The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical-genomic driver associations

Prognostic predictive modeling of non-small cell lung cancer associated with cadmium-related pathogenic genes

Persistent exposure to low-dose of cadmium is strongly linked to both the development and prognosis of non-small cell lung cancer (NSCLC), yet the precise molecular mechanism behind this relationship remains uncertain. In this study, cadmium-related pathogenic genes (CRPGs) in NSCLC were identified via differential expression analysis. NSCLC patient clusters related to CRPGs were constructed through univariate Cox and K-means clustering algorithms. Multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to determine the prognosis. Sixteen CRPGs showed a significant association with NSCLC. We found biological and prognostic differences between patients in clusters A and B. A predictive prognostic risk model for NSCLC revealed that FAM83H, MSMO1, and SNAI1 are central. Hence, the 3 hub genes were named. To further elucidate the role of CRPGs in NSCLC, A549 cells were exposed to CdCl2. The mRNA and protein expression levels of the 3 hub genes and cell invasion were detected. Moreover, 10 μM CdCl2 may increase the protein expression of 3 hub genes and enhance the invasive ability of A549 cells. This risk model may have established a theoretical foundation for investigating the mechanisms, treatment, and prognosis of NSCLC.

Novel cancer-fighting role of ticagrelor inhibits GTSE1-induced EMT by regulating PI3K/Akt/NF-κB signaling pathway in malignant glioma

Background

Glioma is the most common malignant brain tumor of the central nervous system. Despite of the improvement of therapeutic strategy, the prognosis of malignant glioma patients underwent by STUPP strategy is still unexpected. Previous studies have suggested that ticagrelor exerted chemotherapeutic effects by inhibition of epithelial-mesenchymal transition (EMT) in various diseases including tumors. However, whether ticagrelor can exhibit the antitumor efficiency in glioma by affecting the EMT process is still unclear. In this study, we investigated the cancer-fighting role of ticagrelor and demonstrated its chemotherapeutic mechanism in glioma.

Materials and methods

The MTT assay was performed to detect the cytotoxicity of ticagrelor in glioma cells. We evaluated the expression of Ki67 in glioma cells by immunofluorescence assay after ticagrelor treatment. We conducted wound healing assay and transwell assay to determine the effects of ticagrelor on the migration and invasion of glioma cells. RNA-seq analysis was conducted to examine potential target genes and alternative signaling pathways for ticagrelor treatment. The expression levels of key EMT -related proteins were examined by Western blot experiment.

Results

Ticagrelor inhibited the proliferation, migration and invasion of glioma cells with a favorable toxicity profile in vitro. Ticagrelor downregulated the expression of GTSE1 in glioma cells. RNA-seq analysis explored that GTSE1 acted as the potential target gene for ticagrelor treatment. Upregulation of GTSE1 antagonized the inhibitory effect of ticagrelor on the invasion of glioma and EMT progression by regulation of PI3K/Akt/NF-κB signaling pathway. And ticagrelor also exhibited the similar chemotherapeutic effect of glioma in vivo.

Conclusions

Ticagrelor as a potential chemotherapeutic option induced the inhibition of the GTSE1-induced EMT progression by regulation of PI3K/AKT/NF-κB signaling pathway.

Effect of radiotherapy on head and neck cancer tissues in patients receiving radiotherapy: a bioinformatics analysis-based study

Radiotherapy is pivotal in treating head and neck cancers including nasopharyngeal, tongue, hypopharyngeal, larynx, maxillary sinus, parotid gland, and oral cancers. It holds the potential for curative effects and finds application in conjunction with chemotherapy, either as a radical method to preserve organ function or as an adjuvant postoperative treatment. We used bioinformatics analysis to investigate the effects of radiotherapy on head and neck cancer tissues in patients who had received radiotherapy. In this study, the expression and mutation profiles of The Cancer Genome Atlas-Head-Neck Squamous Cell Carcinoma were downloaded from the UCSC-Xena database, categorizing patients into two groups-those receiving radiotherapy and those not receiving radiotherapy. Subsequently, differential expression analysis and gene set enrichment analysis (GSEA) were performed. Following this, single-sample GSEA (ssGSEA) scores related to glucose and lipid metabolism were compared between the two groups. Additionally, immune cell infiltration analysis and single-cell verification were performed. Finally, the mutation profiles of the two groups were compared. The analyses revealed that patients receiving radiotherapy exhibited prolonged survival, enhanced apoptosis in head and neck cancer tissue, and diminished keratinocyte proliferation and migration. A comparison of ssGSEA scores related to glucose and lipid metabolism between the two groups indicated a reduction in glycolysis, tricarboxylic acid cycle activity, and fat synthesis in tissues treated with radiotherapy, suggesting that radiotherapy can effectively inhibit tumour cell energy metabolism. Analyses of immune cell infiltration and single-cell verification suggested decreased infiltration of immune cells post-radiotherapy in head and neck cancer tissues. A comparison of mutation profiles revealed a higher frequency of TP53, TTN, and CDKN2A mutations in patients receiving radiotherapy for head and neck cancer. In conclusion, the bioinformatics analyses delved into the effect of radiotherapy on patients with head and neck carcinoma. This study provides a theoretical framework elucidating the molecular mechanisms underlying radiotherapy's efficacy in treating head and neck cancer and presents scientific recommendations for drug therapy following radiotherapy.

Venetoclax abrogates the prognostic impact of splicing factor gene mutations in newly diagnosed acute myeloid leukemia

Mutations in splicing factor (SF) genes SRSF2, U2AF1, SF3B1, and ZRSR2 are now considered adverse risk in the European LeukemiaNet 2022 acute myeloid leukemia (AML) risk stratification. The prognostic impact of SF mutations in AML has been predominantly derived from younger patients treated with intensive (INT) therapy. We evaluated 994 patients with newly diagnosed AML, including 266 (27%) with a SFmut. Median age was 67 years overall, with patients with SFmut being older at 72 years. SRSF2 (n = 140, 53%) was the most common SFmut. In patients treated with INT, median relapse-free survival (RFS) (9.6 vs 21.4 months, P = .04) and overall survival (OS) (15.9 vs 26.7 months, P = .06) were shorter for patients with SFmut than without SFwt, however this significance abrogated when evaluating patients who received venetoclax with INT therapy (RFS 15.4 vs 20.3 months, P = .36; OS 19.6 vs 30.7 months, P = .98). In patients treated with LI, median RFS (9.3 vs 7.7 months, P = .35) and OS (12.3 vs 8.5 months, P = .14) were similar for patients with and without SFmut , and outcomes improved in all groups with venetoclax. On multivariate analysis, SFmut did not affect hazards of relapse and death for INT arm but reduced both these hazards in LI arm. In a large AML data set with >60% of patients receiving venetoclax with LI/INT therapy, SFmut had no independent negative prognostic impact. Newer prognostic models that consider LI therapy and use of venetoclax among other factors are warranted.

