CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer

PHF5A promotes colorectal cancerprogression by alternative splicing of TEAD2

Dysregulated alternative splicing (AS) plays critical roles in driving cancer progression, and the underlying mechanisms remain largely unknown. Here, we demonstrated that PHF5A, a component of U2 small nuclear ribonucleoproteins, was frequently upregulated in colorectal cancer (CRC) samples and associated with poor prognosis. PHF5A promoted proliferation and metastasis of CRC cells in vitro and in vivo. Transcriptomic analysis identified PHF5A-regulated AS targets and pathways. Particularly, PHF5A induced TEAD2 exon 2 inclusion to activate YAP signaling, and interference of TEAD2-L partially reversed the PHF5A-mediated tumor progression. Pharmacological inhibition of PHF5A using pladienolide B had potent antitumor activity. Collectively, these data revealed the oncogenic role of PHF5A in CRC through regulating AS and established PHF5A as potential therapeutic target.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

A nine-gene signature to improve prognosis prediction of colon carcinoma

This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially expressed genes (DEGs) were obtained through "edgeR" analysis. Finally, a risk predication model was established through a series of regression analyses, and then prognostic performance of the model was comprehensively evaluated though Kaplan-Meier and receiver operating characteristic (ROC) analysis. Gene set enrichment analysis (GSEA) was further performed. Totally, 846 DEGs were obtained by analyzing the gene expression data in GSE39582 dataset. A 9-gene signature-based risk predication model was established via regression analyses, and the model-based risk score was formulated as: Riskscore = (-0.1214) * TNFRSF11A + (-0.2617) * TMEM97 + (-0.1041) * LGR5 + 0.0973 * KLK10 + 0.1655 * HOXB8 + 0.227 * FKBP10 + (-0.1312) * CXCL13 + (-0.1316) * CXCL10 + 0.2593 * CD36. Kaplan-Meier curve showed that colon carcinoma patients in the high-risk group had a lower survival rate. GSEA showed that high-risk group and low-risk group displayed significant difference in biological pathways including ECM RECEPTOR INTERACTION. Besides, correlation analysis between the riskscore of the model and clinical features of patients revealed that the model could effectively predict the prognosis of patients in different ages (age>65, age<65) and stages (tumor_stage I/II, tumor_stage III/IV, T3&T4, N0&N1, N2&N3, M0). This study provides a robust model for the prognosis prediction of colon carcinoma, and lays a basis for researching the molecular mechanism underlying the development of colon carcinoma.

A novel signature predicts recurrence risk and therapeutic response in breast cancer patients

Acetylserotonin O-methyltransferase (ASMT) is a key enzyme in the synthesis of melatonin. Although melatonin has been shown to exhibit anticancer activity and prevents endocrine resistance in breast cancer, the role of ASMT in breast cancer progression remains unclear. In this retrospective study, we analyzed gene expression profiles in 27 data sets on 7244 patients from 11 countries. We found that ASMT expression was significantly reduced in breast cancer tumors relative to healthy tissue. Among breast cancer patients, those with higher levels of ASMT expression had better relapse-free survival outcomes and longer metastasis-free survival times. Following treatment with tamoxifen, patients with greater ASMT expression experienced longer periods before relapse or distance recurrence. Motivated by these results, we devised an ASMT gene signature that can correctly identify low-risk cases with a sensitivity and specificity of 0.997 and 0.916, respectively. This signature was robustly validated using 23 independent breast cancer mRNA array data sets from different platforms (consisting of 5800 patients) and an RNAseq data set from TCGA (comprising 1096 patients). Intriguingly, patients who are classified as high-risk by the signature benefit from adjuvant chemotherapy, and those with grade II tumors who are classified as low-risk exhibit improved overall survival and distance relapse-free outcomes following endocrine therapy. Together, our findings more clearly elucidate the roles of ASMT, provide strategies for improving the efficacy of tamoxifen treatment and help to identify those patients who may maximally benefit from adjuvant or endocrine therapies.

