Gene Expression Profiles and Molecular Markers To Predict Recurrence of Dukes' B Colon Cancer

Cellular and molecular events in colorectal cancer: biological mechanisms, cell death pathways, drug resistance and signalling network interactions

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, affecting millions each year. It emerges from the colon or rectum, parts of the digestive system, and is closely linked to both genetic and environmental factors. In CRC, genetic mutations such as APC, KRAS, and TP53, along with epigenetic changes like DNA methylation and histone modifications, play crucial roles in tumor development and treatment responses. This paper delves into the complex biological underpinnings of CRC, highlighting the pivotal roles of genetic alterations, cell death pathways, and the intricate network of signaling interactions that contribute to the disease's progression. It explores the dysregulation of apoptosis, autophagy, and other cell death mechanisms, underscoring the aberrant activation of these pathways in CRC. Additionally, the paper examines how mutations in key molecular pathways, including Wnt, EGFR/MAPK, and PI3K, fuel CRC development, and how these alterations can serve as both diagnostic and prognostic markers. The dual function of autophagy in CRC, acting as a tumor suppressor or promoter depending on the context, is also scrutinized. Through a comprehensive analysis of cellular and molecular events, this research aims to deepen our understanding of CRC and pave the way for more effective diagnostics, prognostics, and therapeutic strategies.

Liquid biopsy for breast cancer and other solid tumors: a review of recent advances

Liquid biopsy using circulating tumor DNA (ctDNA) has been reported to be less invasive and effective for comprehensive genetic analysis of heterogeneous solid tumors, including decision-making for therapeutic strategies, predicting recurrence, and detecting genetic factors related to treatment resistance in various types of cancers. Breast cancer, colorectal cancer, and lung cancer are among the most prevalent malignancies worldwide, and clinical studies of liquid biopsy for these cancers are ongoing. Liquid biopsy has been used as a companion diagnostic tool in clinical settings, and research findings have accumulated, especially in cases of colorectal cancer after curative resection and non-small cell lung cancer (NSCLC) after curative chemoradiotherapy, in which ctDNA detection helps predict eligibility for adjuvant chemotherapy. Liquid biopsy using ctDNA shows promise across a wide range of cancer types, including breast cancer, and its clinical applications are expected to expand further through ongoing research. In this article, studies on liquid biopsy in breast cancer, colorectal cancer, and NSCLC are compared focusing on ctDNA.

Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model

Background:

Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.

Methods:

A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes.

Results:

Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis.

Conclusion:

This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.

Low Expression of BRCA1 as a Potential Relapse Predictor in B-Cell Acute Lymphoblastic Leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood hematological malignancy worldwide. Treatment outcomes have improved dramatically in recent years; despite this, relapse is still a problem, and the potential molecular explanation for this remains an important field of study. We performed microarray and single-cell RNA-Seq data mining, and we selected significant data with a P -value<0.05. We validated BRCA1 gene expression by means of quantitative (reverse transcription-polymerase chain reaction.) We performed statistical analysis and considered a P -value<0.05 significant. We identified the overexpression of breast cancer 1, early onset (BRCA1; P -value=2.52 -134 ), by means of microarray analysis. Moreover, the normal distribution of BRCA1 expression in healthy bone marrow. In addition, we confirmed the increases in BRCA1 expression using real-time (reverse transcription-polymerase chain reaction and determined that it was significantly reduced in patients with relapse ( P -values=0.026). Finally, we identified that the expression of the BRCA1 gene could predict early relapse ( P -values=0.01). We determined that low expression of BRCA1 was associated with B-cell acute lymphoblastic leukemia relapse and could be a potential molecular prognostic marker.

MicroRNAs as Predictive Biomarkers in Patients with Colorectal Cancer Receiving Chemotherapy or Chemoradiotherapy: A Narrative Literature Review

Colorectal cancer (CRC) is one of the most common malignancies and is associated with high mortality rates worldwide. The underlying mechanism of tumorigenesis in CRC is complex, involving genetic, lifestyle-related, and environmental factors. Although radical resection with adjuvant FOLFOX (5-fluorouracil, leucovorin, and oxaliplatin) chemotherapy and neoadjuvant chemoradiotherapy have remained mainstays of treatment for patients with stage III CRC and locally advanced rectal cancer, respectively, the oncological outcomes of these treatments are often unsatisfactory. To improve patients' chances of survival, researchers are actively searching for new biomarkers to facilitate the development of more effective treatment strategies for CRC and metastatic CRC (mCRC). MicroRNAs (miRs), small, single-stranded, noncoding RNAs, can post-transcriptionally regulate mRNA translation and trigger mRNA degradation. Recent studies have documented aberrant miR levels in patients with CRC or mCRC, and some miRs are reportedly associated with chemoresistance or radioresistance in CRC. Herein, we present a narrative review of the literature on the roles of oncogenic miRs (oncomiRs) and tumor suppressor miRs (anti-oncomiRs), some of which can be used to predict the responses of patients with CRC to chemotherapy or chemoradiotherapy. Moreover, miRs may serve as potential therapeutic targets because their functions can be manipulated using synthetic antagonists and miR mimics.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Gene Expression Profiles in Cancers and Their Therapeutic Implications

ABSTRACT

The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.

A pairwise strategy for imputing predictive features when combining multiple datasets

MOTIVATION

In the training of predictive models using high-dimensional genomic data, multiple studies' worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured in individual or subsets of studies.

RESULTS

We characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of prediction functions through simulation and in gene expression data collected on breast cancer patients. To improve this process, we propose a pairwise strategy that applies any imputation algorithm to two studies at a time and averages imputed features across pairs. We demonstrate that the pairwise strategy is preferable to first merging all datasets together and imputing any resulting missing features. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/YujieWuu/Pairwise_imputation.

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.

Constructing a prognostic immune-related lncRNA model for colon cancer

Colon cancer is a common digestive tract tumor. Although many gene prognostic indicators have been used to predict the prognosis of colon cancer patients, the accuracy of these prognostic indicators is still uncertain. Thus, it is necessary to construct a model for the prognostic analysis of colon cancer. We downloaded the original transcriptome data of colon cancer and performed a differential coexpression analysis of immune-related genes to obtain different immune-related long noncoding RNAs, which were paired as differentially expressed immune-related lncRNA pairs (DEirlncRNAPs). Then, the 1-year overall survival rate receiver operating characteristic curve was calculated, and the Akaike information criterion value was evaluated to determine the maximum inflection point, which was used as the cutoff point to identify groups of colon cancer patients at high and low risk for death. Subsequently, the optimal prediction model was established. Finally, we used the patients' survival times, clinicopathological features, tumor infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers to verify the DEirlncRNAP model. Seventy-one DEirlncRNAPs were obtained to build the risk assessment model. The patients were divided into a high-risk group and a low-risk group according to the cutoff point. Then, the DEirlncRNAP model was verified using patient survival times, clinicopathological features, tumor-infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers. A new DEirlncRNAP model for predicting the prognosis of colon cancer patients was established, which could reveal new insights into the relationships of colon cancer with tumor-infiltrating immune cells and antitumor immunotherapy.

