Gene expression profiling of colorectal tumors and normal mucosa by microarrays meta-analysis using prediction analysis of microarray, artificial neural network, classification, and regression trees

Guanylate cyclase-C Signaling Axis as a theragnostic target in colorectal cancer: a systematic review of literature

Introduction

Colorectal cancer (CRC) is a devastating disease that affects millions of people worldwide. Recent research has highlighted the crucial role of the guanylate cyclase-C (GC-C) signaling axis in CRC, from the early stages of tumorigenesis to disease progression. GC-C is activated by endogenous peptides guanylin (GU) and uroguanylin (UG), which are critical in maintaining intestinal fluid homeostasis. However, it has been found that these peptides may also contribute to the development of CRC. This systematic review focuses on the latest research on the GC-C signaling axis in CRC.

Methods

According to the aim of the study, a systematic literature search was conducted on Medline and PubMed databases. Ultimately, a total of 40 articles were gathered for the systematic review.

Results

Our systematic literature search revealed that alterations in GC-C signaling compartments in CRC tissue have demonstrated potential as diagnostic, prognostic, and therapeutic markers. This research highlights a potential treatment for CRC by targeting the GC-C signaling axis. Promising results from recent studies have explored the use of this signaling axis to develop new vaccines and chimeric antigen receptors that may be used in future clinical trials.

Conclusion

The findings presented in this review provide compelling evidence that targeting the GC-C signaling axis may be an advantageous approach for treating CRC.

Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms

Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank p-value of 0.044 for the final gene signatures. The analysis of tumor immunocyte infiltration showed a weak correlation with the expression of the gene signatures. CA2, CA7, and ITM2C can serve as gene signatures for the early detection of CRC and may provide valuable information for prognostic and therapeutic decision making. Further research is needed to fully understand the potential of these genes in the context of CRC.

5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Diagnostic and Therapeutic Perspectives Associated to Cobalamin-Dependent Metabolism and Transcobalamins’ Synthesis in Solid Cancers

Cobalamin or vitamin B12 (B12) is a cofactor for methionine synthase and methylmalonyl-CoA mutase, two enzymes implicated in key pathways for cell proliferation: methylation, purine synthesis, succinylation and ATP production. Ensuring these functions in cancer cells therefore requires important cobalamin needs and its uptake through the transcobalamin II receptor (TCII-R). Thus, both the TCII-R and the cobalamin-dependent metabolic pathways constitute promising therapeutic targets to inhibit cancer development. However, the link between cobalamin and solid cancers is not limited to cellular metabolism, as it also involves the circulating transcobalamins I and II (TCI or haptocorrin and TCII) carrier proteins, encoded by TCN1 and TCN2, respectively. In this respect, elevations of B12, TCI and TCII concentrations in plasma are associated with cancer onset and relapse, and with the presence of metastases and worse prognosis. In addition, TCN1 and TCN2 overexpressions are associated with chemoresistance and a proliferative phenotype, respectively. Here we review the involvement of cobalamin and transcobalamins in cancer diagnosis and prognosis, and as potential therapeutic targets. We further detail the relationship between cobalamin-dependent metabolic pathways in cancer cells and the transcobalamins’ abundancies in plasma and tumors, to ultimately hypothesize screening and therapeutic strategies linking these aspects.

Recurrent HBV Integration Targets as Potential Drivers in Hepatocellular Carcinoma

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC), frequently with HBV integrating into the host genome. HBV integration, found in 85% of HBV-associated HCC (HBV–HCC) tissue samples, has been suggested to be oncogenic. Here, we investigated the potential of HBV–HCC driver identification via the characterization of recurrently targeted genes (RTGs). A total of 18,596 HBV integration sites from our in-house study and others were analyzed. RTGs were identified by applying three criteria: at least two HCC subjects, reported by at least two studies, and the number of reporting studies. A total of 396 RTGs were identified. Among the 28 most frequent RTGs, defined as affected in at least 10 HCC patients, 23 (82%) were associated with carcinogenesis and 5 (18%) had no known function. Available breakpoint positions from the three most frequent RTGs, TERT, MLL4/KMT2B, and PLEKHG4B, were analyzed. Mutual exclusivity of TERT promoter mutation and HBV integration into TERT was observed. We present an RTG consensus through comprehensive analysis to enable the potential identification and discovery of HCC drivers for drug development and disease management.

Identification of Key mRNAs, miRNAs, and mRNA-miRNA Network Involved in Papillary Thyroid Carcinoma

Background:

In the past decades, the incidence of thyroid cancer (TC) has been gradually increasing, owing to the widespread use of ultrasound scanning devices. However, the key mRNAs, miRNAs, and mRNA-miRNA network in papillary thyroid carcinoma (PTC) has not been fully understood.