Identification of Prognosis-Related Genes in Bladder Cancer Microenvironment across TCGA Database

Background

Bladder cancer (BCa) is a common urothelial malignancy. The Cancer Genome Atlas (TCGA) database allows for an opportunity to analyze the relationship between gene expression and clinical outcomes in bladder cancer patients. This study is aimed at identifying prognosis-related genes in the bladder cancer microenvironment.

Methods

Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We divided bladder cancer patients into high and low groups based on their immune/stromal scores. Then, differentially expressed genes (DEGs) were identified in bladder cancer patients based on the TCGA database. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. Finally, we validated identified genes associated with bladder cancer prognosis through a cohort study in the Gene Expression Omnibus (GEO) database.

Results

A higher stromal score was associated with female (vs. malep = 0.037), age > 65 (vs.age ≤ 65 p = 0.015), T3/4 (vs. T1/2,p < 0.001), N status(p = 0.016), and pathological high grade (vs. low gradeP < 0.001). By analyzing DEGs, there were 1125 genes commonly upregulated, and 209 genes were commonly downregulated. Protein-protein interaction networks further showed the important protein that may be involved in the biological behavior and prognosis of BCa, such as FN1, CXCL12, CD3E, LCK, and ZAP70. A total of 14 DEGs were found to be associated with overall survival of bladder cancer. After validation by a cohort of 165 BCa cases with detailed follow-up information from GSE13507, 10 immune-associated DEGs were demonstrated to be predictive of prognosis in BCa. Among them, 5 genes have not been reported previously associated with the prognosis of BCa, including BTBD16, OLFML2B, PRRX1, SPINK4, and SPON2.

Conclusions

Our study elucidated tight associations between stromal score and clinical characteristics as well as prognosis in BCa. Moreover, we obtained a group of genes closely related to the prognosis of BCa in the tumor microenvironment.

A 70-Gene Signature for Predicting Treatment Outcome in Advanced-Stage Cervical Cancer

Cervical cancer is the fourth most common cancer in women worldwide. The current approaches still have limitations in predicting the therapy outcome of each individual because of cancer heterogeneity. The goal of this study was to establish a gene expression signature that could help when choosing the right therapeutic method for the treatment of advanced-stage cervical cancer. The 666 patients were collected from four independent datasets. The 70-gene expression signature was established using univariate Cox proportional hazard regression analysis. The 70-gene signature was significantly different between low- and high-risk groups in the training dataset (p = 4.24e-6) and in the combined three validation datasets (p = 4.37e-3). Treatment of advanced-stage cancer patients in the high-risk group with molecular-targeted therapy combined with chemoradiotherapy yielded a better survival rate than with only chemoradiotherapy (p = 0.0746). However, treatment of the patients in the low-risk group with the combined therapy resulted in significantly lower survival (p = 0.00283). Functional classification of 70 genes revealed involvement of the angiogenesis pathway, specifically phosphatidylinositol 3-kinase signaling (p = 0.040), extracellular matrix organization (p = 0.0452), and cell adhesion (p = 0.011). The 70-gene signature could predict the prognosis and indicate an optimal therapeutic modality in molecular-targeted therapy or chemotherapy for advanced-stage cervical cancer.

Paeonol exerts anti‑tumor activity against colorectal cancer cells by inducing G0/G1 phase arrest and cell apoptosis via inhibiting the Wnt/β‑catenin signaling pathway