CENPF as an independent prognostic and metastasis biomarker corresponding to CD4+ memory T cells in cutaneous melanoma

Owing to recent advances in immunotherapies, the overall survival of patients with skin cutaneous melanoma (SKCM) has increased; however, the 5-year survival rate of metastatic patients remains poor. Skin cutaneous melanoma-upregulated genes were screened via analysis of differentially expressed genes (GSE3189 and GSE46517), and metastasis-related oncogenes were identified via weighted gene coexpression network analysis of the GSE46517 dataset. As confirmed by the Tumor Immune Estimation Resource, we found highly expressed centromere protein F (CENPF) in SKCM and its metastases. Immunostaining of human melanoma tissues demonstrated high CENPF expression. According to the Kaplan-Meier survival curve log-rank test, receiver-operating characteristic curve, and univariate and multivariate analyses, the Cancer Genome Atlas (TCGA) database suggested CENPF be a typical independent predictor of SKCM. The CIBERSORT algorithm classified the types of the immune cells from GSE46517 and showed higher proportion of CD4+ memory-activated T cells in metastatic melanoma. Single-sample gene set enrichment analysis of TCGA data confirmed the correlation between CENPF and activated CD4+ T cells. Centromere protein F was positively correlated with tumor mutational burden and CD4+ memory T cell markers (interleukin [IL]-23A, CD28, and CD62L), negatively associated with memory T cell maintenance factors (IL-7 and IL-15) by correlation analysis. Moreover, immunofluorescence showed high coexpression of CENPF and IL23A, CD4 in melanoma. Upregulated CENPF might lead to premature depletion of CD4+ memory T cells and immunosuppression. Nomogram indicated CENPF clinical predictive value for 1-, 3-, 5-, and 7-year melanoma overall survival. Therefore, CENPF plays a vital role in the progression and metastasis of melanoma and can be an effective therapeutic target.

Prognostic Nomogram of Prognosis-Related Genes and Clinicopathological Characteristics to Predict the 5-Year Survival Rate of Colon Cancer Patients

Background: The Cancer Genome Atlas (TCGA) has established a genome-wide gene expression profile, increasing our understanding of the impact of tumor heredity on clinical outcomes. The aim of this study was to construct a nomogram using data from the TCGA regarding prognosis-related genes and clinicopathological characteristics to predict the 5-years survival rate of colon cancer (CC) patients. Methods: Kaplan-Meier and Cox regression analyses were used to identify genes associated with the 5-years survival rate of CC patients. Cox regression was used to analyze the relationship between the clinicopathological features and prognostic genes and overall survival rates in patients with CC and to identify independent risk factors for the prognosis of CC patients. A nomogram for predicting the 5-years survival rate of CC patients was constructed by R software. Results: A total of eight genes (KCNJ14, CILP2, ATP6V1G2, GABRD, RIMKLB, SIX2, PLEKHA8P1, and MPP2) related to the 5-years survival of rate CC patients were identified. Age, stage, and PLEKHA8P1 were independent risk factors for the 5-years survival rate in patients with CC. The accuracy, sensitivity and specificity of the nomogram model constructed by age, TNM staging, and PLEKHA8P1 for predicting the 5-years survival of rate CC patients were 83.3, 83.97, and 85.79%, respectively. Conclusion: The nomogram can correctly predict the 5-year survival rate of patients with CC, thus aiding the individualized decision-making process for patients with CC.

Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.

Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).

Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.

Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

Prognostic implications of an autophagy-based signature in colorectal cancer

BACKGROUND

The heterogeneity of colorectal cancer (CRC) poses a significant challenge to the precise treatment of patients. CRC has been divided into 4 consensus molecular subtypes (CMSs) with distinct biological and clinical characteristics, of which CMS4 has the mesenchymal identity and the highest relapse rate. Autophagy plays a vital role in CRC development and therapeutic response.

METHODS

The gene expression profiles collected from 6 datasets were applied to this study. Network analysis was applied to integrate the subtype-specific molecular modalities and autophagy signature to establish an autophagy-based prognostic signature for CRC (APSCRC).

RESULTS

Network analysis revealed that 6 prognostic autophagy genes (VAMP7, DLC1, FKBP1B, PEA15, PEX14, and DNAJB1) predominantly regulated the mesenchymal modalities of CRC. The APSCRC was constructed by these 6 core genes and applied for risk calculation. Patients were divided into high- and low-risk groups based on APSCRC score in all cohorts. Patients within the high-risk group showed an unfavorable prognosis. In multivariate analysis, the APSCRC remained an independent predictor of prognosis. Moreover, the APSCRC achieved higher prognostic power than commercialized multigene signatures.

CONCLUSIONS

We proposed and validated an autophagy-based signature, which is a promising prognostic biomarker of CRC patients. Further prospective studies are warranted to test and validate its efficiency for clinical application.

Epigenetic alterations in ameloblastomas: A literature review

Background

Ameloblastoma is a locally aggressive tumor, originated from odontogenic epithelium, and affects the jawbones with an elevated recurrence rate. The molecular mechanisms involved with the pathogenesis of this tumor remain undetermined. This review aimed to describe the current data regarding epigenetic alterations in ameloblastoma.

Material and Methods

A systematized electronic search was performed in the English-language literature in three databases, combining the following keywords: ameloblastoma, epigenetic, methylation, noncoding RNA, histone acetylation.

Results

According to the gathered results of 11 studies in this review, epigenetic alterations could induce the development and progression of ameloblastoma. DNA methylation has been the most assessed mechanism in ameloblastomas.

Conclusions

Current literature data indicate that epigenetic events can be involved in the etiopathogenesis of ameloblastomas. Key words:Ameloblastoma, epigenetic, methylation, noncoding RNA, histone acetylation.

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning

PURPOSE

Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs).

METHODS

We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein-protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells.

RESULTS

A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation.

CONCLUSION

Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

Adjuvant Chemotherapy for Stage III Colon Cancer

In patients with stage III colon cancer (CC), adjuvant chemotherapy with the combination of oxapliplatin to a fluoropyrimidine (FOLFOX or CAPOX) is a standard of care. The duration of treatment can be reduced from 6 months to 3 months, depending on the regimen, for patients at low risk of recurrence, without loss of effectiveness and allowing a significant reduction in the risk of cumulative sensitive neuropathy. However, our capacity to identify patients that do really need this doublet adjuvant treatment remains limited. In fact, only 30% at the most will actually benefit from this adjuvant treatment, 50% of them being already cured by the surgery and 20% of them experiencing disease recurrence despite the adjuvant treatment. Thus, it is necessary to be able to better predict individually for each patient the risk of recurrence and the need for adjuvant chemotherapy together with the need of new treatment approaches for specific subgroups. Many biomarkers have been described with their own prognostic weight, without leading to any change in clinical practices for now. In this review, we will first discuss the recommendations for adjuvant chemotherapy, and then the different biomarkers described and the future perspectives for the management of stage III CC.