Material and Methods:

In this study, multiple bioinformatics methods were employed, including differential expression analysis, gene set enrichment analysis, and miRNA-mRNA interaction network construction.

Results:

First, we investigated the key miRNAs that regulated significantly more differentially expressed genes based on GSEA method. Second, we searched for the key miRNAs based on the mRNA-miRNA interaction subnetwork involved in PTC. We identified hsa-mir-1275, hsa-mir-1291, hsa-mir-206 and hsa-mir-375 as the key miRNAs involved in PTC pathogenesis.

Conclusion:

The integrated analysis of the gene and miRNA expression data not only identified key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma, but also improved our understanding of the pathogenesis of PTC.

Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

Background

Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.

Methods

GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the “least absolute shrinkage and selection operator” (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.

Results

Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946.

Conclusions

These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.

ETV4 plays a role on the primary events during the adenoma-adenocarcinoma progression in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common cancers worldwide; it is the fourth leading cause of death in the world and the third in Brazil. Mutations in the APC, DCC, KRAS and TP53 genes have been associated with the progression of sporadic CRC, occurring at defined pathological stages of the tumor progression and consequently modulating several genes in the corresponding signaling pathways. Therefore, the identification of gene signatures that occur at each stage during the CRC progression is critical and can present an impact on the diagnosis and prognosis of the patient. In this study, our main goal was to determine these signatures, by evaluating the gene expression of paired colorectal adenoma and adenocarcinoma samples to identify novel genetic markers in association to the adenoma-adenocarcinoma stage transition.

METHODS

Ten paired adenoma and adenocarcinoma colorectal samples were subjected to microarray gene expression analysis. In addition, mutations in APC, KRAS and TP53 genes were investigated by DNA sequencing in paired samples of adenoma, adenocarcinoma, normal tissue, and peripheral blood from ten patients.

RESULTS

Gene expression analysis revealed a signature of 689 differentially expressed genes (DEG) (fold-change> 2, p< 0.05), between the adenoma and adenocarcinoma paired samples analyzed. Gene pathway analysis using the 689 DEG identified important cancer pathways such as remodeling of the extracellular matrix and epithelial-mesenchymal transition. Among these DEG, the ETV4 stood out as one of the most expressed in the adenocarcinoma samples, further confirmed in the adenocarcinoma set of samples from the TCGA database. Subsequent in vitro siRNA assays against ETV4 resulted in the decrease of cell proliferation, colony formation and cell migration in the HT29 and SW480 colorectal cell lines. DNA sequencing analysis revealed KRAS and TP53 gene pathogenic mutations, exclusively in the adenocarcinomas samples.

CONCLUSION

Our study identified a set of genes with high potential to be used as biomarkers in CRC, with a special emphasis on the ETV4 gene, which demonstrated involvement in proliferation and migration.

Increased Expression of VANGL1 is Predictive of Lymph Node Metastasis in Colorectal Cancer: Results from a 20-Gene Expression Signature

This study aimed at building a prognostic signature based on a candidate gene panel whose expression may be associated with lymph node metastasis (LNM), thus potentially able to predict colorectal cancer (CRC) progression and patient survival. The mRNA expression levels of 20 candidate genes were evaluated by RT-qPCR in cancer and normal mucosa formalin-fixed paraffin-embedded (FFPE) tissues of CRC patients. Receiver operating characteristic curves were used to evaluate the prognosis performance of our model by calculating the area under the curve (AUC) values corresponding to stage and metastasis. A total of 100 FFPE primary tumor tissues from stage I-IV CRC patients were collected and analyzed. Among the 20 candidate genes we studied, only the expression levels of VANGL1 significantly varied between patients with and without LNMs (p = 0.02). Additionally, the AUC value of the 20-gene panel was found to have the highest predictive performance (i.e., AUC = 79.84%) for LNMs compared with that of two subpanels including 5 and 10 genes. According to our results, VANGL1 gene expression levels are able to estimate LNMs in different stages of CRC. After a proper validation in a wider case series, the evaluation of VANGL1 gene expression and that of the 20-gene panel signature could help in the future in the prediction of CRC progression.