Paeonol is a simple phenolic compound isolated from herbal root bark, which has been reported to possess numerous biological and pharmacological characteristics, including a desirable anti‑tumor effect. To date, the effect of paeonol against colorectal cancer (CRC) cells is yet to be fully elucidated. Therefore, the present study aimed to identify the underlying mechanism via which paeonol exerts its anti‑tumor activity on HCT116 cells. After incubation with various concentrations of paeonol (7.8125, 15.625, 31.25, 62.5, 125, 250 and 500 µg/ml), the inhibitory effect of paeonol on cell viability was assessed using a Cell Counting Kit‑8 assay. Cell apoptosis and cell cycle distribution were measured using flow cytometry. Moreover, caspase activity was measured using a colorimetric caspase assay. Luciferase assay was also used to determine the β‑catenin‑mediated transcriptional activity of T‑cell specific transcription factor/lymphoid‑enhancer binding factor (TCF/LEF), and western blotting analysis was performed to measure the related expression of proteins. The results indicated that paeonol exhibited a notable effect against HCT116 cells by inducing G0/G1‑phase arrest, as demonstrated by downregulation of the cell cycle regulators cyclin‑dependent kinase 4 and cyclin D1 and upregulation of p21Cip1 in a dose‑dependent manner. Furthermore, paeonol dose‑dependently induced cell apoptosis, accompanied by an increase in the Bax/Bcl‑2 ratio, release of cytochrome c and further activation of caspases. Paeonol also dose‑dependently blocked the activation of the Wnt/β‑catenin signaling pathway by suppressing the expression of β‑catenin, resulting in a decrease in β‑catenin‑mediated activity of TCF/LEF and downregulation of downstream target genes, including cyclin D1, survivin and c‑Myc. Therefore, the present results suggested that paeonol exerted its anti‑tumor effects on CRC cells, including the inhibition of cell proliferation, induction of cell cycle arrest and initiation of apoptosis, at least partly by suppressing the Wnt/β‑catenin pathway, which may offer a promising therapeutic strategy for CRC.

MethCORR modelling of methylomes from formalin-fixed paraffin-embedded tissue enables characterization and prognostication of colorectal cancer

Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management. Yet, robust transcriptional profiling is difficult using formalin-fixed, paraffin-embedded (FFPE) samples, which complicates testing in clinical and archival material. We present MethCORR, an approach that allows uniform molecular characterization and classification of fresh-frozen and FFPE samples. MethCORR identifies genome-wide correlations between RNA expression and DNA methylation in fresh-frozen samples. This information is used to infer gene expression information in FFPE samples from their methylation profiles. MethCORR is here applied to methylation profiles from 877 fresh-frozen/FFPE samples and comparative analysis identifies the same two subtypes in four independent cohorts. Furthermore, subtype-specific prognostic biomarkers that better predicts relapse-free survival (HR = 2.66, 95%CI [1.67-4.22], P value < 0.001 (log-rank test)) than UICC tumor, node, metastasis (TNM) staging and microsatellite instability status are identified and validated using DNA methylation-specific PCR. The MethCORR approach is general, and may be similarly successful for other cancer types.

Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study

BACKGROUND

Clustering of gene expression data is widely used to identify novel subtypes of cancer. Plenty of clustering approaches have been proposed, but there is a lack of knowledge regarding their relative merits and how data characteristics influence the performance. We evaluate how cluster analysis choices affect the performance by studying four publicly available human cancer data sets: breast, brain, kidney and stomach cancer. In particular, we focus on how the sample size, distribution of subtypes and sample heterogeneity affect the performance.

RESULTS

In general, increasing the sample size had limited effect on the clustering performance, e.g. for the breast cancer data similar performance was obtained for n = 40 as for n = 330. The relative distribution of the subtypes had a noticeable effect on the ability to identify the disease subtypes and data with disproportionate cluster sizes turned out to be difficult to cluster. Both the choice of clustering method and selection method affected the ability to identify the subtypes, but the relative performance varied between data sets, making it difficult to rank the approaches. For some data sets, the performance was substantially higher when the clustering was based on data from only one sex compared to data from a mixed population. This suggests that homogeneous data are easier to cluster than heterogeneous data and that clustering males and females individually may be beneficial and increase the chance to detect novel subtypes. It was also observed that the performance often differed substantially between females and males.

CONCLUSIONS

The number of samples seems to have a limited effect on the performance while the heterogeneity, at least with respect to sex, is important for the performance. Hence, by analyzing the genders separately, the possible loss caused by having fewer samples could be outweighed by the benefit of a more homogeneous data.