Signatures of co-deregulated genes and their transcriptional regulators in colorectal cancer

The deregulated genes in colorectal cancer (CRC) vary significantly across different studies. Thus, a systems biology approach is needed to identify the co-deregulated genes (co-DEGs), explore their molecular networks, and spot the major hub proteins within these networks. We reanalyzed 19 GEO gene expression profiles to identify and annotate CRC versus normal signatures, single-gene perturbation, and single-drug perturbation signatures. We identified the co-DEGs across different studies, their upstream regulating kinases and transcription factors (TFs). Connectivity Map was used to identify likely repurposing drugs against CRC within each group. The functional changes of the co-upregulated genes in the first category were mainly associated with negative regulation of transforming growth factor β production and glomerular epithelial cell differentiation; whereas the co-downregulated genes were enriched in cotranslational protein targeting to the membrane. We identified 17 hub proteins across the co-upregulated genes and 18 hub proteins across the co-downregulated genes, composed of well-known TFs (MYC, TCF3, PML) and kinases (CSNK2A1, CDK1/4, MAPK14), and validated most of them using GEPIA2 and HPA, but also through two signature gene lists composed of the co-up and co-downregulated genes. We further identified a list of repurposing drugs that can potentially target the co-DEGs in CRC, including camptothecin, neostigmine bromide, emetine, remoxipride, cephaeline, thioridazine, and omeprazole. Similar analyses were performed in the co-DEG signatures in single-gene or drug perturbation experiments in CRC. MYC, PML, CDKs, CSNK2A1, and MAPKs were common hub proteins among all studies. Overall, we identified the critical genes in CRC and we propose repurposing drugs that could be used against them.

Integrated analysis identifies an immune-based prognostic signature for the mesenchymal identity in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) has been divided into 4 consensus molecular subtypes (CMSs), of which CMS4 has the mesenchymal identity and the highest relapse rate. Our goal is to develop a prognostic signature by integrating the immune system and mesenchymal modalities involved in CMS4.

METHODS

The gene expression profiles collected from 5 public datasets were applied to this study, including 1280 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for CRC (IPSCRC).

RESULTS

We identified 6 immune genes as key factors of CMS4 and established the IPSCRC. The IPSCRC could significantly divide patients into high- and low- risk groups in terms of relapse-free survival (RFS) and overall survival (OS) and in discovery (RFS: P < .0001) and 4 independent validation sets (RFS range: P = .01 to <.0001; OS range: P = .02-.0004). After stage stratification, the IPSCRC could still distinguish poor prognosis patients in discovery (RFS: P = .04) and validation cohorts (RFS range: P = .04-.007) within stage II in terms of RFS. Further, in multivariate analysis, the IPSCRC remained an independent predictor of prognosis. Moreover, Macrophage M2 was significantly enriched in the high-risk group, while plasma cells enriched in the low-risk group.

CONCLUSION

We propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of CRC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.

Development and validation of a lipogenic genes panel for diagnosis and recurrence of colorectal cancer

Background & aim

Accumulated evidence indicates that the elevation of lipid metabolism is an essential step in colorectal cancer (CRC) development, and analysis of the key lipogenic mediators may lead to identifying the new clinically useful prognostic gene signatures.

Methods

The expression pattern of 61 lipogenic genes was assessed between CRC tumors and matched adjacent normal tissues in a training set (n = 257) with the Mann-Whitney U test. Cox's proportional hazards model and the Kaplan–Meier method were used to identifying a lipogenic-biomarkers signature associated with the prognosis of CRC. The biomarkers signature was then confirmed in two independent validation groups, including a set of 223 CRC samples and an additional set of 203 COAD profiles retrieving from the Cancer Genome Atlas (TCGA).

Results

Five genes, including ACOT8, ACSL5, FASN, HMGCS2, and SCD1, were significantly enhanced in CRC tumors. Using the cutoff value 0.493, the samples were classified into high risk and low risk. The AUC of panel for discriminating of all, early (I-II stages), and advanced CRC (III-IV stages) were 0.8922, 0.8446, and 0.9162 (Training set), along with 0.8800, 0.8205, and 0.7351 (validation set I), and 0.9071, 0.8946, and 0.9107 (Validation set II), respectively. There was a reverse correlation between the high predicted point of panel and worse OS of CRC patients in training set (HR (95% CI): 0.1096 (0.07089–0.1694), P < 0.001), validation set I (HR (95% CI): 0.3350 (0.2116–0.5304), P < 0.001), and validation set II (HR (95% CI): 0.1568 (0.1090–0.2257), P < 0.001).

Conclusion

Our study showed that the panel of ACOT8/ACSL5/FASN/HMGBCS2/SCD1 genes had a better prognostic performance than validated clinical risk scales and is applicable for early detection of CRC and tumor recurrence.

The Emerging Role of Adhesion GPCRs in Cancer

Aberrant expression, function, and mutation of G protein-coupled receptors (GPCRs) and their signaling partners, G proteins, have been well documented in many forms of cancer. These cell surface receptors and their endogenous ligands are implicated in all aspects of cancer including proliferation, angiogenesis, invasion, and metastasis. Adhesion GPCRs (aGPCRs) form the second largest family of GPCRs, most of which are orphan receptors with unknown physiological functions. This is mainly due to our limited insight into their structure, natural ligands, signaling pathways, and tissue expression profiles. Nevertheless, recent studies show that aGPCRs play important roles in cell adhesion to the extracellular matrix and cell-cell communication, processes that are dysregulated in cancer. Emerging evidence suggests that aGPCRs are implicated in migration, proliferation, and survival of tumor cells. We here review the role of aGPCRs in the five most common types of cancer (lung, breast, colorectal, prostate, and gastric) and emphasize the importance of further translational studies in this field.

Adjuvant Therapy for High-Risk Stage II or III Colon Adenocarcinoma: A Propensity Score-Matched, Nationwide, Population-Based Cohort Study

Purpose: To determine the optimal adjuvant chemotherapy regimen for patients with high-risk stage II or III colon adenocarcinoma, we conducted this propensity score-matched, nationwide, population-based cohort study to estimate the effects of adjuvant treatments in high-risk stage II or III colon adenocarcinoma. Patients and Methods: Using propensity score matching, we minimized the confounding effects of sex, age, pathologic stage, tumor location, total chemotherapy cycles, and Charlson comorbidity index scores on adjuvant treatment outcomes in patients with high-risk stage II or III resectable colon adenocarcinoma. We selected the patients from the Taiwan Cancer Registry database and divided them into four groups: Group 1, comprising patients who received surgery alone; group 2, comprising those who received adjuvant fluoropyrimidine alone; group 3, comprising those who received adjuvant oxaliplatin-fluoropyrimidine-leucovorin (FOLFOX); and group 4, comprising those who received adjuvant folinic acid-fluorouracil-irinotecan (FOLFIRI). Results: In both univariate and multivariate Cox regression analyses, the adjusted hazard ratios (aHRs, as well as the 95% confidence intervals (Cis)) for mortality observed for groups 1, 2, and 4 relative to group 3 were 1.55 (1.32 to 1.82), 1.22 (1.05 to 1.43), and 2.97 (2.43 to 3.63), respectively. After a stratified subgroup analysis for high-risk stage II colon adenocarcinoma, we noted that the aHR (95% CI) for mortality for group 2 relative to group 3 was 0.52 (0.30 to 0.89). Conclusions: Adjuvant fluoropyrimidine alone is the most optimal regimen for patients with high-risk stage II colon adenocarcinoma compared with the other adjuvant chemotherapy regimens. Adjuvant FOLFOX can serve as an optimal regimen for patients with pathologic stage III colon adenocarcinoma, regardless of age, sex, or tumor location.