MAF1 is a predictive biomarker in HER2 positive breast cancer

RNA polymerase III transcription is pivotal in regulating cellular growth and frequently deregulated in various cancers. MAF1 negatively regulates RNA polymerase III transcription. Currently, it is unclear if MAF1 is universally deregulated in human cancers. Recently, MAF1 expression has been demonstrated to be altered in colorectal and liver carcinomas and Luminal B breast cancers. In this study, we analyzed clinical breast cancer datasets to determine if MAF1 alterations correlate with clinical outcomes in HER2-positive breast cancer. Using various bioinformatics tools, we screened breast cancer datasets for alterations in MAF1 expression. We report that MAF1 is amplified in 39% of all breast cancer sub-types, and the observed amplification co-occurs with MYC. MAF1 amplification correlated with increased methylation of the MAF1 promoter and MAF1 protein expression is significantly decreased in luminal, HER2-positive, and TNBC breast cancer subtypes. MAF1 protein expression is also significantly reduced in stage 2 and 3 breast cancer compared to normal and significantly decreased in all breast cancer patients, regardless of race and age. In SKBR3 and BT474 breast cancer cell lines treated with anti-HER2 therapies, MAF1 mRNA expression is significantly increased. In HER2-positive breast cancer patients, MAF1 expression significantly increases and correlates with five years of relapse-free survival in response to trastuzumab treatment, suggesting MAF1 is a predictive biomarker in breast cancer. These data suggest a role for MAF1 alterations in HER2-positive breast cancer. More extensive studies are warranted to determine if MAF1 serves as a predictive and prognostic biomarker in breast cancer.

Recent advances of pathomics in colorectal cancer diagnosis and prognosis

Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.

Colorectal Cancer Liver Metastases: Genomics and Biomarkers with Focus on Local Therapies

Molecular cancer biomarkers help personalize treatment, predict oncologic outcomes, and identify patients who can benefit from specific targeted therapies. Colorectal cancer (CRC) is the third-most common cancer, with the liver being the most frequent visceral metastatic site. KRAS, NRAS, BRAF V600E Mutations, DNA Mismatch Repair Deficiency/Microsatellite Instability Status, HER2 Amplification, and NTRK Fusions are NCCN approved and actionable molecular biomarkers for colorectal cancer. Additional biomarkers are also described and can be helpful in different image-guided hepatic directed therapies specifically for CRLM. For example, tumors maintaining the Ki-67 proliferation marker after thermal ablation was shown to be particularly resilient to ablation. Ablation margin was also shown to be an important factor in predicting local recurrence, with a ≥10 mm minimal ablation margin being required to attain local tumor control, especially for patients with mutant KRAS CRLM.

BDP1 as a biomarker in serous ovarian cancer

BACKGROUND

TFIIIB, an RNA polymerase III specific transcription factor has been found to be deregulated in human cancers with much of the research focused on the TBP, BRF1, and BRF2 subunits. To date, the TFIIIB specific subunit BDP1 has not been investigated in ovarian cancer but has previously been shown to be deregulated in neuroblastoma, breast cancer, and Non-Hodgkins lymphoma.

RESULTS

Using in silico analysis of clinically derived platforms, we report a decreased BDP1 expression as a result of deletion in serous ovarian cancer and a correlation with higher and advanced ovarian stages. Further analysis in the context of TP53 mutations, a major contributor to ovarian tumorigenesis, suggests that high BDP1 expression is unfavorable for overall survival and high BDP1 expression occurs in stages 2, 3 and 4 serous ovarian cancer. Additionally, high BDP1 expression is disadvantageous and unfavorable for progression-free survival. Lastly, BDP1 expression significantly decreased in patients treated with first-line chemotherapy, platin and taxane, at twelve-month relapse-free survival.

CONCLUSIONS

Taken together with a ROC analysis, the data suggest BDP1 could be of clinical relevance as a predictive biomarker in serous ovarian cancer. Lastly, this study further demonstrates that both the over- and under expression of BDP1 warrants further investigation and suggests BDP1 may exhibit dual function in the context of tumorigenesis.

Comprehensive Pan-Cancer Analysis of GINS2 for Human Tumour Prognosis and as an Immunological Biomarker

BACKGROUND

In recent years, more and more reports have shown that GINS complex subunit 2 (GINS2) plays an important role in the occurrence and progression of tumours. However, there is a lack of comprehensive and systematic research on its prognostic and immune effects in pan-cancer. Therefore, this study is aimed at investigating the prognostic value and immune-related role of GINS2 in human tumours and providing a comprehensive understanding of its carcinogenic mechanism in pan-cancer.

METHODS

We investigated different databases, including TIMER, TCGA, GTEX, CPTAC, GEPIA, and SangerBox. The study was carried out on the expression and prognosis of GINS2 in human tumours, immune infiltration and microenvironment, immune checkpoints, neoantigens, tumour mutational burden, microsatellite instability, mismatch repair (MMR) genes, methylation, cancer-associated fibroblasts (CAFs), and enrichment analysis of gene set.

RESULTS

GINS2 plays a potential carcinogenic role in various human tumours through mRNA and protein levels. It is highly expressed in most cancers, and its expression is significantly correlated with tumour prognosis. In addition, the expression of GINS2 is associated with immune microenvironment and immune infiltration, especially in brain lower-grade glioma, lung squamous cell carcinoma, TGCT, breast invasive carcinoma, and glioblastoma multiforme. At the same time, GINS2 is related to immune neoantigens and the expression profiles of immune checkpoint genes in pan-cancer. It also affects the expression of DNA MMR genes and methyltransferase in pan-cancer. Finally, the correlation between GINS2 and CAF abundance in most tumours was studied, and an enrichment analysis of GINS2 and its related proteins was also carried out.

CONCLUSION

This is the first study on GINS2 as a prognostic and immune mechanism in pan-cancer. GINS2 may be a valuable prognostic immunological biomarker of pan-cancer. This paper provides a relatively comprehensive understanding on the correlation of GINS2 with pan-cancer.

