Low expression of lncRNA APTR promotes gastric cancer progression

BackgroundGastric cancer (GC) is one of the most common cancers worldwide and the majority of GC patients are diagnosed at advanced stages due to the lack of early detection biomarkers. LncRNAs have been shown to play important roles in various diseases and could be predictive biomarkers and therapeutic targets. Our study demonstrated that low expression of lncRNA APTR could promote gastric cancer progression.MethodsDifferentiated expressed lncRNAs were identified through analyzing TCGA paired GC RNA sequencing data. LncRNA APTR's clinical relevance was analyzed using the TCGA dataset and GEO datasets. APTR expression in patient samples was detected through qPCR. The proliferation, colony formation, and migration of GC cells were tested. Bioinformatic analyses were performed to explore APTR-affected signaling pathways in GC.ResultsLncRNA APTR is lower expressed in gastric tumor samples and low expression of APTR predicts a poor diagnosis and outcome in GC patients. Silencing APTR promotes gastric cancer proliferation and invasiveness. APTR expression is negatively correlated with inflammatory signaling in the gastric tumor microenvironment.ConclusionOur study showed that low expression of lncRNA APTR in gastric cancer is correlated with tumorigenesis and poor diagnosis and prognosis, which is a potential biomarker for gastric cancer patients' diagnosis and treatment.     

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data

Background

Large-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and protein expression, offer tantalizing possibilities for realizing the promise and potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including heterogeneity, and high-dimensionality of omics data.

Methods

We propose a novel framework for multi-omics data integration using multi-view feature selection. We introduce a novel multi-view feature selection algorithm, MRMR-mv, an adaptation of the well-known Min-Redundancy and Maximum-Relevance (MRMR) single-view feature selection algorithm to the multi-view setting.

Results

We report results of experiments using an ovarian cancer multi-omics dataset derived from the TCGA database on the task of predicting ovarian cancer survival. Our results suggest that multi-view models outperform both view-specific models (i.e., models trained and tested using a single type of omics data) and models based on two baseline data fusion methods.

Conclusions

Our results demonstrate the potential of multi-view feature selection in integrative analyses and predictive modeling from multi-omics data.

     

A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side

Background

Single Nucleotide Polymorphism (SNP) in the genome has become crucial information for clinical use. For example, the targeted cancer therapy is primarily based on the information which clinically important SNPs are detectable from the tumor. Many hospitals have developed their own panels that include clinically important SNPs. The genome information exchange between the patient and the hospital has become more popular. However, the genome sequence information is innate and irreversible and thus its leakage has serious consequences. Therefore, protecting one’s genome information is critical. On the other side, hospitals may need to protect their own panels. There is no known secure SNP panel scheme to protect both.

Results

In this paper, we propose a secure SNP panel scheme using homomorphically encrypted K-mers without requiring SNP calling on the user side and without revealing the panel information to the user. Use of the powerful homomorphic encryption technique is desirable, but there is no known algorithm to efficiently align two homomorphically encrypted sequences. Thus, we designed and implemented a novel secure SNP panel scheme utilizing the computationally feasible equality test on two homomorphically encrypted K-mers. To make the scheme work correctly, in addition to SNPs in the panel, sequence variations at the population level should be addressed. We designed a concept of Point Deviation Tolerance (PDT) level to address the false positives and false negatives. Using the TCGA BRCA dataset, we demonstrated that our scheme works at the level of over a hundred thousand somatic mutations. In addition, we provide a computational guideline for the panel design, including the size of K-mer and the number of SNPs.

Conclusions

The proposed method is the first of its kind to protect both the user’s sequence and the hospital’s panel information using the powerful homomorphic encryption scheme. We demonstrated that the scheme works with a simulated dataset and the TCGA BRCA dataset. In this study, we have shown only the feasibility of the proposed scheme and much more efforts should be done to make the scheme usable for clinical use.