Aberrant Scinderin Expression Correlates With Liver Metastasis and Poor Prognosis in Colorectal Cancer

Many genes and mutations have been reported for colorectal cancer (CRC); however, very few have been associated with colorectal cancer liver metastasis (CRLM). We performed gene expression profiling experiments to identify genetic markers for CRLM and elucidate the molecular mechanisms. Microarray experiments were performed on CRC primary tumor samples with or without liver metastasis (LM) using the Affymetrix U133 plus 2.0 GeneChip Array. A new identified gene-scinderin (SCIN) was overexpressed with synchronous LM at both the RNA level evaluated with quantitative real-time PCR and protein level evaluated with immunohistochemistry and also with short overall survival analyzed with Kaplan-Meier method. With multivariate analysis indicated that SCIN served as an independent poor prognostic predictor for CRC patients. Disease-free survival was also significantly lower in SCIN overexpressing CRC patients with metachronous LM. In addition, SCIN knockdown significantly reduced cell proliferation, induced cell cycle arrest, and promoted the expression of some cell cycle apoptosis-related protein. Moreover, the DIAPH1, STAT3, CDK2, CDK4, and EGFR levels were downregulated, whereas CDKN2B and COL4A1 were upregulated in DLD-1-shSCIN cells by microarray analysis compared with DLD-1 shCon cells. These findings revealed that SCIN may serve as an important predictor of CRLM and poor outcome for CRC patients. SCIN may be a potential therapeutic target in human CRC. However, translation of its roles into clinical practice will require further investigation and additional experimental validation.

High blood sugar levels but not diabetes mellitus significantly enhance oxaliplatin chemoresistance in patients with stage III colorectal cancer receiving adjuvant FOLFOX6 chemotherapy

Background:

The high prevalence of type 2 diabetes mellitus (DM) among patients with colorectal cancer (CRC) is becoming a serious public health concern worldwide. FOLFOX4 chemotherapy is one of the most widely used adjuvant therapies in patients with stage III colon cancer after surgical resection. However, chemotherapy resistance is associated with a poor prognosis. The prognostic impact of high blood sugar levels on oxaliplatin resistance in CRC patients is an unexplored topic.

Methods:

In total, 157 patients with stage III CRC were classified according to their fasting blood sugar level (⩾126 or <126 mg/dl). Clinicopathological features and oxaliplatin chemoresistance/survival outcome of the two groups were compared. In vitro cell proliferation assay was performed through d-(+)-glucose administration.

Results:

Multivariate analysis results revealed that high blood sugar level was a significantly independent prognostic factor of disease-free survival and overall survival (both p < 0.05), but not DM history. After metformin administration, enhanced proliferation of CRC cells (HT-29, HCT-116, SW480, and SW620) with d-(+)-glucose administration could be reversed and oxaliplatin chemosensitivity considerably increased (p < 0.05). Furthermore, phosphorylation of two glycolysis-related target proteins, SMAD3 and MYC, notably increased under high glucose concentration.

Conclusions:

Hyperglycemia can affect clinical outcomes in stage III CRC patients receiving adjuvant chemotherapy, and the mechanism underlying oxaliplatin resistance is possibly associated with increased phosphorylation of SMAD3 and MYC and upregulation of EHMT2 expression.

The effect of MTHFD2 on the proliferation and migration of colorectal cancer cell lines

Purpose

Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is a tetramethylfolate dehydrogenase enzyme involved in folate metabolism. The aim of this study is to determine the effect of MTHFD2 on the proliferation and metastasis of colorectal cancer (CRC).

Patients and methods

MTHFD2 was silenced or overexpressed in CRC cells. qRT-PCR and Western blotting were used to analyze mRNA and protein expression, respectively. The MTT assay and colony forming assay were used to detect cell proliferation and colony formation ability. The cycle and apoptosis changes were detected by flow cytometry. Transwell experiments were used to analyze the migration ability of CRC cells.

Results

The expression of MTHFD2 in 31 kinds of cancers was analyzed by bioinformatics, and MTHFD2 was found highly expressed in various cancer cells including CRC cells. Silencing the expression of MTHFD2 resulted in inhibition of the proliferation of CRC cells, weakening of the migration ability, blocking of the cell cycle in G0/G1-S phase, and promotion of the apoptosis of CRC cells. On the contrary, overexpression of MTHFD2 in CRC cells resulted in enhancement of the proliferation and migration ability, promotion of cell cycle progression and inhibition of cell apoptosis.

Conclusion

MTHFD2 is positively related with colorectal cancer and the MTHFD2 gene is a tumor promoting gene in CRC cells.

Expressions of 10 genes as candidate predictors of recurrence in stage III colon cancer patients receiving adjuvant oxaliplatin-based chemotherapy

Approximately 30% patients with stage III colon cancer (CC) develop local recurrence and/or distant metastasis, even if postoperative adjuvant chemotherapy with oxaliplatin plus 5-fluorouracil and leucovorin (5-FU/LV) has been completed. In the present study, molecular analysis was performed to identify molecular markers of tumor recurrence in patients with stage III CC receiving oxaliplatin-based adjuvant chemotherapy. The FACOS study was conducted as a phase II study to evaluate the safety and efficacy of oxaliplatin-based treatment for stage III CC patients. Of the 132 CC patients enrolled in the present study, gene expression analysis using a microarray was conducted in 51 patients. Of these 51 patients, 6 developed recurrence within 5 years. The topmost 5% genes that showed differential expressions between cases that developed/did not develop recurrence were selected, and a set of predictive molecular markers for recurrence was identified. Of the 34,694 genes in the microarray, 1,734 genes were extracted as topmost 5% genes showing differential expressions between cases with and without recurrence. Among these, 10 genes, includingADH1A, ADH1C, CA12, CHP2, HMGCS2, SNAR-A1, TPI1, MS4A12, PLA2G10 and PTPRO, were identified as markers that could clearly divide patients with and without recurrence. Although several prediction models of tumor recurrence have been reported for CC, the set of 10 genes that the present study identified may be useful to predict the risk of recurrence in stage III CC patients receiving oxaliplatin-based adjuvant chemotherapy. Based on these results, high-risk patients with CC should be carefully observed to detect tumor recurrence during the follow-up period.