Overexpression of microRNA-98 inhibits cell proliferation and promotes cell apoptosis via claudin-1 in human colorectal carcinoma

Colorectal carcinoma (CRC) is a major cause of cancer-related deaths worldwide, and investigations on novel targets are imperative. MiR-98 has been reported to act as a tumor suppressor in several cancers. To evaluate miR-98 as a novel anticancer molecule for CRC, examinations to validate whether miR-98 conferred an inhibiting effect on proliferation, migration, and invasion were performed. The microarray-based gene expression profiling involving CRC was used to identify the differentially expressed genes. The potential relationship between miR-98 and CLDN1 was analyzed by cell experimentation. Then, the CRC cells were transfected with miR-98 mimic or miR-98 inhibitor to investigate the potential effect of miR-98 overexpression and depletion on CRC cell proliferation, migration, invasion, and apoptosis. The expressions of CLDN1, Bcl-2 associated protein x (Bax), runt-related transcription factor 3 (RUNX3), B-cell lymphoma 2 (Bcl-2), C-myc, and proliferating cell nuclear antigen (PCNA) were determined. The downregulated miR-98 along with an upregulated CLDN1 was observed in CRC, in which miR-98 could target to regulate CLDN1. The overexpression of miR-98 or silencing of CLDN1 was shown to increase the expression of Bax and RUNX3 along with promoted cell apoptosis and arrested cells in G1 phase, while decreasing the expression of CLDN1, Bcl-2, C-myc, and PCNA with suppressed proliferation, migration, and invasion. Collectively, the current study supports the notion that miR-98 plays an inhibitory role in human CRC cell proliferation, migration, and invasion and act as a contributor for cell apoptosis by downregulating CLDN1. The current study highlights a potential future strategy to help prevent the development of CRC.

Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling

Background

Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information.

Results

We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75–2.61).

Conclusions

The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5193-9) contains supplementary material, which is available to authorized users.

Nomogram Integrating Genomics with Clinicopathologic Features Improves Prognosis Prediction for Colorectal Cancer

The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic-genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183-5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218-4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic-genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255-0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469-0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373-84. ©2018 AACR.

The Application of Gene Expression Profiling in Predictions of Occult Lymph Node Metastasis in Colorectal Cancer Patients

A key factor in determining the likely outcome for a patient with colorectal cancer is whether or not the tumour has metastasised to the lymph nodes-information which is also important in assessing any possibilities of lymph node resection so as to improve survival. In this review we perform a wide-range assessment of literature relating to recent developments in gene expression profiling (GEP) of the primary tumour, to determine their utility in assessing node status. A set of characteristic genes seems to be involved in the prediction of lymph node metastasis (LNM) in colorectal patients. Hence, GEP is applicable in personalised/individualised/tailored therapies and provides insights into developing novel therapeutic targets. Not only is GEP useful in prediction of LNM, but it also allows classification based on differences such as sample size, target gene expression, and examination method.

A four-gene signature predicts survival in clear-cell renal-cell carcinoma

Clear-cell renal-cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma (RCC), accounting for about 80% of RCC. In order to find potential prognostic biomarkers in ccRCC, we presented a four-gene signature to evaluate the prognosis of ccRCC. SurvExpress and immunohistochemical (IHC) staining of tissue microarrays were used to analyze the association between the four genes and the prognosis of ccRCC. Data from TCGA dataset revealed a prognostic prompt function of the four genes (PTEN, PIK3C2A, ITPA and BCL3). Further discovery suggested that the four-gene signature predicted survival better than any of the four genes alone. Moreover, IHC staining demonstrated a consistent result with TCGA, indicating that the signature was an independent prognostic factor of survival in ccRCC. Univariate and multivariate Cox proportional hazard regression analysis were conducted to verify the association of clinicopathological variables and the four genes' expression levels with survival. The results further testified that the risk (four-gene signature) was an independent prognostic factors of both Overall Survival (OS) and Disease-free Survival (DFS) (P<0.05). In conclusion, the four-gene signature was correlated with the survival of ccRCC, and therefore, may help to provide significant clinical implications for predicting the prognosis of patients.