Intra-tumoral infiltration of adipocyte facilitates the activation of antitumor immune response in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal malignancy that is characterized by an immunosuppressive microenvironment. The immune suppression in PDAC is largely driven by heterogeneous stromal and tumor cells. However, how adipocyte in the tumor microenvironment (TME) is related to the immune cell infiltration in PDAC has rarely been published. We identified adipocytes by performing bioinformatics analyses, and explored the clinical outcomes and TME characters in PDAC with different levels of adipocyte infiltration. Interestingly, in contrast to adiposity, high adipocyte infiltration in the TME was related to significantly increased median overall survival and a lower total tumor mutational burden. Functionally, high adipocyte infiltration was associated with the immune response, particularly with the abundant cytokine infiltration in PDAC samples. Moreover, adipocyte infiltration in the TME was positively associated with anticancer signatures in the immune microenvironment. Immunohistochemistry and RT-PCR were performed with PDAC tissue samples from our center to study the expression of adipocytes in PDAC. The mature adipocytes were strongly associated with the immune composition and prognosis of patients with PDAC. Primary adipocytes were isolated from mice to construct a PDAC transplantation tumor model. In vivo experiments showed that adipocytes elicited increased CD8+ T cell infiltration and potent antitumor activity in tumor-bearing mice. Overall, we innovatively found that adipocytes facilitated the antitumor immune response in the TME by performing mouse experiments and analyzing PDAC samples. This study provides a new perspective on the activation of the immune microenvironment in PDAC.

Gene Expression and DNA Methylation in Human Papillomavirus Positive and Negative Head and Neck Squamous Cell Carcinomas

High-risk human papillomaviruses (HPV) are important agents, responsible for a large percentage of the 745,000 cases of head and neck squamous cell carcinomas (HNSCC), which were identified worldwide in 2020. In addition to being virally induced, tobacco and heavy alcohol consumption are believed to cause DNA damage contributing to the high number of HNSCC cases. Gene expression and DNA methylation differ between HNSCC based on HPV status. We used publicly available gene expression and DNA methylation profiles from the Cancer Genome Atlas and compared HPV positive and HPV negative HNSCC groups. We used differential gene expression analysis, differential methylation analysis, and a combination of these two analyses to identify the differences. Differential expression analysis identified 1854 differentially expressed genes, including PCNA, TNFRSF14, TRAF1, TRAF2, BCL2, and BIRC3. SYCP2 was identified as one of the top deregulated genes in the differential methylation analysis and in the combined differential expression and methylation analyses. Additionally, pathway and ontology analyses identified the extracellular matrix and receptor interaction pathway as the most altered between HPV negative and HPV positive HNSCC groups. Combining gene expression and DNA methylation can help in elucidating the genes involved in HPV positive HNSCC tumorigenesis, such as SYCP2 and TAF7L.

BDP1 Alterations Correlate with Clinical Outcomes in Breast Cancer

Simple Summary

Breast cancer accounts for 30% of all new cancer diagnoses in the United States. The most common type of breast cancer is invasive breast cancer. A hallmark trait of breast cancer is uncontrolled cell growth due to genetic alterations. TFIIIB-mediated RNA polymerase III transcription is specifically deregulated in human cancers. The TFIIIB BDP1 subunit is not well characterized in human cancer. The objective of this study was to analyze publicly available clinical cancer datasets to determine if BDP1 alterations correlate with clinical outcomes in available breast cancer datasets. BDP1 copy number and expression negatively correlate with breast cancer outcomes, including stage, grade, and mortality.

Abstract

TFIIIB is deregulated in a variety of cancers. However, few studies investigate the TFIIIB subunit BDP1 in cancer. BDP1 has not been studied in breast cancer patients. Herein, we analyzed clinical breast cancer datasets to determine if BDP1 alterations correlate with clinical outcomes. BDP1 copy number (n = 1602; p = 8.03 × 10⁻⁹) and mRNA expression (n = 130; p = 0.002) are specifically decreased in patients with invasive ductal carcinoma (IDC). In IDC, BDP1 copy number negatively correlates with high grade (n = 1992; p = 2.62 × 10⁻¹⁹) and advanced stage (n = 1992; p = 0.005). BDP1 mRNA expression also negatively correlated with high grade (n = 55; p = 6.81 × 10⁻⁴) and advanced stage (n = 593; p = 4.66 × 10⁻⁴) IDC. Decreased BDP1 expression correlated with poor clinical outcomes (n = 295 samples): a metastatic event at three years (p = 7.79 × 10⁻⁷) and cancer reoccurrence at three years (p = 4.81 × 10⁻⁷) in IDC. Decreased BDP1 mRNA correlates with patient death at three (p = 9.90 × 10⁻⁶) and five (p = 1.02 × 10⁻⁶) years. Both BDP1 copy number (n = 3785; p = 1.0 × 10⁻¹⁴) and mRNA expression (n = 2434; p = 5.23 × 10⁻⁶) are altered in triple-negative invasive breast cancer (TNBC). Together, these data suggest a role for BDP1 as potential biomarker in breast cancer and additional studies are warranted.

Drug repurposing in silico screening platforms

Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

An Integrative Pan-Cancer Analysis of the Oncogenic Role of COPB2 in Human Tumors

Several studies have suggested that coatomer protein complex subunit beta 2 (COPB2) may act as an oncogene in various cancer types. However, no systematic pan-cancer analysis has been performed to date. Therefore, the present study analyzed the potential oncogenic role of COPB2 using TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) datasets. The majority of the cancer types overexpressed the COPB2 protein, and its expression significantly correlated with tumor prognosis. In certain tumors, such as those found in breast and ovarian tissues, phosphorylated S859 exhibited high expression. It was found that mutations of the COPB2 protein in kidney and endometrial cancers exhibited a significant impact on patient prognosis. It is interesting to note that COPB2 expression correlated with the number of cancer-associated fibroblasts in certain tumors, such as cervical and endocervical cancers and colon adenocarcinomas. In addition, COPB2 was involved in the transport of substances and correlated with chemotherapy sensitivity. This is considered the first pan-tumor study, which provided a relatively comprehensive understanding of the mechanism by which COPB2 promotes cancer growth.

Tailored graphical lasso for data integration in gene network reconstruction

BACKGROUND

Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively.

RESULTS

We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks.