Development and validation of an immune-related gene pairs signature in colorectal cancer

Although the outcome of colorectal cancer (CRC) patients has improved significantly with the recent implementation of annual screening programs, reliable prognostic biomarkers are still needed due to the disease heterogeneity. Increasing pieces of evidence revealed an association between immune signature and CRC prognosis. Thus, we aim to build a robust immune-related gene pairs (IRGPs) signature that can estimate prognosis for CRC. Gene expression profiles and clinical information of CRC patients were collected from six public cohorts, divided into training cohort (n = 565) and five independent validation cohorts (n = 572, 290, 90 177 and 68, respectively). Within 1534 immune genes, a 19 IRGPs signature consisting of 36 unique genes was constructed which was significantly associated with the survival. In the validation cohorts, the IRGPs signature significantly stratified patients into high- vs low-risk groups in terms of prognosis across and within subpopulations with early stages disease and was prognostic in univariate and multivariate analyses. Several biological processes, including response to bacterium, were enriched among genes in the IRGPs signature. Macrophage M2 and mast cells were significantly higher in the high-risk risk group compared with the low-risk group. The IRGPs signature achieved a higher accuracy than commercialized multigene signatures for estimation of survival. When integrated with clinical factors such as sex and stage, the composite clinical and IRGPs signature showed improved prognostic accuracy relative to IRGPs signatures alone. In short, we developed a robust IRGPs signature for estimating prognosis in CRC, including early-stage disease, providing new insights into the identification of CRC patients with a high risk of mortality.

Identification of a 13-gene-based classifier as a potential biomarker to predict the effects of fluorouracil-based chemotherapy in colorectal cancer

The aim of the current study was to develop a predictor classifier for response to fluorouracil-based chemotherapy in patients with advanced colorectal cancer (CRC) using microarray gene expression profiles of primary CRC tissues. Using two expression profiles downloaded from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between responders and non-responders to fluorouracil-based chemotherapy were identified. A total of 791 DEGs, including 303 that were upregulated and 488 that were downregulated in responders, were identified. Functional enrichment analysis revealed that the DEGs were primarily involved in ‘cell mitosis’, ‘DNA replication’ and ‘cell cycle’ signaling pathways. Following feature selection using two methods, a random forest classifier for response to fluorouracil-based chemotherapy with 13 DEGs was constructed. The accuracy of the 13-gene classifier was 0.930 in the training set and 0.810 in the validation set. The receiver operating characteristic curve analysis revealed that the area under the curve was 1.000 in the training set and 0.873 in the validation set (P=0.227). The 13-gene-based classifier described in the current study may be used as a potential biomarker to predict the effects of fluorouracil-based chemotherapy in patients with CRC.

Liquid biopsy in breast cancer: A comprehensive review

Breast cancer is the most common cancer among women worldwide. Due to its complexity in nature, effective breast cancer treatment can encounter many challenges. Traditional methods of cancer detection such as tissue biopsy are not comprehensive enough to capture the entire genomic landscape of breast tumors. However, with the introduction of novel techniques, the application of liquid biopsy has been enhanced, enabling the improvement of various aspects of breast cancer management including early diagnosis and screening, prediction of prognosis, early detection of relapse, serial sampling and efficient longitudinal monitoring of disease progress and response to treatment. Various components of tumor cells released into the blood circulation can be analyzed in liquid biopsy sampling, some of which include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free RNA, tumor-educated platelets and exosomes. These components can be utilized for different purposes. As an example, ctDNA can be sequenced for genetic profiling of the tumors to enhance individualized treatment and longitudinal screening. CTC plasma count analysis or ctDNA detection after curative tumor resection surgery could facilitate early detection of minimal residual disease, aiding in the initiation of adjuvant therapy to prevent recurrence. Furthermore, CTC plasma count can be assessed to determine the stage and prognosis of breast cancer. In this review, we discuss the advantages and limitations of the various components of liquid biopsy used in breast cancer diagnosis and will expand on aspects that require further focus in future research.

Identification of tissue-specific tumor biomarker using different optimization algorithms

BACKGROUND

Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of genes.

OBJECTIVE

In this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid) METHODS: Here, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the optimization algorithms Artificial Bee Colony (ABC), Ant Colony Optimization, Differential Evolution, and Particle Swarm Optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machine RESULTS: Cancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statistical test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signature CONCLUSION: The current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin.

Gene pair based prognostic signature for colorectal colon cancer

BACKGROUND

The identification of high-risk colorectal cancer (CRC) patient is key to individualized treatment after surgery and reliable prognostic biomarkers are needed identifying high-risk CRC patients.

METHODS

We developed a gene pair based prognostic signature that could can the prognosis risk in patients with CRC. This study retrospectively analyzed 4 public CRC datasets, and 1123 patients with CRC were divided into a training cohort (n = 300) and 3 independent validation cohorts (n = 507, 226, and 90 patients).

RESULTS

A signature of 9 prognosis-related gene pairs (PRGPs) consisting of 17 unique genes was constructed. Then, a PRGP index (PRGPI) was constructed and divided patients into high- and low-risk groups according to the signature score. Patients in the high-risk group showed a poorer relapse-free survival than the low-risk group in both the training cohort [hazard ratio (HR) range, 4.6, 95% confidence interval (95% CI), 2.55-8.32; P < .0001] and meta-validation set (hazard ratio range, 4.09, 95% CI, 1.99-8.39; P < .0001). The PRGPI signature achieved a higher accuracy [mean concordance index (C-index): 0.6∼0.74] than a commercialized molecular signature (mean C-index, 0.48∼0.56) for estimation of relapse-free survival in comparable validation sets.

CONCLUSION

The gene pair based prognostic signature is a promising biomarker for estimating relapse-free survival of CRC.

Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequence

This study aimed to identify differentially expressed genes (DEGs) related to the colorectal normal mucosa-adenoma-carcinoma sequence using bioinformatics analysis. Raw data files were downloaded from Gene Expression Omnibus (GEO) and underwent quality assessment and preprocessing. DEGs were analyzed by the limma package in R software (R version 3.3.2). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the DAVID online tool. In a comparison of colorectal adenoma (n = 20) and colorectal cancer (CRC) stage I (n = 31), II (n = 38), III (n = 45), and IV (n = 62) with normal colorectal mucosa (n = 19), we identified 336 common DEGs. Among them, seven DEGs were associated with patient prognosis. Five (HEPACAM2, ITLN1, LGALS2, MUC12, and NXPE1) of the seven genes presented a sequentially descending trend in expression with tumor progression. In contrast, TIMP1 showed a sequentially ascending trend. GCG was constantly downregulated compared with the gene expression level in normal mucosa. The significantly enriched GO terms included extracellular region, extracellular space, protein binding, and carbohydrate binding. The KEGG categories included HIF-1 signaling pathway, insulin secretion, and glucagon signaling pathway. We discovered seven DEGs in the normal colorectal mucosa-adenoma-carcinoma sequence that was associated with CRC patient prognosis. Monitoring changes in these gene expression levels may be a strategy to assess disease progression, evaluate treatment efficacy, and predict prognosis.