An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer

Identification of a potential gene signature for improved diagnosis in non-small cell lung cancer (NSCLC) patient is necessary. Here, we aim to establish and validate the prognostic efficacy of a gene set that can predict prognosis and benefits of adjuvant chemotherapy (ACT) in NSCLC patients from various ethnicities. An 8-gene signature was calculated from the gene expression of 181 patients using univariate Cox proportional hazard regression analysis. The prognostic value of the signature was robustly validated in 1,477 patients from five microarray independent data sets and one RNA-seq data set. The 8-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses [hazard ratio (HR): 2.84, 95% confidence interval CI (1.74-4.65), p=3.06e-05, [HR] 2.62, 95% CI (1.51-4.53), p=0.001], respectively. Subset analysis demonstrated that the 8-gene signature could identify high-risk patients in stage II-III with improved survival from ACT [(HR) 1.47, 95% CI (1.01-2.14), p=0.044]. The 8-gene signature also stratified risk groups in EGFR-mutated and wild-type patients. In conclusion, the 8-gene signature is a strong and independent predictor that can significantly stratify patients into low- and high-risk groups. Our gene signature also has the potential to predict patients in stage II-III that are likely to benefit from ACT.

Prognostic value and their clinical implication of 89-gene signature in glioma

Gliomas are the most common and aggressive primary tumors in adults. The current approaches, such as histological classification and molecular genetics, have limitation in prediction of individual therapeutic outcomes due to heterogeneity within the tumor groups. Recent studies have proposed several gene signatures to predict glioma's prognosis. However, most of the gene expression profiling studies have been performed on relatively small number of patients and combined probes from diverse microarray chips. Here, we identified prognostic 89 common genes from diverse microarray chips. The 89-gene signature classified patients into good and bad prognostic groups which differed in the overall survival significantly, reflecting the biological characteristics and heterogeneity. The robustness and accuracy of the gene signature as an independent prognostic factor was validated in three microarray and one RNA-seq data sets independently. By incorporating into histological classification and molecular marker, the 89-gene signature could further stratify patients with 1p/19q co-deletion and IDH1 mutation. Additionally, subset analyses suggested that the 89-gene signature could predict patients who would benefit from adjuvant chemotherapy. Conclusively, we propose that the 89-gene signature would have an independent and accurate prognostic value for clinical use. This study also offers opportunities for novel targeted treatment of individual patients.

Prognostic impact of a novel gene expression profile classifier for the discrimination between metastatic and non-metastatic primary colorectal cancer tumors

Despite significant advances have been achieved in the genetic characterization of sporadic colorectal cancer (sCRC), the precise genetic events leading to the development of distant metastasis remain poorly understood. Thus, accurate prediction of metastatic disease in newly-diagnosed sCRC patients remains a challenge. Here, we evaluated the specific genes and molecular pathways associated with the invasive potential of colorectal tumor cells, through the assessment of the gene expression profile (GEP) of coding and non-coding genes in metastatic (MTX) vs. non-metastatic (non-MTX) primary sCRC tumors followed for >5 years. Overall, MTX tumors showed up-regulation of genes associated with tumor progression and metastatic potential while non-MTX cases displayed GEP associated with higher cell proliferation, activation of DNA repair and anti-tumoral immune/inflammatory responses. Based on only 19 genes a specific GEP that classifies sCRC tumors into two MTX-like and non-MTX-like molecular subgroups was defined which shows an independent prognostic impact on patient overall survival, particularly when it is combined with the lymph node status at diagnosis. In summary, we show an association between the global GEP of primary sCRC cells and their metastatic potential and defined a GEP-based classifier that provides the basis for further prognostic stratification of sCRC patients who are at risk of distant metastases.