     

TFCP2L1在子宫内膜癌细胞增殖及侵袭中的作用

摘要 目的：研究人子宫内膜癌中TFCP2L1的表达情况以及分析TFCP2L1对子宫内膜癌细胞增殖及迁移能力的影响。方法：（1）通过TCGA 及GTEx数据库分析子宫内膜癌中TFCP2L1的表达水平及患者生存期。采用Western blot验证正常子宫内膜上皮细胞与多种子宫内膜癌细胞系中TFCP2L1的表达情况。（2）使用CRISPR-Cas9技术敲除Ishikawa细胞系的TFCP2L1,并用流式分选技术筛选单个细胞进行培养,形成单克隆细胞系,以此来研究TFCP2L1对子宫内膜癌的细胞周期和细胞增殖的影响。通过 Western blot 及细胞免疫荧光检测细胞周期相关蛋白的表达,检测细胞增殖情况,采用平板克隆实验及CCK8实验。（3）通过Transwell小室及划痕实验对侵袭和转移能力进行检测。结果：TCGA 及GTEx数据库分析发现TFCP2L1在子宫内膜癌中高表达且与肿瘤患者预后不良相关。敲除TFCP2L1后,Ki67、Cyclin D1 和 Cyclin D2的蛋白水平显著下调,CCK8及平板克隆实验结果表明,敲除TFCP2L1能够显著降低子宫内膜癌细胞的增殖能力。划痕实验及Transwell侵袭实验结果表明敲除TFCP2L1的子宫内膜癌细胞侵袭迁移能力均减弱。结论：本研究证明了TFCP21L1是子宫内膜癌的促癌因子。TFCP2L1的高表达可能与子宫内膜癌预后不良相关。敲除TFCP2L1可以抑制子宫内膜癌的侵袭和转移。     

KANK1对子宫内膜癌细胞增殖及迁移的影响

摘要 目的：研究KANK1在子宫内膜癌中的表达以及对子宫内膜癌细胞增殖及迁移的影响。方法：（1）TCGA数据库分析KANK1在子宫内膜癌中的表达和生存期分析。（2）采用实时荧光定量聚合酶链反应验证转染KANK1质粒的效果。采用Ishikawa和ECC1这两种子宫内膜癌细胞来探讨KANK1对子宫内膜癌的细胞周期和凋亡的影响。通过Western blot检测细胞周期相关蛋白的表达,以及流式细胞术检测细胞周期和凋亡水平。（3）通过Transwell小室实验和划痕实验检测细胞的侵袭和转移能力。结果：TCGA数据库分析发现KANK1在子宫内膜癌中低表达且与患者预后良好相关。过表达KANK1下调了Cyclin D1和Cyclin D2的蛋白水平,并将细胞周期阻滞在G1期。流式细胞术检测发现过表达KANK1组的细胞凋亡水平（Ishikawa：22.7%;ECC1：19.0%）比对照组（Ishikawa：18.1%;ECC1：15.3%）高,差异具有统计学意义。Transwell迁移和侵袭实验结果表明过表达KANK1组的子宫内膜癌细胞侵袭和转移能力减弱。结论：本研究证明了KANK1在子宫内膜癌中发挥抑癌作用。KANK1高表达与子宫内膜癌的预后良好成正相关。KANK1通过抑制癌细胞周期和促进肿瘤细胞凋亡发挥抑制子宫内膜癌增殖的作用。此外,KANK1抑制了子宫内膜癌的侵袭和转移。     

A 2015 survey of established or potential epigenetic biomarkers for the accurate detection of human cancers

Context The silencing or activation of cancer-associated genes by epigenetic mechanisms can ultimately lead to the clonal expansion of cancer cells. Objective The aim of this review is to summarize all relevant epigenetic biomarkers that have been proposed to date for the diagnosis of some prevalent human cancers. Methods A Medline search for the terms epigenetic biomarkers, human cancers, DNA methylation, histone modifications and microRNAs was performed. Results One hundred fifty-seven relevant publications were found and reviewed. Conclusion To date, a significant number of potential epigenetic cancer biomarkers of human cancer have been investigated, and some have advanced to clinical implementation.     

RADIA: RNA and DNA Integrated Analysis for Somatic Mutation Detection

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual’s DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.     