A comprehensive meta-analysis of genetic associations between five key SNPs and colorectal cancer risk

Genome-wide association studies (GWAS) on colorectal cancer (CRC) have identified dozens of single nucleotide polymorphisms (SNPs) in more than 19 independent loci associated with CRC. Due to the heterogeneity of the studied subjects and the contrary results, it is challenging to verify the certainty of the association between these loci and CRC.We conducted a critical review of the published studies of SNPs associated with CRC. Five most frequently reported SNPs, which are rs6983267/8q24.21, rs4939827/18q21.1, rs10795668/10p14, rs4444235/14q22.2 and rs4779584/ 15q13.3, were selected for the current study from the qualified studies. Then meta-analyses based on larger sample sizes with average of 33,000 CRC cases and 34,000 controls were performed to assess the association between SNPs and CRC risk. Heterogeneity among studies and publication bias were assessed by the χ2-based Q statistic test Begg's funnel plot or Egger's test, respectively.Our meta-analysis confirmed significant associations of the five SNPs with CRC risk under different genetic models. Two risk variants at rs6983267 {Odds Ratio (OR) 1.388, 95% Confidence Interval (CI) 1.180-1.8633} and rs10795668 (OR 1.323, 95% CI 1.062-1.648) had the highest ORs in homogeneous model. While ORs of the other three variants at rs4939827 {OR 1.298, 95% CI 1.135-1.483}, rs4779584 (OR 1.261, 95% CI 1.146-1.386) and rs4444235 (OR 1.160, 95% CI 1.106-1.216) were also statistically significant. Sensitivity analyses and publication bias assessment indicated the robust stability and reliability of the results.

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 network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer

Background

The clinical benefit of adjuvant chemotherapy for stage II colorectal cancer (CRC) is controversial. This study aimed to explore novel gene signature to predict outcome benefit of postoperative 5-Fu-based therapy in stage II CRC.

Methods

Gene-expression profiles of stage II CRCs from two datasets with 5-Fu-based adjuvant chemotherapy (training dataset, n = 212; validation dataset, n = 85) were analyzed to identify the indicator. A systemic approach by integrating gene-expression and protein-protein interaction (PPI) network was implemented to develop the predictive signature. Kaplan-Meier curves and Cox proportional hazards model were used to determine the survival benefit of adjuvant chemotherapy. Experiments with shRNA knock-down were carried out to confirm the signature identified in this study.

Results

In the training dataset, we identified 44 PPI sub-modules, by which we separate patients into two clusters (1 and 2) having different chemotherapeutic benefit. A predictor of 11 PPI sub-modules (11-PPI-Mod) was established to discriminate the two sub-groups, with an overall accuracy of 90.1%. This signature was independently validated in an external validation dataset. Kaplan-Meier curves showed an improved outcome for patients who received adjuvant chemotherapy in Cluster 1 sub-group, but even worse survival for those in Cluster 2 sub-group. Similar results were found in both the training and the validation dataset. Multivariate Cox regression revealed an interaction effect between 11-PPI-Mod signature and adjuvant therapy treatment in the training dataset (RFS, p = 0.007; OS, p = 0.006) and the validation dataset (RFS, p = 0.002). From the signature, we found that PTGES gene was up-regulated in CRC cells which were more resistant to 5-Fu. Knock-down of PTGES indicated a growth inhibition and up-regulation of apoptotic markers induced by 5-Fu in CRC cells.

Conclusions

Only a small proportion of stage II CRC patients could benefit from adjuvant therapy. The 11-PPI-Mod as a potential predictor could be helpful to distinguish this sub-group with favorable outcome.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3821-4) contains supplementary material, which is available to authorized users.

microRNA-1297 Inhibits the Growth and Metastasis of Colorectal Cancer by Suppressing Cyclin D2 Expression

microRNAs (miR) can potentially be used for categorizing the various subtypes of colorectal cancer (CRC) and predicting a patient's response to treatment with traditional anti-CRC therapies. We investigated how miR-1297 and its potential target molecule cyclin D2 (CCND2) might affect the progression of CRC. Thirty-two pairs of CRC specimens and corresponding samples of para-tumor tissue were collected and examined for their levels of miR-1297 and CCND2 expression. We also examined miR-1297 and CCND2 expression in cultured SW480 cells. The effects of modulated levels of miR-1297 and CCND2 on cell viability, anchorage-independent growth ability, proliferation, apoptosis, cell cycle distribution, migration, and invasion were detected using specific techniques. The possible regulatory effect of miR-1297 on CCND2 was investigated using dual luciferase assays. Our results showed that miR-1297 expression was downregulated in clinical CRC specimens, and such downregulation was associated with upregulated levels of CCND2 expression. Upregulation of miR-1297 and downregulation of CCND2 reduced the proliferation and metastasis potential of SW480 cells, but did not affect the apoptotic process. In addition, miR-1297 regulated CCND2 function by directly binding to the promoter sequence of the CCND2 gene, which would block CCND2-related signaling at the transcription level. Our findings validate the anti-CRC function of miR-1297 and pro-CRC function of CCND2. Our findings may assist in developing miR-based therapies against CRC.

Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Cancer therapy agents have been used extensively as cytotoxic drugs against tissue or organ of a specific type of cancer. With the better understanding of molecular mechanisms underlying carcinogenesis and cellular events during cancer progression and metastasis, it is now possible to use targeted therapy for these molecular events. Targeted therapy is able to identify cancer patients with dissimilar genetic defects at cellular level for the same cancer type and consequently requires individualized approach for treatment. Cancer therapy begins to shift steadily from the traditional approach of “one regimen for all patients” to a more individualized approach, through which each patient will be treated specifically according to their specific genetic defects. Personalized medicine accordingly requires identification of indicators or markers that guide in the decision making of such therapy to the chosen patients for more effective therapy. Cancer biomarkers are frequently used in clinical practice for diagnosis and prognosis, as well as identification of responsive patients and prediction of treatment response of cancer patient. The rapid breakthrough and development of microarray and sequencing technologies is probably the main tool for paving the way toward “individualized biomarker-driven cancer therapy” or “personalized medicine”. In this review, we aim to provide an updated knowledge and overview of the current landscape of cancer biomarkers and their role in personalized medicine, emphasizing the impact of genomics on the implementation of new potential targeted therapies and development of novel cancer biomarkers in improving the outcome of cancer therapy.

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.

Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis

BACKGROUND

Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology.