CONCLUSIONS

The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.

lncExplore: a database of pan-cancer analysis and systematic functional annotation for lncRNAs from RNA-sequencing data

Over the past few years, with the rapid growth of deep-sequencing technology and the development of computational prediction algorithms, a large number of long non-coding RNAs (lncRNAs) have been identified in various types of human cancers. Therefore, it has become critical to determine how to properly annotate the potential function of lncRNAs from RNA-sequencing (RNA-seq) data and arrange the robust information and analysis into a useful system readily accessible by biological and clinical researchers. In order to produce a collective interpretation of lncRNA functions, it is necessary to integrate different types of data regarding the important functional diversity and regulatory role of these lncRNAs. In this study, we utilized transcriptomic sequencing data to systematically observe and identify lncRNAs and their potential functions from 5034 The Cancer Genome Atlas RNA-seq datasets covering 24 cancers. Then, we constructed the 'lncExplore' database that was developed to comprehensively integrate various types of genomic annotation data for collective interpretation. The distinctive features in our lncExplore database include (i) novel lncRNAs verified by both coding potential and translation efficiency score, (ii) pan-cancer analysis for studying the significantly aberrant expression across 24 human cancers, (iii) genomic annotation of lncRNAs, such as cis-regulatory information and gene ontology, (iv) observation of the regulatory roles as enhancer RNAs and competing endogenous RNAs and (v) the findings of the potential lncRNA biomarkers for the user-interested cancers by integrating clinical information and disease specificity score. The lncExplore database is to our knowledge the first public lncRNA annotation database providing cancer-specific lncRNA expression profiles for not only known but also novel lncRNAs, enhancer RNAs annotation and clinical analysis based on pan-cancer analysis. lncExplore provides a more complete pathway to highly efficient, novel and more comprehensive translation of laboratory discoveries into the clinical context and will assist in reinterpreting the biological regulatory function of lncRNAs in cancer research. Database URL http://lncexplore.bmi.nycu.edu.tw.

A novel lncRNA-miRNA-mRNA competing endogenous RNA regulatory network in lung adenocarcinoma and kidney renal papillary cell carcinoma

Background

GPRIN1 may be a novel tumor regulator, but its role and mechanism in tumors are still unclear.

Methods

First, a pan‐cancer correlation analysis was conducted on the expression and prognosis of GPRIN1 based on the data downloaded from The Cancer Genome Atlas (TCGA) database. Second, the Starbase database was used to predict the upstream miRNAs and lncRNAs of GPRIN1, and the expression analysis, survival analysis, and correlation analysis were performed to screen the microRNA (miRNAs)/long non‐coding RNAs (lncRNAs) that had a correlation with kidney renal papillary cell carcinoma (KIRP) or lung adenocarcinoma (LUAD). Third, the CIBERSORT algorithm was employed to calculate the proportion of various types of immune cells, and then the R packages were used for evaluating the relation between GPRIN1 expression and tumor immune cell infiltration as well as between GPRIN1 and the immune cell biomarker. Finally, the correlation analysis was made on GPRIN1 and immune checkpoints (CD274, CTLA4, and PDCD1).

Results

The pan‐cancer analysis suggested that GPRIN1 was up‐expressed in KIRP and LUAD, and it correlated with poor prognosis. LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG‐miR‐140‐3p was likely to be the most promising upstream regulation pathway of GPRIN1. Upexpression of LINC00894/MMP25‐AS1/SNHG1/LINC02298/MIR193BHG and downexpression of miR‐140‐3p were found relevant with poor outcomes of KIRP and LUAD. GPRIN1 expression was significantly correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoints.

Conclusions

The competitive endogenous (ceRNA) of miR‐140‐3p‐GPRIN1 axis and its upstream lncRNAs are closely related to KIRP and LUAD, and might affect the prognosis and therapeutic effect of KIRP and LUAD.

Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods

Colorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to the malignant progression of CRC (metastasis, p = 0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein-protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine-cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in two independent cohorts (GSE17538 and GSE161158). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data, can aid in the development of novel personalized cancer therapeutic strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate such preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with colorectal cancer patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). We employed cell-type-specific RNA-seq gene expression data from the FlyGut-seq database to annotate and then validate these networks. Next, we developed three literature-based colorectal cancer case studies to evaluate cell fate outcomes from the model. Results obtained from analyses of the proposed DPM help: (i) elucidate cell fate evolution in colorectal tumorigenesis, (ii) validate cytotoxicity of nine FDA-approved CRC drugs, and (iii) devise optimal personalized treatment combinations. The personalized network models helped identify synergistic combinations of paclitaxel-regorafenib, paclitaxel-bortezomib, docetaxel-bortezomib, and paclitaxel-imatinib for treating different colorectal cancer patients. Follow-on therapeutic screening of six colorectal cancer patients from cBioPortal using this drug combination demonstrated a 100% increase in apoptosis and a 100% decrease in proliferation. In conclusion, this work outlines a novel roadmap for decoding colorectal tumorigenesis along with the development of personalized combinatorial therapeutics for preclinical translational studies.

In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

Background

Identification of factors associated with proliferation in the hepatocellular carcinoma (HCC) microenvironment aids in understanding the mechanisms of disease progression and provides druggable targets. Gene expression profiles of individual cells in HCC and para-carcinoma tissues can be effectively obtained using the single-cell RNA sequencing (scRNA-Seq) technique. Here, we aimed to identify proliferative hepatocytes from HCC and para-carcinoma tissues, detect differentially expressed genes between the two types of proliferative hepatocytes, and investigate their potential roles in aberrant proliferation.

Results

Two respective gene signatures for proliferative cells and hepatocytes were established and used to identify proliferative hepatocytes from HCC and para-carcinoma tissues based on scRNA-Seq data. Gene expression profiles between the two types of proliferative hepatocytes were compared. Overall, 40 genes were upregulated in proliferative hepatocytes from para-carcinoma tissue, whereas no upregulated genes were detected in those from HCC tissue. Twelve of the genes, including HAMP, were specifically expressed in the liver tissue. Based on previous reports, we found that HAMP modulates cell proliferation through interaction with its receptor SLC40A1. Comprehensive analysis of cells in HCC and para-carcinoma tissues revealed that: (1) HAMP is specifically expressed in hepatocytes and significantly downregulated in malignant hepatocytes; (2) a subset of macrophages expressing SLC40A1 and genes reacting to various infections is present in para-carcinoma but not in HCC tissue. We independently validated the findings with scRNA-Seq and large-scale tissue bulk RNA-Seq/microarray analyses.

