanuts, and red wine. As a natural food ingredient, resveratrol possesses antioxidant, anti-inflammatory, and cardioprotective properties. Moreover, resveratrol exhibited promising effects in suppressing the initiation and progression of cancers. Noncoding RNAs (ncRNAs) have been universally accepted as vital regulators in cancer pathogenesis. The modulation of miRNAs and lncRNAs by resveratrol has been described. Thus, the mechanism involving the domination of ncRNA function is one of the keys to understand the anticancer effects of resveratrol. In this review, we focus on the antagonistic effects of resveratrol on cancer progression through regulation of miRNAs and lncRNAs. We also discuss the potential application of resveratrol in cancer management.

Mining and validating the expression pattern and prognostic value of acetylcholine receptors in non-small cell lung cancer

Acetylcholine receptors (AChRs), including nicotinic acetylcholine receptors (nAChRs) and muscarinic acetylcholine receptors (mAChRs), are highly expressed in bronchial epithelial cells.We used The Cancer Genome Atlas (TCGA) data set to evaluate the expression pattern and prognostic value of the AChR gene family in non-small cell lung cancer (NSCLC). The mined data was validated by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).The survival analysis of TCGA data set showed that only CHRNA7 in the AChR gene family affected prognosis in both lung adenocarcinoma and lung squamous cell carcinoma. Furthermore, qRT-PCR proved that CHRNA7 was significantly upregulated in tumor tissues compared with matched normal tissues at mRNA level (P = .001). The expression level of α7 nAChR (encoded by CHRNA7) in 141 patients was measured by IHC and a high expression of α7 nAChR was associated with unfavorable prognosis (P = .008). Multivariate analysis showed that α7 nAChR was an independent prognostic factor (HR = 2.041; 95% CI 1.188-3.506; P = .007).α7 nAChR was upregulated in NSCLC and was associated with unfavorable prognosis. This gene may be a potential target for lung cancer treatment.

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Background

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported.

Methods

Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them.

Results

Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD.

Conclusions

Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

Electronic supplementary material

The online version of this article (10.1186/s13229-019-0262-8) contains supplementary material, which is available to authorized users.

Cancer Genetic Network Inference Using Gaussian Graphical Models

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.