A long non-coding RNA expression signature to predict survival of patients with colon adenocarcinoma

Colon cancer is the most common type of gastrointestinal cancer and is still the leading cause of cancer-related mortality worldwide. Long non-coding RNAs (lncRNAs) have been proved to be superior biomarkers in cancer diagnosis and prognosis than miRNAs and protein-coding genes. In the current study, our objective was to detect novel lncRNA biomarkers by analyzing lncRNA expression profiles and clinical data in a large cohort of patients with colon patients from The Cancer Genome Atlas (TCGA). By using Cox regression analysis, we identified two lncRNAs (SNHG6 and CTD-2354A18.1) which could be independent prognostic factors for predicting clinical outcome in colon adenocarcinoma. Then a linear combination of these two lncRNA biomarkers (SNHG6 and CTD-2354A18.1), termed two-lncRNA signature, was developed in the training set as a predictor for OS in patients with colon adenocarcinoma, and validated in the testing set and the entire patient set. This two-lncRNA signature demonstrated significant prognostic performance in both the testing set and the entire patient set which classified the patients into two groups with significantly different OS. The multivariate and stratified analysis suggested that the prognostic value of the two-lncRNA signature was independent of other traditional clinical variables. Functional analysis suggested that these two lncRNA biomarkers might be mainly involved in transcription/translation-related or DNA repair-related biological processes. In summary, our results warrant further studies on these lncRNAs that will improve our understanding of the mechanisms associated with pathogenesis and progression of colon adenocarcinoma.

The Prognostic 97 Chemoresponse Gene Signature in Ovarian Cancer

Patient diagnosis and care would be significantly improved by understanding the mechanisms underlying platinum and taxane resistance in ovarian cancer. Here, we aim to establish a gene signature that can identify molecular pathways/transcription factors involved in ovarian cancer progression, poor clinical outcome, and chemotherapy resistance. To validate the robustness of the gene signature, a meta-analysis approach was applied to 1,020 patients from 7 datasets. A 97-gene signature was identified as an independent predictor of patient survival in association with other clinicopathological factors in univariate [hazard ratio (HR): 3.0, 95% Confidence Interval (CI) 1.66–5.44, p = 2.7E-4] and multivariate [HR: 2.88, 95% CI 1.57–5.2, p = 0.001] analyses. Subset analyses demonstrated that the signature could predict patients who would attain complete or partial remission or no-response to first-line chemotherapy. Pathway analyses revealed that the signature was regulated by HIF1α and TP53 and included nine HIF1α-regulated genes, which were highly expressed in non-responders and partial remission patients than in complete remission patients. We present the 97-gene signature as an accurate prognostic predictor of overall survival and chemoresponse. Our signature also provides information on potential candidate target genes for future treatment efforts in ovarian cancer.

Standardising RNA profiling based biomarker application in cancer-The need for robust control of technical variables

Histopathology-based staging of colorectal cancer (CRC) has utility in assessing the prognosis of patient subtypes, but as yet cannot accurately predict individual patient's treatment response. Transcriptomics approaches, using array based or next generation sequencing (NGS) platforms, of formalin fixed paraffin embedded tissue can be harnessed to develop multi-gene biomarkers for predicting both prognosis and treatment response, leading to stratification of treatment. While transcriptomics can shape future biomarker development, currently <1% of published biomarkers become clinically validated tests, often due to poor study design or lack of independent validation. In this review of a large number of CRC transcriptional studies, we identify recurrent sources of technical variability that encompass collection, preservation and storage of malignant tissue, nucleic acid extraction, methods to quantitate RNA transcripts and data analysis pipelines. We propose a series of defined steps for removal of these confounding issues, to ultimately aid in the development of more robust clinical biomarkers.

Cell cycle related genes up-regulated in human colorectal development predict the overall survival of late-stage colorectal cancer patients