Conclusion

HAMP was significantly downregulated in malignant hepatocytes. In addition, a subset of macrophages expressing SLC40A1 and genes reacting to various infections was absent in HCC tissue. These findings support the involvement of HAMP-SLC40A1 signaling in aberrant hepatocyte proliferation in the HCC microenvironment. The collective data from our in silico analysis provide novel insights into the mechanisms underlying HCC progression and require further validation with wet laboratory experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-00977-0.

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P < .001). A total of 540 genes were screened as DEGs with 539 up‐regulated genes and 1 down‐regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine‐cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4,MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment‐related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

GTSE1 promotes prostate cancer cell proliferation via the SP1/FOXM1 signaling pathway

G2 and S phase-expressed-1 (GTSE1) has been implicated in the pathogenesis of several malignant tumors. However, its specific role in prostate cancer (PCa) remains unclear. In this study, RNA-Seq data from patients with PCa and controls were downloaded from the FIREBROWSE database, and it was found that the GTSE1 mRNA level was significantly upregulated in PCa. Moreover, patients with higher GTSE1 mRNA levels had higher Gleason scores (P < 0.001), a more advanced pT stage (P = 0.011), and a more advanced pN stage (P = 0.006) as well as a shorter time to biochemical recurrence (P = 0.005). In addition, overexpression of GTSE1 could promote proliferation in LNCaP cells, whereas silencing GTSE1 could inhibit the growth of C4-2 cells in vitro and in vivo. Mechanistically, GTSE1 enhanced the expression of FOXM1 by upregulating the SP1 protein level, a transcription factor of FOXM1, which ultimately promoted PCa cell proliferation. In summary, GTSE1 is a new candidate oncogene in the development and progression of PCa, and it can promote PCa cell proliferation via the SP1/FOXM1 signaling pathway.

siGCD: a web server to explore survival interaction of genes, cells and drugs in human cancers

It is pivotal and remains challenge for cancer precision treatment to identify the survival outcome interactions between genes, cells and drugs. Here, we present siGCD, a web-based tool for analysis and visualization of the survival interaction of Genes, Cells and Drugs in human cancers. siGCD utilizes the cancer heterogeneity to simulate the manipulated gene expression, cell infiltration and drug treatment, which overcomes the data and experimental limitations. To illustrate the performance of siGCD, we identified the survival interaction partners of EGFR (gene level), T cells (cell level) and sorafenib (drug level), and our prediction was consistent with previous reports. Moreover, we validate the synergistic effect of regorafenib and glyburide, and found that glyburide could significantly improve the regorafenib response. These results demonstrate that siGCD could benefit cancer precision medicine in a wide range of advantageous application scenarios including gene regulatory network construction, immune cell regulatory gene identification, drug (especially multiple target drugs) response biomarker screening and combination therapeutic design.

A novel hypoxic long noncoding RNA KB-1980E6.3 maintains breast cancer stem cell stemness via interacting with IGF2BP1 to facilitate c-Myc mRNA stability

The hostile hypoxic microenvironment takes primary responsibility for the rapid expansion of breast cancer tumors. However, the underlying mechanism is not fully understood. Here, using RNA sequencing (RNA-seq) analysis, we identified a hypoxia-induced long noncoding RNA (lncRNA) KB-1980E6.3, which is aberrantly upregulated in clinical breast cancer tissues and closely correlated with poor prognosis of breast cancer patients. The enhanced lncRNA KB-1980E6.3 facilitates breast cancer stem cells (BCSCs) self-renewal and tumorigenesis under hypoxic microenvironment both in vitro and in vivo. Mechanistically, lncRNA KB-1980E6.3 recruited insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) to form a lncRNA KB-1980E6.3/IGF2BP1/c-Myc signaling axis that retained the stability of c-Myc mRNA through increasing binding of IGF2BP1 with m6A-modified c-Myc coding region instability determinant (CRD) mRNA. In conclusion, we confirm that lncRNA KB-1980E6.3 maintains the stemness of BCSCs through lncRNA KB-1980E6.3/IGF2BP1/c-Myc axis and suggest that disrupting this axis might provide a new therapeutic target for refractory hypoxic tumors.

Rapid advancement in cancer genomic big data in the pursuit of precision oncology

In the current big data era, massive genomic cancer data are available for open access from anywhere in the world. They are obtained from popular platforms, such as The Cancer Genome Atlas, which provides genetic information from clinical samples, and Cancer Cell Line Encyclopedia, which offers genomic data of cancer cell lines. For convenient analysis, user-friendly tools, such as the Tumor Immune Estimation Resource (TIMER), which can be used to analyze tumor-infiltrating immune cells comprehensively, are also emerging. In clinical practice, clinical sequencing has been recommended for patients with cancer in many countries. Despite its many challenges, it enables the application of precision medicine, especially in medical oncology. In this review, several efforts devoted to accomplishing precision oncology and applying big data for use in Indonesia are discussed. Utilizing open access genomic data in writing research articles is also described.

Differential Expression of ADP/ATP Carriers as a Biomarker of Metabolic Remodeling and Survival in Kidney Cancers

ADP/ATP carriers (AACs) are mitochondrial transport proteins playing a strategic role in maintaining the respiratory chain activity, fueling the cell with ATP, and also regulating mitochondrial apoptosis. To understand if AACs might represent a new molecular target for cancer treatment, we evaluated AAC expression levels in cancer/normal tissue pairs available on the Tissue Cancer Genome Atlas database (TCGA), observing that AACs are dysregulated in most of the available samples. It was observed that at least two AACs showed a significant differential expression in all the available kidney cancer/normal tissue pairs. Thus, we investigated AAC expression in the corresponding kidney non-cancer (HK2)/cancer (RCC-Shaw and CaKi-1) cell lines, grown in complete medium or serum starvation, for investigating how metabolic alteration induced by different growth conditions might influence AAC expression and resistance to mitochondrial apoptosis initiators, such as "staurosporine" or the AAC highly selective inhibitor "carboxyatractyloside". Our analyses showed that AAC2 and AAC3 transcripts are more expressed than AAC1 in all the investigated kidney cell lines grown in complete medium, whereas serum starvation causes an increase of at least two AAC transcripts in kidney cancer cell lines compared to non-cancer cells. However, the total AAC protein content is decreased in the investigated cancer cell lines, above all in the serum-free medium. The observed decrease in AAC protein content might be responsible for the decrease of OXPHOS activity and for the observed lowered sensitivity to mitochondrial apoptosis induced by staurosporine or carboxyatractyloside. Notably, the cumulative probability of the survival of kidney cancer patients seriously decreases with the decrease of AAC1 expression in KIRC and KIRP tissues making AAC1 a possible new biomarker of metabolic remodeling and survival in kidney cancers.