Clinicopathological Analysis and Prognostic Assessment of Transcobalamin I (TCN1) in Patients with Colorectal Tumors

BACKGROUND Colorectal cancer (CRC) is one of the most common malignancies worldwide. Overall survival (OS) of patients is largely dependent on disease stage at diagnosis and/or surgical resection. TCN1 mainly encodes the vitamin B12 transporter, transcobalamin. Early studies show that TCN1 is a marker of CRC progression, but the impact of TCN1 on survival is unclear. MATERIAL AND METHODS We reviewed and analyzed colorectal tumor records, summarized the clinicopathological data, performed immunohistochemical detection of TCN1 again, and semi-quantitatively analyzed protein expression in tumor tissue, non-tumor tissue, and lymph nodes. We followed up patients for 5-year survival. RESULTS Of 123 patients, 60 (48.7%) had a strong TCN1 immunohistochemical reaction, 36 (29.3%) had a moderate immune response, and 27 (22.0%) had weak expression. The level of immunohistochemical reactivity of TCN1 was correlated with the degree of histological differentiation (H (2.92)=4.976; P=0.083). Survival analysis showed that OS in patients with low TCN1 expression was significantly longer than that in the medium and high TCN1 expression groups (P=0.045). Five-year OS in patients with low, medium, and high TCN1 expression was 88.9%, 50.0%, and 40.0%, respectively. In univariate analysis, TCN1 immune expression was significantly correlated with the 5-year survival rate. CONCLUSIONS Although independent risk factors affecting survival of patients with CRC are age, serum CA125, CA19-9, lymph node metastasis, and nerve invasion, negative factors affecting overall 5-year survival in TCN1 should not be ignored, because its high expression suggests a worse clinical prognosis.

High expression of TCN1 is a negative prognostic biomarker and can predict neoadjuvant chemosensitivity of colon cancer

Transcobalamin (TCN1) is a vitamin B12 (cobalamin)-binding protein that regulates cobalamin homeostasis. Recent studies and bioinformatic analyses have found that TCN1 is highly expressed in cancer tissues and is associated with tumour aggressiveness and poor prognosis. The present study aimed to detect TCN1 as a novel biomarker for prognosis and chemosensitivity of colon cancer. Next-generation sequencing showed that TCN1 was one of several upregulated mRNAs in colon cancer, which was verified by further bioinformatics analyses. Western blotting (n = 9) and quantitative real time polymerase chain reaction (qRT-PCR, n = 30) revealed that TCN1 was highly expressed in colon cancer tissues at both the protein and mRNA level. A total of 194 cases of colon cancer were examined by immunohistochemistry and revealed that TCN1 expression level was related to advanced stages (P < 0.005). Kaplan-Meier analysis verified that patients with lower TCN1 expression usually had longer overall survival (P = 0.008). In addition, TCN1 was highly expressed in pulmonary metastatic tumour tissues (n = 37, P = 0.025) and exhibited higher levels in right-sided colon cancer than in left-sided colon cancer (P = 0.029). TCN1 expression in specimens that had received neoadjuvant chemotherapy decreased compared with that in colonoscopy biopsy tissues (n = 42, P = 0.009). Further bioinformatics analyses verified that apoptosis pathways might have a role in high TCN1 expression. All the studies revealed that TCN1 expression in colon cancer was significantly associated with malignant biological behaviour. Therefore, TCN1 could be used as a novel biomarker for colon cancer aggressiveness and prognosis and might also be a potential biomarker for predicting neoadjuvant chemosensitivity.

Genome wide meta-analysis of cDNA datasets reveals new target gene signatures of colorectal cancer based on systems biology approach

Background

Colorectal cancer is known to be the most common type of cancer worldwide with high disease-related mortality. It is the third most common cancer in men and women and is the second major cause of death globally due to cancer. It is a complicated and fatal disease comprising of a group of molecular heterogeneous disorders.

Results

This study identifies the potential biomarkers of CRC through differentially expressed analysis, system biology, and proteomic analysis. Ten publicly available microarray datasets were analyzed and seven potential biomarkers were identified from the list of differentially expressed genes having a p value < 0.05. The expression profiling and the functional enrichment analysis revealed the role of these genes in cell communication, signal transduction, and immune response. The protein-protein interaction showed the functional association of the source genes (CTNNB1, NNMT, PTCH1, CALD1, CXCL14, CXCL8, and TNFAIP3) with the target proteins, such as AXIN, MAPK, IL6, STAT, APC, GSK3B, and SHH.

Conclusion

The integrated pathway analysis indicated the role of these genes in important physiological responses, such as cell cycle regulation, WNT, hedgehog, MAPK, and calcium signaling pathways during colorectal cancer. These pathways are involved in cell proliferation, chemotaxis, cellular growth, differentiation, tissue patterning, and cytokine production. The study shows the regulatory role of these genes in colorectal cancer and the pathways that can be effected after the dysregulation of these genes.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing

The previous cancer studies were difficult to reproduce since the tumor tissues were analyzed directly. But the tumor tissues were actually a mixture of different cancer cells. The transcriptome of single-cell was much robust than the transcriptome of a mixed tissue. The single-cell transcriptome had much smaller variance. In this study, we analyzed the single-cell transcriptome of 272 colorectal cancer (CRC) epithelial cells and 160 normal epithelial cells and identified 342 discriminative transcripts using advanced machine learning methods. The most discriminative transcripts were LGALS4, PHGR1, C15orf48, HEPACAM2, PERP, FABP1, FCGBP, MT1G, TSPAN1 and CKB. We further clustered the 342 transcripts into two categories. The upregulated transcripts in CRC epithelial cells were significantly enriched in Ribosome, Protein processing in endoplasmic reticulum, Antigen processing and presentation and p53 signaling pathway. The downregulated transcripts in CRC epithelial cells were significantly enriched in Mineral absorption, Aldosterone-regulated sodium reabsorption and Oxidative phosphorylation pathways. The biological analysis of the discriminative transcripts revealed the possible mechanism of colorectal cancer.

Identification of Potential Biomarkers with Diagnostic Value in Pituitary Adenomas Using Prediction Analysis for Microarrays Method

Pituitary adenomas are the most common intrasellar tumors. Patients should be identified at an early stage so that effective treatment can be implemented. The study aims at detecting the potential biomarkers with diagnostic value of pituitary adenomas. Using a total of seven gene expression profiles (GEPs) of the datasets from the Gene Expression Omnibus (GEO) database, we first screened 1980 significant differentially expressed genes (DEGs). Then, we employed the prediction analysis for microarray (PAM) algorithm to identify 340 significant DEGs able to differ pituitary tumor from normal samples, which include 208 upregulated DEGs and 132 downregulated DEGs. DAVID database was used to carry out the enrichment analysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways. We found that upregulated candidates were enriched in protein folding and metabolic pathways. Downregulated DEGs saw a significant enrichment in insulin receptor signaling pathway and hedgehog signaling pathway. Based on the protein-protein interaction (PPI) network as well as module analysis, we determined ten hub genes including PHLPP, ENO2, ACTR1A, EHHADH, EHMT2, FOXO1, DLD, CCT2, CSNK1D, and CETN2 that could be potential biomarkers with diagnostic value in pituitary adenomas. In conclusion, the study contributes to reliable and potential molecular biomarkers with diagnostic value. Moreover, these potential biomarkers may be used for prognosis and new therapeutic targets for the pituitary adenomas.

Colonic epithelial cell diversity in health and inflammatory bowel disease

The colonic epithelium facilitates host-microorganism interactions to control mucosal immunity, coordinate nutrient recycling and form a mucus barrier. Breakdown of the epithelial barrier underpins inflammatory bowel disease (IBD). However, the specific contributions of each epithelial-cell subtype to this process are unknown. Here we profile single colonic epithelial cells from patients with IBD and unaffected controls. We identify previously unknown cellular subtypes, including gradients of progenitor cells, colonocytes and goblet cells within intestinal crypts. At the top of the crypts, we find a previously unknown absorptive cell, expressing the proton channel OTOP2 and the satiety peptide uroguanylin, that senses pH and is dysregulated in inflammation and cancer. In IBD, we observe a positional remodelling of goblet cells that coincides with downregulation of WFDC2-an antiprotease molecule that we find to be expressed by goblet cells and that inhibits bacterial growth. In vivo, WFDC2 preserves the integrity of tight junctions between epithelial cells and prevents invasion by commensal bacteria and mucosal inflammation. We delineate markers and transcriptional states, identify a colonic epithelial cell and uncover fundamental determinants of barrier breakdown in IBD.

Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape

Purpose

Transcriptomic profiling has enabled the neater genomic characterization of several cancers, among them colorectal cancer (CRC), through the derivation of genes with enhanced causal role and informative gene sets. However, the identification of small-sized gene signatures, which can serve as potential biomarkers in CRC, remains challenging, mainly due to the great genetic heterogeneity of the disease.

Methods

We developed and exploited an analytical framework for the integrative analysis of CRC datasets, encompassing transcriptomic data and positron emission tomography (PET) measurements. Profiling data comprised two microarray datasets, pertaining biopsy specimen from 30 untreated patients with primary CRC, coupled by their F-18-Fluorodeoxyglucose (FDG) PET values, using tracer kinetic analysis measurements. The computational framework incorporates algorithms for semantic processing, multivariate analysis, data mining and dimensionality reduction.

Results

Transcriptomic and PET data feature sets, were evaluated for their discrimination performance between primary colorectal adenocarcinomas and adjacent normal mucosa. A composite signature was derived, pertaining 12 features: 7 genes and 5 PET variables. This compact signature manifests superior performance in classification accuracy, through the integration of gene expression and PET data.

Conclusions

This work represents an effort for the integrative, multilayered, signature-oriented analysis of CRC, in the context of radio-genomics, inferring a composite signature with promising results for patient stratification.