RESULTS

Weighted gene co-expression network analysis, a powerful technique used to extract co-expressed gene networks from mRNA expressions, was conducted to identify 11 co-regulated modules in a discovery dataset with 461 patients. A transcriptional module enriched in cell cycle processes was correlated with the recurrence-free survival of the CC patients in the discovery (HR = 0.59; 95% CI = 0.42-0.81) and validation (HR = 0.51; 95% CI = 0.25-1.05) datasets. The prognostic potential of the hub gene Centromere Protein-A (CENPA) was also identified and the upregulation of this gene was associated with good survival. Another cell cycle phase-related gene module was correlated with the survival of the patients with a KRAS mutation CC subtype. The downregulation of several genes, including those found in this co-expression module, such as cyclin-dependent kinase 1 (CDK1), was associated with poor survival.

CONCLUSION

Network-based approaches may facilitate the discovery of biomarkers for the prognosis of a subset of patients with stage II or III CC, these approaches may also help direct personalised therapies.

High blood sugar levels significantly impact the prognosis of colorectal cancer patients through down-regulation of microRNA-16 by targeting Myb and VEGFR2

The high prevalence of type 2 diabetes mellitus in colorectal cancer patients is a crucial public health issue worldwide. The deregulation of microRNAs has been shown to be associated with the progression of CRC; however, the effects of high blood sugar levels on miR deregulation and, in turn, CRC remain unexplored. In this study, 520 CRC patients were classified into two groups according to their blood sugar levels (≧110 or <110 mg/dL). Clinicopathologic features, clinical outcomes, and serum miR-16 levels of the two groups were then analyzed, while cell cycles, cell proliferation, migration, and cellular miR-16 expression were investigated via D-(+)-glucose administration. Additionally, the target genes of miR-16 were identified. Through multivariate analysis, both the disease-free survival and overall survival of the CRC patients were found to be associated with the UICC stage, perineural invasion, and blood glucose levels (P < 0.05). Serum miR-16 levels were significantly lower in the high blood glucose patients than in the normal blood glucose patients (P = 0.0329). With D-(+)-glucose administration, the proliferation and migration of CRC cells in vitro increased remarkably (P < 0.05), while their accumulation in the G1 phase decreased significantly. Cellular miR-16 expression was suppressed by D-(+)-glucose administration. The expression levels of two target genes, Myb and VEGFR2, were affected significantly by miR-16, while glucose administration inhibited miR-16 expression and enhanced tumor cell proliferation and migration. Hyperglycemia can impact the clinical outcomes of CRC patients, likely by inhibiting miR-16 expression and the expression of its downstream genes Myb and VEGFR2.

Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death. Most cases of CRC are detected in Western countries, with its incidence increasing year by year. The probability of suffering from colorectal cancer is about 4%–5% and the risk for developing CRC is associated with personal features or habits such as age, chronic disease history and lifestyle. In this context, the gut microbiota has a relevant role, and dysbiosis situations can induce colonic carcinogenesis through a chronic inflammation mechanism. Some of the bacteria responsible for this multiphase process include Fusobacterium spp, Bacteroides fragilis and enteropathogenic Escherichia coli. CRC is caused by mutations that target oncogenes, tumour suppressor genes and genes related to DNA repair mechanisms. Depending on the origin of the mutation, colorectal carcinomas can be classified as sporadic (70%); inherited (5%) and familial (25%). The pathogenic mechanisms leading to this situation can be included in three types, namely chromosomal instability (CIN), microsatellite instability (MSI) and CpG island methylator phenotype (CIMP). Within these types of CRC, common mutations, chromosomal changes and translocations have been reported to affect important pathways (WNT, MAPK/PI3K, TGF-β, TP53), and mutations; in particular, genes such as c-MYC, KRAS, BRAF, PIK3CA, PTEN, SMAD2 and SMAD4 can be used as predictive markers for patient outcome. In addition to gene mutations, alterations in ncRNAs, such as lncRNA or miRNA, can also contribute to different steps of the carcinogenesis process and have a predictive value when used as biomarkers. In consequence, different panels of genes and mRNA are being developed to improve prognosis and treatment selection. The choice of first-line treatment in CRC follows a multimodal approach based on tumour-related characteristics and usually comprises surgical resection followed by chemotherapy combined with monoclonal antibodies or proteins against vascular endothelial growth factor (VEGF) and epidermal growth receptor (EGFR). Besides traditional chemotherapy, alternative therapies (such as agarose tumour macrobeads, anti-inflammatory drugs, probiotics, and gold-based drugs) are currently being studied to increase treatment effectiveness and reduce side effects.

TRIM37 promotes epithelial‑mesenchymal transition in colorectal cancer

There is substantial research on the oncogenic role of tripartite motif containing 37 (TRIM37); however, its importance in colorectal cancer (CRC) remains to be elucidated. The present study used reverse transcription-quantitative polymerase chain reaction, immunohistochemistry and western blotting to detect the expression level of TRIM37 in CRC. The importance of TRIM37 in cell proliferation, invasion and metastasis of CRC were investigated through overexpressing or knocking-down of TRIM37 in CRC cell lines, to observe its function. The present study revealed that TRIM37 was overexpressed in human CRC tissues. High TRIM37 expression resulted in increased CRC proliferation, migration and invasion. Mechanistically, it was confirmed that TRIM37 enhanced invasion and metastasis of CRC via the epithelial-mesenchymal transition pathway. In conclusion, the present study suggested that TRIM3 may contribute to CRC and act as a potential therapeutic target for CRC treatment.

A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM

OBJECTIVE

To screen the gene signature for distinguishing patients with high risks from those with low-risks for colon cancer recurrence and predicting their prognosis.

METHODS

Five microarray datasets of colon cancer samples were collected from Gene Expression Omnibus database and one was obtained from The Cancer Genome Atlas (TCGA). After preprocessing, data in GSE17537 were analyzed using the Linear Models for Microarray data (LIMMA) method to identify the differentially expressed genes (DEGs). The DEGs further underwent PPI network-based neighborhood scoring and support vector machine (SVM) analyses to screen the feature genes associated with recurrence and prognosis, which were then validated by four datasets GSE38832, GSE17538, GSE28814 and TCGA using SVM and Cox regression analyses.

RESULTS

A total of 1207 genes were identified as DEGs between recurrence and no-recurrence samples, including 726 downregulated and 481 upregulated genes. Using SVM analysis and five gene expression profile data confirmation, a 15-gene signature (HES5, ZNF417, GLRA2, OR8D2, HOXA7, FABP6, MUSK, HTR6, GRIP2, KLRK1, VEGFA, AKAP12, RHEB, NCRNA00152 and PMEPA1) were identified as a predictor of recurrence risk and prognosis for colon cancer patients.

CONCLUSION

Our identified 15-gene signature may be useful to classify colon cancer patients with different prognosis and some genes in this signature may represent new therapeutic targets.