Mutation Scanning Using MUT-MAP,a High-Throughput,Microfluidic Chip-Based,Multi-Analyte Panel

Targeted anticancer therapies rely on the identification of patient subgroups most likely to respond to treatment. Predictive biomarkers play a key role in patient selection, while diagnostic and prognostic biomarkers expand our understanding of tumor biology, suggest treatment combinations, and facilitate discovery of novel drug targets. We have developed a high-throughput microfluidics method for mutation detection (MUT-MAP, mutation multi-analyte panel) based on TaqMan or allele-specific PCR (AS-PCR) assays. We analyzed a set of 71 mutations across six genes of therapeutic interest. The six-gene mutation panel was designed to detect the most common mutations in the EGFR, KRAS, PIK3CA, NRAS, BRAF, and AKT1 oncogenes. The DNA was preamplified using custom-designed primer sets before the TaqMan/AS-PCR assays were carried out using the Biomark microfluidics system (Fluidigm; South San Francisco, CA). A cross-reactivity analysis enabled the generation of a robust automated mutation-calling algorithm which was then validated in a series of 51 cell lines and 33 FFPE clinical samples. All detected mutations were confirmed by other means. Sample input titrations confirmed the assay sensitivity with as little as 2 ng gDNA, and demonstrated excellent inter- and intra-chip reproducibility. Parallel analysis of 92 clinical trial samples was carried out using 2–100 ng genomic DNA (gDNA), allowing the simultaneous detection of multiple mutations. DNA prepared from both fresh frozen and formalin-fixed, paraffin-embedded (FFPE) samples were used, and the analysis was routinely completed in 2–3 days: traditional assays require 0.5–1 µg high-quality DNA, and take significantly longer to analyze. This assay can detect a wide range of mutations in therapeutically relevant genes from very small amounts of sample DNA. As such, the mutation assay developed is a valuable tool for high-throughput biomarker discovery and validation in personalized medicine and cancer drug development.     

基于在线数据库和真实世界样本探讨肺腺癌患者POLE/POLD1突变的临床意义

摘要 目的：POLE和POLD1突变导致DNA聚合酶校对功能丧失可能会影响基因组稳定性并导致突变增加和肿瘤形成。本文结合在线数据库和真实世界样本进一步分析肺腺癌（LUAD）患者POLE和/或 POLD1 突变的临床意义。方法：纳入2021年1月～2021年8月徐州医科大学附属医院肺癌术后组织标本115例,利用二代测序技术（NGS）检测基因突变;从癌症基因组图谱（TCGA）数据库收集肺腺癌数据集,通过Cbioportal在线数据库获得肿瘤突变分布图,通过Cibersort法计算获得样本的免疫相关细胞浸润情况。结果：真实世界样本中POLE/ POLD1突变的比例为7.83%（9/115）。TCGA数据显示POLE/POLD1突变的LUAD患者总生存期（OS）减少（P=0.0359）。然而,携带该突变的患者并发其他基因改变的频率明显增加,尤其是与TP53突变存在正相关;同时,POLE/POLD1突变与LUAD组织浸润性免疫杀伤细胞呈正相关,与免疫抑制细胞呈负相关,提示这部分患者对免疫检查点抑制剂（ICI）敏感。结论：LUAD患者POLE/POLD1突变预示较高的肿瘤突变负荷和免疫微环境改变,可作为ICI疗效预测的潜在生物标志物,值得临床关注。     

Minimum error calibration and normalization for genomic copy number analysis

BackgroundCopy number variations (CNV) are regional deviations from the normal autosomal bi-allelic DNA content. While germline CNVs are a major contributor to genomic syndromes and inherited diseases, the majority of cancers accumulate extensive “somatic” CNV (sCNV or CNA) during the process of oncogenetic transformation and progression. While specific sCNV have closely been associated with tumorigenesis, intriguingly many neoplasias exhibit recurrent sCNV patterns beyond the involvement of a few cancer driver genes. Currently, CNV profiles of tumor samples are generated using genomic micro-arrays or high-throughput DNA sequencing. Regardless of the underlying technology, genomic copy number data is derived from the relative assessment and integration of multiple signals, with the data generation process being prone to contamination from several sources. Estimated copy number values have no absolute or strictly linear correlation to their corresponding DNA levels, and the extent of deviation differs between sample profiles, which poses a great challenge for data integration and comparison in large scale genome analysis.ResultsIn this study, we present a novel method named “Minimum Error Calibration and Normalization for Copy Numbers Analysis” (Mecan4CNA). It only requires CNV segmentation files as input, is platform independent, and has a high performance with limited hardware requirements. For a given multi-sample copy number dataset, Mecan4CNA can batch-normalize all samples to the corresponding true copy number levels of the main tumor clones. Experiments of Mecan4CNA on simulated data showed an overall accuracy of 93% and 91% in determining the normal level and single copy alteration (i.e. duplication or loss of one allele), respectively. Comparison of estimated normal levels and single copy alternations with existing methods and karyotyping data on the NCI-60 tumor cell line produced coherent results. To estimate the method's impact on downstream analyses, we performed GISTIC analyses on the original and Mecan4CNA normalized data from the Cancer Genome Atlas (TCGA) where the normalized data showed prominent improvements of both sensitivity and specificity in detecting focal regions.ConclusionsMecan4CNA provides an advanced method for CNA data normalization, especially in meta-analyses involving large profile numbers and heterogeneous source data quality. With its informative output and visualization options, Mecan4CNA also can improve the interpretation of individual CNA profiles. Mecan4CNA is freely available as a Python package and through its code repository on Github.     