A reliable method for colorectal cancer prediction based on feature selection and support vector machine

Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. In this study, we build an integrated model based on logistic regression (LR) and support vector machine (SVM) to classify the CRC into cancer and normal samples. From various factors, human location, age, gender, BMI, and cancer tumor type, tumor grade, and DNA, of the cancer, we select the most significant factors (p < 0.05) using logistic regression as main features, and with these features, a grid-search SVM model is designed using different kernel types (Linear, radial basis function (RBF), Sigmoid, and Polynomial). The result of the logistic regression indicates that the Firmicutes (AUC 0.918), Bacteroidetes (AUC 0.856), body mass index (BMI) (AUC 0.777), and age (AUC 0.710) and their combined factors (AUC 0.942) are effective for CRC detection. And the best kernel type is RBF, which achieves an accuracy of 90.1% when k = 5, and 91.2% when k = 10. This study provides a new method for colorectal cancer prediction based on independent risky factors. Graphical abstract Flow chart depicting the method adopted in the study. LR (logistic regression) and ROC curve are used to select independent features as input of SVM. SVM kernel selection aims to find the best kernel function for classification by comparing Linear, RBF, Sigmoid, and Polynomial kernel types of SVM, and the result shows the best kernel is RBF. Classification performance of LR + RF, LR + NB, LR + KNN, and LR + ANNs models are compared with LR + SVM. After these steps, the cancer and healthy individuals can be classified, and the best model is selected.

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Transcobalamin I: a novel prognostic biomarker of neoadjuvant chemotherapy in locally advanced hypopharyngeal squamous cell cancers

Background

Hypopharyngeal squamous cell carcinoma (HPSCC) is an aggressive head and neck squamous cell carcinoma with poor prognosis. Neoadjuvant chemotherapy (NACT) followed by concurrent chemoradiotherapy could provide better efficacy in HPSCC treatment. Identification of predictive biomarkers is critically needed to improve selection of patients who derive the most benefit from NACT. The aim of this study was to investigate whether transcobalamin I (TCN1) could be a novel predictive biomarker for NACT in HPSCC.

Methods

We collected biopsy specimens from 102 patients with primary locally advanced HPSCC. Messenger RNA (mRNA) and protein expression levels of TCN1 were analyzed using quantitative polymerase chain reaction and immunohistochemistry, respectively. The relationship between TCN1 expression, chemotherapy sensitivity, and clinical outcome was assessed using univariate Kaplan-Meier survival analyses and multivariate analysis with covariate adjustments. Furthermore, we knocked down TCN1 by small interfering RNA (siRNA) in HPSCC cell FaDu, tested the effects of TCN1 knockdown on cisplatin toxicity by MTT assay, and detected cisplatin-induced apoptosis by Western blotting.

Results

TCN1 expression was significantly lower in NACT-sensitive patients than nonsensitive patients at protein level (p=0.013) and mRNA level (p<0.001), indicating that low TCN1 expression predicts better NACT treatment response. Furthermore, TCN1 was an independent prognostic biomarker for both overall survival (p=0.047) and disease-free survival (p=0.05) in advanced HPSCC patients. In addition, in vitro experiments showed that genetic silencing of TCN1 using siRNA sensitized FaDu cells to cisplatin treatment with increased cell apoptosis.

Conclusion

Low expression of TCN1 might be a novel prognostic biomarker for predicting NACT sensitivity and clinical outcome in local advanced HPSCC patients.

Differentially expressed lncRNAs and mRNAs identified by NGS analysis in colorectal cancer patients

Long noncoding RNAs (lncRNAs) play an important role in gene regulation, but their impact on the pathogenesis of colorectal cancer and the biological function of cancer cells is unclear. In this study, we used next‐generation sequencing to study the differences in the expression profiles of lncRNAs and mRNAs in colorectal cancer tissues. We analyzed the differentially expressed genes by Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment and predicted new lncRNA functions. Our results revealed that compared with lncRNAs and mRNAs in nontumor colorectal tissues, 1019 lncRNAs (512 upregulated, 507 downregulated) and 3221 mRNAs (1606 upregulated, 1615 downregulated) were differentially expressed in tumor colorectal tissues (fold change >2 and P < 0.05). We validated some of these genes by qPCR. Furthermore, we identified some new lncRNAs differently expressed in colorectal cancer samples from patients in northern China. We confirmed the function of lncRNA‐FIRRE‐201 and SLCO4A1‐AS1‐202 in colorectal cancer cells to provide an experimental basis for studies on their roles in the occurrence and development of colorectal cancer and in the regulation of networks.