A tumor can be perceived as a special "organ" that undergoes aberrant and poorly regulated organogenesis. Embryonic development and carcinogenesis share striking similarities in their cellular behavior and underlying molecular mechanisms. This intimate association makes embryonic development a viable reference model for studying cancer thereby circumventing the potentially misleading complexity of tumor heterogeneity. Therefore, on the basis of global expression profile, the genes simultaneously activated (up-regulated in terms of expression profile) or suppressed (down-regulated) in both the embryonic development and cancer stage, probably contain profound information on the molecular mechanism of cancer. In this study, the Affymetrix expression profile of 1593 colorectal cancer samples was downloaded from Gene Expression Omnibus. The 1396 differentially expressed probes were robustly obtained using 660 colorectal normal and cancer samples, the expression pattern of which was analyzed using our human colorectal developmental data. All of these 1396 probes were classified into 27 distinct patterns based on their expression patterns during the developmental process. By means of gene set enrichment analysis, we collected 393 V probes simultaneously up-regulated in both development and carcinogenesis and 207 A probes down-regulated in both. Functional enrichment analysis indicated that the V probes were significantly related to cell cycle regulation. Notably, 28 cell-cycle related probes within the V probe group were found to be significantly associated with an overall survival of Stage III/IV patients (GSE17536 cross validation, n = 96, p = 5.70 × 10(-3); GSE29621, n = 36, p = 1.70 × 10(-3); GSE39084, n = 38, p = 0.05; GSE39582, n = 264, p = 0.047; GSE17537, n = 36, p = 5.90 × 10(-3)).

A 19-Gene expression signature as a predictor of survival in colorectal cancer

Background

Histopathological assessment has a low potential to predict clinical outcome in patients with the same stage of colorectal cancer. More specific and sensitive biomarkers to determine patients’ survival are needed. We aimed to determine gene expression signatures as reliable prognostic marker that could predict survival of colorectal cancer patients with Dukes’ B and C.

Methods

We examined microarray gene expression profiles of 78 archived tissues of patients with Dukes’ B and C using the Illumina DASL assay. The gene expression data were analyzed using the GeneSpring software and R programming.

Results

The outliers were detected and replaced with randomly chosen genes from the 90 % confidence interval of the robust mean for each group. We performed three statistical methods (SAM, LIMMA and t-test) to identify significant genes. There were 19 significant common genes identified from microarray data that have been permutated 100 times namely NOTCH2, ITPRIP, FRMD6, GFRA4, OSBPL9, CPXCR1, SORCS2, PDC, C12orf66, SLC38A9, OR10H5, TRIP13, MRPL52, DUSP21, BRCA1, ELTD1, SPG7, LASS6 and DUOX2. This 19-gene signature was able to significantly predict the survival of patients with colorectal cancer compared to the conventional Dukes’ classification in both training and test sets (p < 0.05). The performance of this signature was further validated as a significant independent predictor of survival using patient cohorts from Australia (n = 185), USA (n = 114), Denmark (n = 37) and Norway (n = 95) (p < 0.05). Validation using quantitative PCR confirmed similar expression pattern for the six selected genes.

Conclusion

Profiling of these 19 genes may provide a more accurate method to predict survival of patients with colorectal cancer and assist in identifying patients who require more intensive treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0218-1) contains supplementary material, which is available to authorized users.

A seven-gene signature predicts overall survival of patients with colorectal cancer

Colorectal cancer (CRC) is a major cause of global cancer mortality. Gene expression profiles can help predict prognosis of patients with CRC. In most of previous studies, disease recurrence was analyzed as the survival endpoint. Thus we aim to build a robust gene signature for prediction of overall survival (OS) in patients with CRC. Fresh frozen CRC tissues from 64 patients were analyzed using Affymetrix HG-U133plus 2.0 gene arrays. By performing univariate survival analysis, 6487 genes were found to be associated with the OS in our cohort. KEGG analysis revealed that these genes were mainly involved in pathways such as endocytosis, axon guidance, spliceosome, Wnt signalling and ubiquitin mediated proteolysis. A seven-gene signature was further selected by a robust likelihood-based survival modelling approach. The prognostic model of seven-gene signature (NHLRC3, ZDHHC21, PRR14L, CCBL1, PTPRB, PNPO, and PPIP5K2) was constructed and weighted by regression coefficient, which divided patients into high- and low-risk groups. The OS for patients in high-risk group was significantly poorer compared with patients in low-risk group. Moreover, all seven genes were found to be differentially expressed in CRC tissues as compared with adjacent normal tissues, indicating their potential role in CRC initiation and progression. This seven-gene signature was further validated as an independent prognostic marker for OS prediction in patients with CRC in other two independent cohorts. In short, we developed a robust seven-gene signature that can predict the OS for CRC patients, providing new insights into identification of CRC patients with high risk of mortality.