A meta-analysis of BRF2 as a prognostic biomarker in invasive breast carcinoma

BACKGROUND

Deregulation of the RNA polymerase III specific TFIIIB subunit BRF2 occurs in subtypes of human cancers. However, correlations between BRF2 alterations and clinical outcomes in breast cancer are limited. We conducted this review to analyze BRF2 alterations in genomic data sets housed in Oncomine and cBioPortal to identify potential correlations between BRF2 alterations and clinical outcomes.

METHODS

The authors queried both Oncomine and cBioPortal for alterations in BRF2 in human cancers and performed meta-analyses identifying significant correlations between BRF2 and clinical outcomes in invasive breast cancer (IBC).

RESULTS

A meta cancer outlier profile analysis (COPA) of 715 data sets (86,733 samples) in Oncomine identified BRF2 as overexpressed in 60% of breast cancer data sets. COPA scores in IBC data sets (3594 patients) are comparable for HER2 (24.211, median gene rank 60) and BRF2 (29.656, median gene rank 36.5). Overall survival in IBC patients with BRF2 alterations (21%) is significantly decreased (p = 9.332e-3). IBC patients with BRF2 alterations aged 46 to 50 have a significantly poor survival outcome (p = 7.093e-3). Strikingly, in metastatic breast cancer, BRF2 is altered in 33% of women aged 45-50. BRF2 deletions are predominant in this age group.

CONCLUSION

This study suggests BRF2 may be an prognostic biomarker in invasive breast carcinoma.

Bioinformatics analysis of aberrantly expressed exosomal lncRNAs in oral squamous cell carcinoma (CAL-27 vs. oral epithelial) cells

Oral squamous cell carcinoma (OSCC) is the most prevalent form of malignant tumour in the oral cavity and its early detection is critical for improving the prognosis of affected patients. The present study aimed to isolate and confirm exosomes derived from the supernatant of the OSCC cell line CAL-27 and human oral epithelial cells (HOECs), analyze long non-coding RNA (lncRNA) expression profiles and determine the diagnostic value based on bioinformatics analyses. The results indicated that the particles isolated from the supernatant of CAL-27 and HOECs were either round or oval, had a size range of 30–150 nm and were enriched with ALG-2 interacting protein X (ALIX) and tumour susceptibility 101 proteins (TSG101). These characteristics confirmed that these particles were exosomes. Three lncRNAs (NR-026892.1, NR-126435.1 and NR-036586.1) were selected as potential diagnostic biomarkers for OSCC. The expression levels of the selected lncRNAs were significantly different in CAL-27-exo vs. HOEC-exo, as well as in whole cells (CAL-27 vs. HOECs) (P<0.001). The expression levels of the three lncRNAs confirmed by quantitative PCR were consistent with the sequencing data. In conclusion, various lncRNAs were aberrantly expressed between cancerous and non-cancerous exosomes, suggesting that they may serve as biomarkers for cancer.

Expression levels of lncRNAs are prognostic for hepatocellular carcinoma overall survival

Studies have demonstrated that long non-coding RNAs (lncRNAs) play important roles in cancer development and progression. However, associations between the expression patterns and prognostic roles of lncRNAs in hepatocellular carcinoma (HCC) have not been comprehensively described. In this study, we established a prognostic model of lncRNA expression using public datasets of HCC from The Cancer Genome Atlas (TCGA) and adopted the International Cancer Genome Consortium (ICGC) as an independent cohort to validate the stability of our model. Cox regression analysis was used to explore the independent prognostic factor in both training and validation cohorts. Additionally, we explored the functional roles of lncRNAs using bioinformatic analyses. According to lncRNA consensus clusters, we resolved the distribution of molecular and clinical data and observed that individual lncRNA could function as prognostic biomarkers in HCC. Furthermore, the novel lncRNA molecular subtypes were statistically significant for predicting HCC status, which was validated by nested cross-validation. We found that lncRNA subtypes were partially related to gender, histological grade, and mutations within TP53. The lncRNA subtypes were also consistent with mRNA-based subtypes, and pathway enrichment analysis identified the involvement of multiple signaling pathways. In addition, we observed that upregulated DANCR was significantly associated with poor prognosis in HCC patients. In conclusion, our model based on lncRNA expression is statistically significant as a diagnostic and prognostic indicator for patients with HCC.

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive ‘atlas’ of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

Role of the ERO1-PDI interaction in oxidative protein folding and disease

Protein folding in the endoplasmic reticulum is an oxidative process that relies on protein disulfide isomerase (PDI) and endoplasmic reticulum oxidase 1 (ERO1). Over 30% of proteins require the chaperone PDI to promote disulfide bond formation. PDI oxidizes cysteines in nascent polypeptides to form disulfide bonds and can also reduce and isomerize disulfide bonds. ERO1 recycles reduced PDI family member PDIA1 using a FAD cofactor to transfer electrons to oxygen. ERO1 dysfunction critically affects several diseases states. Both ERO1 and PDIA1 are overexpressed in cancers and implicated in diabetes and neurodegenerative diseases. Cancer-associated ERO1 promotes cell migration and invasion. Furthermore, the ERO1-PDIA1 interaction is critical for epithelial-to-mesenchymal transition. Co-expression analysis of ERO1A gene expression in cancer patients demonstrated that ERO1A is significantly upregulated in lung adenocarcinoma (LUAD), glioblastoma and low-grade glioma (GBMLGG), pancreatic ductal adenocarcinoma (PAAD), and kidney renal papillary cell carcinoma (KIRP) cancers. ERO1Α knockdown gene signature correlates with knockdown of cancer signaling proteins including IGF1R, supporting the search for novel, selective ERO1 inhibitors for the treatment of cancer. In this review, we explore the functions of ERO1 and PDI to support inhibition of this interaction in cancer and other diseases.

Genome-wide methylation and expression profiling identify a novel epigenetic signature in gastrointestinal pan-adenocarcinomas

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.