Annexin A1 Preferentially Predicts Poor Prognosis of Basal-Like Breast Cancer Patients by Activating mTOR-S6 Signaling

IntroductionAnnexin A1 (ANXA1) is an anti-inflammatory protein reported to play a role in cell proliferation and apoptosis, and to be deregulated in breast cancer. The exact role of annexin A1 in the biology of breast cancer remains unclear. We hypothesized that the annexin A1 plays an oncogenic role in basal subtype of breast cancer by modulating key growth pathway(s).MethodsBy mining the Cancer Genome Atlas (TCGA)-Breast Cancer dataset and manipulating annexin A1 levels in breast cancer cell lines, we studied the role of annexin A1 in breast cancer and underlying signaling pathways.ResultsOur in-silico analysis of TCGA-breast cancer dataset demonstrated that annexin A1 mRNA expression is higher in basal subtype compared to luminal and HER2 subtypes. Within the basal subtype, patients show significantly poorer overall survival associated with higher expression of annexin A1. In both TCGA patient samples and cell lines, annexin A1 levels were significantly higher in basal-like breast cancer than luminal and Her2/neu-positive breast cancer. Stable annexin A1 knockdown in TNBC cell lines suppressed the mTOR-S6 pathway likely through activation of AMPK but had no impact on the MAPK, c-Met, and EGFR pathways. In a cell migration assay, annexin A1-depleted TNBC cells showed delayed migration as compared to wild-type cells, which could be responsible for poor patient prognosis in basal like breast cancers that are known to express higher annexin A1.ConclusionsOur data suggest that annexin A1 is prognostic only in patients with basal like breast cancer. This appears to be in part due to the role of annexin A1 in activating mTOR-pS6 pathway.     

An exosome-related lncRNA signature correlates with prognosis,immune microenvironment,and therapeutic responses in hepatocellular carcinoma

BackgroundExosomes act as essential modulators of cancer development and progression in hepatocellular carcinoma. However, little is known about the potential prognostic value and underlying molecular features of exosome-related long non-coding RNAs.MethodsGenes associated with exosome biogenesis, exosome secretion, and exosome biomarkers were collected. Exosome-related lncRNA modules were identified using PCA and WGCNA analysis. A prognostic model based on data from the TCGA, GEO, NODE, and ArrayExpress was developed and validated. A comprehensive analysis of the genomic landscape, functional annotation, immune profile, and therapeutic responses underlying the prognostic signature was performed on multi-omics data, and bioinformatics methods were also applied to predict potential drugs for patients with high risk scores. qRT-PCR was used to validate the differentially expressed lncRNAs in normal and cancer cell lines.ResultsTwenty-six hub lncRNAs were identified as highly correlated with exosomes and overall survival and were used for prognosis modeling. Three cohorts consistently showed higher scores in the high-risk group, with an AUC greater than 0.7 over time. These higher scores implied poorer overall survival, higher genomic instability, higher tumor purity, higher tumor stemness, pro-tumor pathway activation, lower anti-tumor immune cell and tertiary lymphoid structure infiltration, and poor responses to immune checkpoint blockade therapy and transarterial chemoembolization therapy.ConclusionThrough developing an exosome-related lncRNA predictor for HCC patients, we revealed the clinical relevance of exosome-related lncRNAs and their potential as prognostic biomarkers and therapeutic response predictors.     

Cancer biomarker discovery and translation: proteomics and beyond

Introduction: Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond.

Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies.

Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.     