Human colorectal cancer initiation is bidirectional, and cell growth, metabolic genes and transporter genes are early drivers of tumorigenesis

The role of stem cells in the development of solid tumors remains controversial. In colorectal cancers (CRC), this is complicated by the conflicting "top-down" or "bottom-up" hypotheses of cancer initiation. We profiled the expressions of genes from the top (T) and bottom (B) crypt fractions of normal-appearing human colonic mucosa (M) at least 20 cm away from the tumor as a baseline and compared this to the genes of matched mucosa adjacent to tumors (M_T) in twenty-three sporadic CRC patients. In thirteen patients, the genetic distance (M-M_T) between the B fractions is smaller than the distance between the T fractions, indicating that the expressions diverge further in the top fractions (B < T). In the remaining patients, the reverse effect is observed (B > T). Assuming that a greater genetic divergence in the top or bottom fractions indicates that position as the initiation site, it is thus equally likely that human CRC initiates from 'top-down' via de-differentiated colonocytes or 'bottom-up' via dysregulated intestinal stem cells. Dysregulated genes that persist until tumor stage are not limited to tumor suppressors or oncogenes but include metabolic and transporter genes such as CA7, PHLPP2, and AQP8.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

BACKGROUND

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.

RESULTS

We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.

CONCLUSIONS

Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

A pan-cancer analysis of MYC-PVT1 reveals CNV-unmediated deregulation and poor prognosis in renal carcinoma

The PVT1 lncRNA has recently been involved in tumorigenesis by affecting the protein stability of the MYC proto-oncogene. Both MYC and PVT1 reside in a well-known cancer-risk locus and enhanced levels of their products have been reported in different human cancers. Nonetheless, the extension and relevance of the MYC-PVT1 deregulation in tumorigenesis has not yet been systematically addressed.

Here we performed a pan-cancer analysis of matched copy number, transcriptomic, methylation, proteomic and clinicopathological profiles for almost 7000 patients from 17 different cancers represented in the TCGA cohorts. Among all cancers types, kidney renal clear cell carcinoma (KIRC) showed the strongest upregulation of PVT1 and increased levels of both MYC and PVT1 correlated with the clinical outcome. PVT1 misregulation in KIRC is mostly associated to promoter hypomethylation rather than locus amplification. Furthermore, we found an association between MYC levels and PVT1 expression, which impacted on MYC-target genes.

Collectively, our study discloses the role of PVT1 as a novel prognostic factor and as a molecular target for novel therapeutic interventions in renal carcinoma.

Transcriptome analysis of human colorectal cancer biopsies reveals extensive expression correlations among genes related to cell proliferation, lipid metabolism, immune response and collagen catabolism

Precise characterization of biological processes critical to proliferation and metastasis of colorectal cancer should facilitate the development of diagnostic and prognostic biomarkers as well as novel treatments. Using mRNA-Seq, we examined the protein coding messenger RNA (mRNA) expression profiles across different histologically defined stages of primary colon cancers and compared them to their patient matched normal tissue controls. In comparing 79 colorectal cancers to their matched normal mucosa, tumors were distinguished from normal non-malignant tissues not only in the upregulation of biological processes pertaining to cell proliferation, inflammation, and tissue remodeling, but even more strikingly, in downregulated biological processes including fatty acid beta oxidization for ATP production and epithelial cell differentiation and function. A network analysis of deregulated genes revealed newly described cancer networks and putative hub genes.

Taken together, our findings suggest that, within an inflammatory microenvironment, invasive, dedifferentiated and rapidly dividing tumor cells divert the oxidation of fatty acids and lipids from energy production into lipid components of cell membranes and organelles to support tumor proliferation. A gene co-expression network analysis provides a clear and broad picture of biological pathways in tumors that may significantly enhance or supplant current histopathologic studies.

Spi-B-Mediated Silencing of Claudin-2 Promotes Early Dissemination of Lung Cancer Cells from Primary Tumors

Dissociation from epithelial sheets and invasion through the surrounding stroma are critical early events during epithelial cancer metastasis. Here we find that a lymphocyte lineage-restricted transcription factor, Spi-B, is frequently expressed in human lung cancer tissues. The Spi-B-expressing cancer cells coexpressed vimentin but repressed E-cadherin and exhibited invasive behavior. Increased Spi-B expression was associated with tumor grade, lymphatic metastasis, and short overall survival. Mechanistically, Spi-B disrupted intercellular junctions and enhanced invasiveness by reconfiguring the chromatin structure of the tight junction gene claudin-2 (CLDN2) and repressing its transcription. These data suggest that Spi-B participates in mesenchymal invasion, linking epithelial cancer metastasis with a lymphatic transcriptional program. Cancer Res; 77(18); 4809-22. ©2017 AACR.