Prognostic microRNAs modulate the RHO adhesion pathway: A potential therapeutic target in undifferentiated pleomorphic sarcomas

A common and aggressive subtype of soft-tissue sarcoma, undifferentiated pleomorphic sarcoma (UPS) was examined to determine the role of micro-RNAs (miRNAs) in modulating distant metastasis. Following histopathologic review, 110 fresh frozen clinically annotated UPS samples were divided into two independent cohorts for Training (42 patients), and Validation (68 patients) analyses. Global miRNA profiling on the Training Set and functional analysis in vitro suggested that miRNA-138 and its downstream RHO-ROCK cell adhesion pathway was a convergent target of miRNAs associated with the development of metastasis. A six-miRNA signature set prognostic of distant metastasis-free survival (DMFS) was developed from Training Set miRNA expression values. Using the six-miRNA signature, patients were successfully categorized into high- and low-risk groups for DMFS in an independent Validation Set, with a hazard ratio (HR) of 2.25 (p = 0.048). After adjusting for other known prognostic variables such as age, gender, tumor grade, size, depth, and treatment with radiotherapy, the six-miRNA signature retained prognostic value with a HR of 3.46 (p < 0.001). A prognostic miRNA biomarker for clinical validation was thus identified along with a functional pathway that modulates UPS metastatic phenotype.

Understanding molecular markers in recurrent oral squamous cell carcinoma treated with chemoradiation

Introduction

Oral cancer accounts for approximately 2.1% of all cancers worldwide. In India, oral squamous cell carcinoma (OSCC) is the most common cancer with half a million new cases diagnosed every year. More than 50% of patients eventually develop local recurrence or metastasis usually within the first 2-years following completion of treatment. It is beneficial to analyze the prognostic significance of Cyclin D1, p53 and EGFR which are critical mediators in the pathogenesis of OSCC. The objective of this study was to assess the association of expression of these markers with recurrence and pattern of recurrence in OSCC patients undergoing chemoradiation.

Materials and Methods

A Total 290 OSCC cases of locally advanced stage (III, IV) oral cancer with World Health Organization (W.H.O.) performance status of grade 0/1 in the year 2009–2012 were enrolled in the study. Treatment response was assessed according to W.H.O. criteria. Cyclin D1, EGFR and p53 expression in tumor tissue was estimated by immunohistochemical (IHC) method and quantified as percentage positive nuclei.

Results

During the 2-years follow up, 56 (19.3%) patients recurred, out of which, 47 (83.9%) were locoregional and 9 (16.1%) distant sites. On correlating, χ² test showed significant (P < 0.05 or P < 0.01 or P < 0.001) association of marker expressions (Cyclin D1, EGFR and p53) with recurrence. The strong positive expressions of all three markers showed significant association with early time of recurrence. The multivariate logistic regression analysis showed significant (P < 0.05 or P < 0.01 or P < 0.001) association of recurrence with primary site, differentiation, Cyclin D1 and p53 expressions indicating these as an independent predictors of recurrence in OSCC. The Cyclin D1, EGFR and p53 expressions also showed significant (P < 0.001) poor survivals (OS, DFS and RFS) in patients with positive/strong positive expressions than negative expression suggesting their prognosis in OSCC.

Conclusion

Our results signifies that tumors over expressing Cyclin D1, EGFR and p53 are resistant to chemoradiation and are associated with increased risk of locoregional recurrence and metastasis in OSCC patients undergoing chemoradiation.

Molecular Biomarkers of Colorectal Cancer and Cancer Disparities: Current Status and Perspective

This review provides updates on the efforts for the development of prognostic and predictive markers in colorectal cancer based on the race/ethnicity of patients. Since the clinical consequences of genetic and molecular alterations differ with patient race and ethnicity, the usefulness of these molecular alterations as biomarkers needs to be evaluated in different racial/ethnic groups. To accomplish personalized patient care, a combined analysis of multiple molecular alterations in DNA, RNA, microRNAs (miRNAs), metabolites, and proteins in a single test is required to assess disease status in a precise way. Therefore, a special emphasis is placed on issues related to utility of recently identified genetic and molecular alterations in genes, miRNAs, and various "-omes" (e.g., proteomes, kinomes, metabolomes, exomes, methylomes) as candidate molecular markers to determine cancer progression (disease recurrence/relapse and metastasis) and to assess the efficacy of therapy in colorectal cancer in relation to patient race and ethnicity. This review will be useful for oncologists, pathologists, and basic and translational researchers.

Expression and clinicopathological significance of the lncRNA HOXA11-AS in colorectal cancer

HOXA11 antisense RNA (HOXA11-AS) is a long non-coding RNA (lncRNA) that is important in determining cancer progression. HOXA11-AS was recently identified as a novel biomarker in lung cancer progression. However, its role in colorectal cancer (CRC) remains poorly understood. The present study aimed to analyze lncRNA HOXA11-AS expression in CRC and investigate a possible association between HOXA11-AS and clinicopathological factors. HOXA11-AS expression was examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in 84 CRC tissues and adjacent non-cancerous tissues, in addition to 3 CRC cell lines and 1 human normal colorectal cell line. The results demonstrated that HOXA11-AS expression was decreased in the CRC tissues and cell lines compared with that of the controls (P<0.05). Clinicopathological analysis indicated that low HOXA11-AS expression was significantly correlated with tumor size, advanced tumor-node-metastasis stage, lymph node metastasis and carcinoembryonic antigen level of patients with CRC (P<0.05). Furthermore, the areas under the curve (AUC) were 0.613 and 0.628 for HOXA11-AS, indicating that the lncRNA is able to distinguish CRC tissue from non-cancerous tissue, and CRC tissue with lymph node metastasis from CRC without lymph node metastasis. Therefore, HOXA11-AS may function as a potential biomarker and target for novel therapeutic strategies to treat CRC.

Evaluation of frozen tissue-derived prognostic gene expression signatures in FFPE colorectal cancer samples

Defining molecular features that can predict the recurrence of colorectal cancer (CRC) for stage II-III patients remains challenging in cancer research. Most available clinical samples are Formalin-Fixed, Paraffin-Embedded (FFPE). NanoString nCounter® and Affymetrix GeneChip® Human Transcriptome Array 2.0 (HTA) are the two platforms marketed for high-throughput gene expression profiling for FFPE samples. In this study, to evaluate the gene expression of frozen tissue-derived prognostic signatures in FFPE CRC samples, we evaluated the expression of 516 genes from published frozen tissue-derived prognostic signatures in 42 FFPE CRC samples measured by both platforms. Based on HTA platform-derived data, we identified both gene (99 individual genes, FDR < 0.05) and gene set (four of the six reported multi-gene signatures with sufficient information for evaluation, P < 0.05) expression differences associated with survival outcomes. Using nCounter platform-derived data, one of the six multi-gene signatures (P < 0.05) but no individual gene was associated with survival outcomes. Our study indicated that sufficiently high quality RNA could be obtained from FFPE tumor tissues to detect frozen tissue-derived prognostic gene expression signatures for CRC patients.

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

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