Deciphering Immune-Associated Genes to Predict Survival in Clear Cell Renal Cell Cancer

Background

To elucidate the correlations between tumor microenvironment and clinical characteristics as well as prognosis in clear cell renal cell cancer (ccRCC) and investigate the immune-associated genes by a comprehensive analysis of The Cancer Genome Atlas (TCGA) database.

Methods

We collected mRNA expression profiles of 537 ccRCC samples from the TCGA database. Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. The differentially expressed genes (DEGs) were screened between high immune/stromal score and low immune/stromal score groups by the cutoff of |log (fold change)| > 1, P value <0.05 by using package "limma" in R. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction network of intersected DEGs between stromal score and immune score groups was conducted using the STRING database. The Kaplan-Meier method was used to explore DEGs with predictive values in overall survival, and the prognostic DEGs were further validated in a Gene Expression Omnibus (GEO) dataset GSE29609.

Results

A higher immune score was associated with T3/4 (vs. T1/2, P < 0.001), N1 (vs. N0, P=0.05), M1 (vs. M0, P=0.004), G3/4 (vs. G1/2, P < 0.001), advanced AJCC stage (P < 0.001), and shorter overall survival (P=0.04). Intersected DEGs between immune and stromal score groups were 48 upregulated and 47 downregulated genes, with 43 DEGs associated with overall survival in ccRCC. After validation by a cohort of 39 ccRCC cases with detailed follow-up information from GSE29609, six immune-associated DEGs including CASP5, HSD11B1, VSIG4, HMGCS2, HSD11B2, and OGDHL were demonstrated to be predictive of prognosis in ccRCC.

Conclusions

Our study elucidated tight associations between immune score and clinical characteristics as well as prognosis in ccRCC. Moreover, six DEGs were explored and validated to exert predictive values in overall survival of ccRCC.

TIMP1 Is A Potential Key Gene Associated With The Pathogenesis And Prognosis Of Ulcerative Colitis-Associated Colorectal Cancer

Purpose

Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide. As a high-risk factor for CRC, ulcerative colitis (UC) has been demonstrated to lead to epithelial dysplasia, DNA damage, and eventually cancer. There are approximately 18% of patients with UC may develop CRC.

Patients and methods

The gene expression profiles were retrieved from the Gene Expression Omnibus. The Database for Annotation, Visualization and Integrated Discovery was employed to conduct gene annotations. Protein-protein interaction network was constructed by the Search Tool for the Retrieval of Interacting Genes, and further analysed by the Molecular Complex Detection. The correlation between TIMP1 and prognosis was evaluated by the Gene Expression Profiling Interactive Analysis. To predict the potential functions of TIMP1, the GeneMANIA, Coremine, and FunRich were employed. After transfection with small interfering RNA targeting TIMP1, cell proliferation, migration, and apoptosis were determined by CCK-8, scratch wound, and Annexin V-FITC/PI assays, respectively.

Results

TIMP1, consistently overexpressed in the initiation and progression of UC-associated CRC (ucaCRC), was identified to be a potential biomarker for the prognosis of patients with CRC. Experimental results showed knockdown of TIMP1 could increase the migration, while did not affect the proliferation and apoptosis of RKO cells. The role of TIMP1 in the malignant transformation of ucaCRC was confirmed by using the protein/gene interactions and biological process annotation and validated by analysing the transcription factors targeting TIMP1.

Conclusion

TIMP1 is consistently upregulated in the pathological process of ucaCRC and can be a potential biomarker for the worse prognosis of CRC.

Heterogeneity of breast cancer: The importance of interaction between different tumor cell populations

INTRODUCTION

Breast cancer is the most common cancer and the second leading cause of cancer-related death in women worldwide. Despite the early detection of breast cancer and increasing knowledge of its biology and chemo-resistance, metastatic breast cancer is largely incurable disease. We provide a review of the intertumor and intratumor heterogeneity, explain the differences between triple-negative breast cancer subtypes. Also, we describe the interaction of breast tumor cells with their microenvironment cells and explain how this interaction contributes to the tumor progression, metastasis formation and resistance to the treatment.

DISCUSSION

One of the main causes that complicate the treatment is tumor heterogeneity. It is observed among patients (intertumor heterogeneity) and in each individual tumor (intratumor heterogeneity). In the case of intratumor heterogeneity, the tumor consists of different phenotypical cell populations. During breast cancer subtype identification, a small piece of solid tumor tissue does not necessarily represent all the tumor composition. Breast tumor cell phenotypical differences may appear due to cell localization in different tumor sites, unique response to the treatment, cell interaction with tumor microenvironment or tumor cell interaction with each other. This heterogeneity may lead to breast cancer aggressiveness and challenging treatment.

CONCLUSION

Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Identification of differences between populations and their response to anticancer drugs would help to predict the potential resistance to chemotherapy and thus would help to select the most suitable anticancer treatment.

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups

BACKGROUND

Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features.

METHODS

In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database.

RESULTS

Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma.

CONCLUSIONS

This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.

High expression of meningioma 1 is correlated with reduced survival rates in colorectal cancer patients

The identification of prognostic markers for colorectal cancer (CRC) has important clinical implications. However, the association between meningioma 1 (MN1) expression and clinical outcomes of CRC has not been fully investigated. The aim of this study was to investigate the expression of MN1 in the clinical context of CRC. We first used immunohistochemistry (IHC) staining to examine and compare MN1 expression between multiple human cancer tissues and normal tissues. Initial screening revealed that the expression of MN1 proteins was significantly higher in tumor tissues of the breast, colon, and liver than in normal tissues. In further testing conducted on 59 paired CRC samples, we observed that the expression of MN1 in CRC tissue samples was significantly higher than in adjacent normal tissues. Moreover, high MN1 expression was not significantly associated with clinicopathological characteristics. Kaplan-Meier survival analysis revealed that high expression of MN1 mRNA or MN1 protein was significantly associated with poor CRC prognosis. Furthermore, univariate Cox analysis revealed that a high MN1 score was significantly associated with prognostic factors. Multivariate Cox analysis further indicated that gender, histologic grade, tumor-node-metastasis (TNM) stage, and a high MN1 score were independent factors of overall CRC survival rates. Finally, MN1 and PCNA protein levels were positively correlated, which suggests that MN1 may be involved in the cell proliferation process during CRC formation. Our results, which confirm those of other studies, indicate that (1) high levels of MN1 expression contribute to poor CRC prognosis and (2) MN1 can serve as a novel potential biomarker in predicting the prognosis of CRC patients.

Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

BACKGROUND

The mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.

RESULTS

In this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges.

CONCLUSIONS

The identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.

GTSE1 is involved in breast cancer progression in p53 mutation-dependent manner

Background

With the rapid development of the high throughput detection techniques, tumor-related Omics data has become an important source for studying the mechanism of tumor progression including breast cancer, one of the major malignancies worldwide. A previous study has shown that the G2 and S phase-expressed-1 (GTSE1) can act as an oncogene in several human cancers. However, its functional roles in breast cancer remain elusive.

Method

In this study, we analyzed breast cancer data downloaded from The Cancer Genome Atlas (TCGA) databases and other online database including the Oncomine, bc-GenExMiner and PROGgeneV2 database to identify the molecules contributing to the progression of breast cancer. The GTSE1 expression levels were investigated using qRT-PCR, immunoblotting and IHC. The biological function of GTSE1 in the growth, migration and invasion of breast cancer was examined in MDA-MB-231, MDA-MB-468 and MCF7 cell lines. The in vitro cell proliferative, migratory and invasive abilities were evaluated by MTS, colony formation and transwell assay, respectively. The role of GTSE1 in the growth and metastasis of breast cancer were revealed by in vivo investigation using BALB/c nude mice.

Results

We showed that the expression level of GTSE1 was upregulated in breast cancer specimens and cell lines, especially in triple negative breast cancer (TNBC) and p53 mutated breast cancer cell lines. Importantly, high GTSE1 expression was positively correlated with histological grade and poor survival. We demonstrated that GTSE1 could promote breast cancer cell growth by activating the AKT pathway and enhance metastasis by regulating the Epithelial-Mesenchymal transition (EMT) pathway. Furthermore, it could cause multidrug resistance in breast cancer cells. Interestingly, we found that GTSE1 could regulate the p53 function to alter the cell cycle distribution dependent on the mutation state of p53.

Conclusion

Our results reveal that GTSE1 played a key role in the progression of breast cancer, indicating that GTSE1 could serve as a novel biomarker to aid in the assessment of the prognosis of breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1157-4) contains supplementary material, which is available to authorized users.

OncomiR: an online resource for exploring pan-cancer microRNA dysregulation

Summary

Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer. Using combined miRNA-seq, RNA-seq and clinical data from The Cancer Genome Atlas, we systematically performed statistical analyses to identify dysregulated miRNAs that are associated with tumor development and progression in most major cancer types. Additional analyses further identified potential miRNA-gene target interactions in tumors. These results are stored in a backend database and presented through a web server interface. Moreover, through a backend bioinformatics pipeline, OncomiR can also perform dynamic analysis with custom miRNA selections for in-depth characterization of miRNAs in cancer.

Availability and implementation

The OncomiR website is freely accessible at www.oncomir.org.

Contact

xiaowei.wang@wustl.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Back to the Colorectal Cancer Consensus Molecular Subtype Future

PURPOSE OF REVIEW

This review seeks to provide an informed prospective on the advances in molecular profiling and analysis of colorectal cancer (CRC). The goal is to provide a historical context and current summary on how advances in gene and protein sequencing technology along with computer capabilities led to our current bioinformatic advances in the field.

RECENT FINDINGS

An explosion of knowledge has occurred regarding genetic, epigenetic, and biochemical alterations associated with the evolution of colorectal cancer. This has led to the realization that CRC is a heterogeneous disease with molecular alterations often dictating natural history, response to treatment, and outcome. The consensus molecular subtypes (CMS) classification classifies CRC into four molecular subtypes with distinct biological characteristics, which may form the basis for clinical stratification and subtype-based targeted intervention. This review summarizes new developments of a field moving "Back to the Future." CRC molecular subtyping will better identify key subtype specific therapeutic targets and responses to therapy.

Datalog Extensions for Bioinformatic Data Analysis

Recent growth in public bioinformatic databases has facilitated the analysis of genomic and proteomic data. However, the large size of the datasets makes it hard for nonexpert programmers to perform the analysis. In this paper we present B-Log, a high-level query language for bioinformatic data analysis. Based on Datalog, B-Log can simply express graph analysis algorithms; it is extended with nested tables, recursive aggregations, and foreign functions, which helps quick exploratory analyses. We implemented several analysis algorithms in B-Log; we also implemented a prototype system to explore TCGA dataset. We find B-Log to be useful for exploratory analysis and quick prototyping.

Radiomics: the facts and the challenges of image analysis

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

Radiomics: the facts and the challenges of image analysis

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.

Revisiting the role of dihydroorotate dehydrogenase as a therapeutic target for cancer

Identified as a hallmark of cancer, metabolic reprogramming allows cancer cells to rapidly proliferate, resist chemotherapies, invade, metastasize, and survive a nutrient-deprived microenvironment. Rapidly growing cells depend on sufficient concentrations of nucleotides to sustain proliferation. One enzyme essential for the de novo biosynthesis of pyrimidine-based nucleotides is dihydroorotate dehydrogenase (DHODH), a known therapeutic target for multiple diseases. Brequinar, leflunomide, and teriflunomide, all of which are potent DHODH inhibitors, have been clinically evaluated but failed to receive FDA approval for the treatment of cancer. Inhibition of DHODH depletes intracellular pyrimidine nucleotide pools and results in cell cycle arrest in S-phase, sensitization to current chemotherapies, and differentiation in neural crest cells and acute myeloid leukemia (AML). Furthermore, DHODH is a synthetic lethal susceptibility in several oncogenic backgrounds. Therefore, DHODH-targeted therapy has potential value as part of a combination therapy for the treatment of cancer. In this review, we focus on the de novo pyrimidine biosynthesis pathway as a target for cancer therapy, and in particular, DHODH. In the first part, we provide a comprehensive overview of this pathway and its regulation in cancer. We further describe the relevance of DHODH as a target for cancer therapy using bioinformatic analyses. We then explore the preclinical and clinical results of pharmacological strategies to target the de novo pyrimidine biosynthesis pathway, with an emphasis on DHODH. Finally, we discuss potential strategies to harness DHODH as a target for the treatment of cancer.

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80-85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC. Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein-protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset. Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis

OBJECTIVE

To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases.

METHODS

Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis.

RESULT

225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K-Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs' protein-protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes.

CONCLUSION

BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.