Overexpression of Transcobalamin 1 is an Independent Negative Prognosticator in Rectal Cancers Receiving Concurrent Chemoradiotherapy

Objective: Neoadjuvant concurrent chemoradiotherapy (CCRT) is an increasingly common therapeutic strategy for locally advanced rectal cancer, but stratification of risk and final outcomes remain a major challenge. Transcobalamin 1 (TCN1), a vitamin B12 (cobalamin)-binding protein, regulates cobalamin homeostasis. High expression of TCN1 have been reported in neoplasms such as breast cancer and hepatocellular carcinoma. However, little is known about the relevance of TCN1 to rectal cancer receiving CCRT. This study examined the predictive and prognostic impact of TCN1 expression in patients with rectal cancer following neoadjuvant CCRT. Methods: Through data mining from a published transcriptome of rectal cancers (GSE35452), we identified upregulation of TCN1 gene as the most significantly predicted poor response to CCRT among ion transport-related genes (GO:0006811). We evaluated TCN1 immunohistochemistry and performed an H-score analysis on endoscopic biopsy specimens from 172 rectal cancer patients receiving neoadjuvant CCRT followed by curative surgery. Expression levels of TCN1 were further correlated with clinicopathologic features, therapeutic response, tumor regression grade (TRG) and survivals including metastasis-free survival (MeFS), disease-specific survival (DSS) and recurrent-free survival (LRFS). Results: TCN1 overexpression was significantly related to advanced post-treatment tumor (T3, T4; p<0.001) and nodal status (N1, N2; p<0.001), vascular invasion (p=0.003) and inferior tumor regression grade (p < 0.001). In survival analyses, TCN1 overexpression was significantly associated with shorter DSS (p<0.0001), MeFS (p=0.0002) and LRFS (p=0.0001). Furthermore, it remained an independent prognosticator of worse DSS (p=0.002, hazard ratio=3.344), MeFS (p=0.021, hazard ratio=3.015) and LRFS (p=0.037, hazard ratio=3.037) in the multivariate comparison. Conclusion: Overexpression of TCN1 is associated with poor therapeutic response and adverse outcomes in rectal cancer patients receiving CCRT, justifying the potential prognostic value of TCN1 in rectal cancer receiving CCRT.

Functionally-focused algorithmic analysis of high resolution microarray-CGH genomic landscapes demonstrates comparable genomic copy number aberrations in MSI and MSS sporadic colorectal cancer

Array-based comparative genomic hybridization (aCGH) emerged as a powerful technology for studying copy number variations at higher resolution in many cancers including colorectal cancer. However, the lack of standardized systematic protocols including bioinformatic algorithms to obtain and analyze genomic data resulted in significant variation in the reported copy number aberration (CNA) data. Here, we present genomic aCGH data obtained using highly stringent and functionally relevant statistical algorithms from 116 well-defined microsatellites instable (MSI) and microsatellite stable (MSS) colorectal cancers. We utilized aCGH to characterize genomic CNAs in 116 well-defined sets of colorectal cancer (CRC) cases. We further applied the significance testing for aberrant copy number (STAC) and Genomic Identification of Significant Targets in Cancer (GISTIC) algorithms to identify functionally relevant (nonrandom) chromosomal aberrations in the analyzed colorectal cancer samples. Our results produced high resolution genomic landscapes of both, MSI and MSS sporadic CRC. We found that CNAs in MSI and MSS CRCs are heterogeneous in nature but may be divided into 3 distinct genomic patterns. Moreover, we show that although CNAs in MSI and MSS CRCs differ with respect to their size, number and chromosomal distribution, the functional copy number aberrations obtained from MSI and MSS CRCs were in fact comparable but not identical. These unifying CNAs were verified by MLPA tumor-loss gene panel, which spans 15 different chromosomal locations and contains 50 probes for at least 20 tumor suppressor genes. Consistently, deletion/amplification in these frequently cancer altered genes were identical in MSS and MSI CRCs. Our results suggest that MSI and MSS copy number aberrations driving CRC may be functionally comparable.

Risk analysis of colorectal cancer incidence by gene expression analysis

Background

Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction.

Objective

Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples.

Methods

We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues.

Results

Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method.

Conclusions

Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.