A “hit-and-run” affair – A possible link for cancer progression in virally driven cancers

BackgroundIt is well-known that certain cancers are caused by viruses. However, viral oncogenesis is complex and only a small fraction of the infected people develop cancer. Indeed, a number of environmental factors can contribute to virally infected cells developing cancer hallmarks, promoting tumorigenesis.Scope of reviewThe hit-and-run theory proposes that viruses facilitate the accumulation of mutations and promote genomic instability until the virus becomes dispensable for tumour maintenance. Indeed, several studies have reported viral genome, episome and/or oncogene loss in tumour cells without losing malignant phenotype.Major conclusionsThe current evidence supports the clear contribution of certain viruses to develop cancers. Importantly, the evidence supporting the sustained maintenance of malignancy after the loss of viral “presence” is sufficient to support the hit-and-run hypothesis of viral cancer development. Long-term tracking of vaccination outcome over the decades will test this theory.General significanceIf the hit-and-run theory is true, viruses might cause more cancers than previously thought and will have implications in the prevention of many cancers through implementing vaccination programs.     

Probing altered enzyme activity in the biochemical characterization of cancer

Enzymes have evolved to catalyze their precise reactions at the necessary rates, locations, and time to facilitate our development, to respond to a variety of insults and challenges, and to maintain a healthy, balanced state. Enzymes achieve this extraordinary feat through their unique kinetic parameters, myriad regulatory strategies, and their sensitivity to their surroundings, including substrate concentration and pH. The Cancer Genome Atlas (TCGA) highlights the extraordinary number of ways in which the finely tuned activities of enzymes can be disrupted, contributing to cancer development and progression often due to somatic and/or inherited genetic alterations. Rather than being limited to the domain of enzymologists, kinetic constants such as k_cat, K_m, and k_cat/K_m are highly informative parameters that can impact a cancer patient in tangible ways—these parameters can be used to sort tumor driver mutations from passenger mutations, to establish the pathways that cancer cells rely on to drive patients’ tumors, to evaluate the selectivity and efficacy of anti-cancer drugs, to identify mechanisms of resistance to treatment, and more. In this review, we will discuss how changes in enzyme activity, primarily through somatic mutation, can lead to altered kinetic parameters, new activities, or changes in conformation and oligomerization. We will also address how changes in the tumor microenvironment can affect enzymatic activity, and briefly describe how enzymology, when combined with additional powerful tools, and can provide us with tremendous insight into the chemical and molecular mechanisms of cancer.     

Allele-specific disparity in breast cancer

Background

Molecular alterations critical to development of cancer include mutations, copy number alterations (amplifications and deletions) as well as genomic rearrangements resulting in gene fusions. Massively parallel next generation sequencing, which enables the discovery of such changes, uses considerable quantities of genomic DNA (> 5 ug), a serious limitation in ever smaller clinical samples. However, a commonly available microarray platforms such as array comparative genomic hybridization (array CGH) allows the characterization of gene copy number at a single gene resolution using much smaller amounts of genomic DNA. In this study we evaluate the sensitivity of ultra-dense array CGH platforms developed by Agilent, especially that of the 1 million probe array (1 M array), and their application when whole genome amplification is required because of limited sample quantities.

Methods

We performed array CGH on whole genome amplified and not amplified genomic DNA from MCF-7 breast cancer cells, using 244 K and 1 M Agilent arrays. The ADM-2 algorithm was used to identify micro-copy number alterations that measured less than 1 Mb in genomic length.

Results

DNA from MCF-7 breast cancer cells was analyzed for micro-copy number alterations, defined as measuring less than 1 Mb in genomic length. The 4-fold extra resolution of the 1 M array platform relative to the less dense 244 K array platform, led to the improved detection of copy number variations (CNVs) and micro-CNAs. The identification of intra-genic breakpoints in areas of DNA copy number gain signaled the possible presence of gene fusion events. However, the ultra-dense platforms, especially the densest 1 M array, detect artifacts inherent to whole genome amplification and should be used only with non-amplified DNA samples.

Conclusions

This is a first report using 1 M array CGH for the discovery of cancer genes and biomarkers. We show the remarkable capacity of this technology to discover CNVs, micro-copy number alterations and even gene fusions. However, these platforms require excellent genomic DNA quality and do not tolerate relatively small imperfections related to the whole genome amplification.