Gene expression changes in colon tissues from colorectal cancer patients following the intake of an ellagitannin-containing pomegranate extract: a randomized clinical trial

The clinical evidence of dietary polyphenols as colorectal cancer (CRC) chemopreventive compounds is very weak. Verification in humans of tissue-specific molecular regulation by the intake of polyphenols requires complex clinical trials that allow for the procurement of sufficient pre- and postsupplementation tissue samples. Ellagitannins (ETs), ellagic acid (EA) and their gut microbiota-derived metabolites, the urolithins, modify gene expression in colon normal and cancer cultured cells. We conducted here the first clinical trial with 35 CRC patients daily supplemented with 900 mg of an ET-containing pomegranate extract (PE) and evaluated the expression of various CRC-related genes in normal and cancerous colon tissues before (biopsies) and after (surgical specimens) 5-35 days of supplementation. Tissues were also obtained from 10 control patients (no supplementation) that confirmed a large, gene- and tissue-specific interindividual variability and impact of the experimental protocol on gene expression, with some genes induced (MYC, CD44, CDKN1A, CTNNB1), some repressed (CASP3) and others not affected (KRAS). Despite these issues, the consumption of the PE was significantly associated with a counterbalance effect in the expression of CD44, CTNNB1, CDKN1A, EGFR and TYMs, suggesting that the intake of this PE modulated the impact of the protocol on gene expression in a gene- and tissue-specific manner. These effects were not associated with the individuals' capacity to produce specific urolithins (i.e., metabotypes) or the levels of urolithins and EA in the colon tissues and did not reproduce in vitro effects evidencing the difficulty of demonstrating in vivo the in vitro results.

Prediction of Possible Biomarkers and Novel Pathways Conferring Risk to Post-Traumatic Stress Disorder

Post-traumatic stress disorder is one of the common mental ailments that is triggered by exposure to traumatic events. Till date, the molecular factors conferring risk to the development of PTSD is not well understood. In this study, we have conducted a meta-analysis followed by hierarchical clustering and functional enrichment, to uncover the potential molecular networks and critical genes which play an important role in PTSD. Two datasets of expression profiles from Peripheral Blood Mononuclear Cells from 62 control samples and 63 PTSD samples were included in our study. In PTSD samples of GSE860 dataset, we identified 26 genes informative when compared with Post-deploy PTSD condition and 58 genes informative when compared with Pre-deploy and Post-deploy PTSD of GSE63878 dataset. We conducted the meta-analysis using Fisher, roP, Stouffer, AW, SR, PR and RP methods in MetaDE package. Results from the rOP method of MetaDE package showed that among these genes, the following showed significant changes including, OR2B6, SOX21, MOBP, IL15, PTPRK, PPBPP2 and SEC14L5. Gene ontology analysis revealed enrichment of these significant PTSD-related genes for cell proliferation, DNA damage and repair (p-value ≤ 0.05). Furthermore, interaction network analysis was performed on these 7 significant genes. This analysis revealed highly connected functional interaction networks with two candidate genes, IL15 and SEC14L5 highly enriched in networks. Overall, from these results, we concluded that these genes can be recommended as some of the potential targets for PTSD.

Insights into the role of reactive sulfhydryl groups of Carbonic Anhydrase III and VII during oxidative damage

Carbonic anhydrases (CAs) III and VII are two cytosolic isoforms of the α-CA family which catalyze the physiological reaction of carbon dioxide hydration to bicarbonate and proton. Despite these two enzymes share a 49% sequence identity and present a very similar three-dimensional structure, they show profound differences when comparing the specific activity for CO₂ hydration reaction, with CA VII being much more active than CA III. Recently, CA III and CA VII have been proposed to play a new role as scavenger enzymes in cells where oxidative damage occurs. Here, we will examine functional and structural features of these two isoforms giving insights into their newly proposed protective role against oxidative stress.

Able but not willing? Exploring divides in digital versus physical payment use in China

Purpose

– China is the world’s largest user market for digital technologies and experiencing unprecedented rates of rural-urban migration set to create the world’s first “urban billion”. This is an important context for studying nuanced adoption behaviours that define a digital divide. Large-scale studies are required to determine what behaviours exist in such populations, but can offer limited ability to draw inferences about why. The purpose of this paper is to report a large-scale study inside China that probes a nuanced “digital divide” behaviour: consumer demographics indicating ability to pay by electronic means but behaviour suggesting lack of willingness to do so, and extends current demographics to help explain this.

Design/methodology/approach

– The authors report trans-national access to commercial “Big Data” inside China capturing the demographics and consumption of millions of consumers across a wide range of physical and digital market channels. Focusing on one urban location we combine traditional demographics with a new measure that reflecting migration: “Distance from Home”, and use data-mining techniques to develop a model that predicts use behaviour.

Findings

– Use behaviour is predictable. Most use is explained by value of the transaction. “Distance from Home” is more predictive of technology use than traditional demographics.

Research limitations/implications

– Results suggest traditional demographics are insufficient to explain “why” use/non-use occurs and hence an insufficient basis to formulate and target government policy.

Originality/value

– The authors understand this to be the first large-scale trans-national study of use/non-use of digital channels within China, and the first study of the impact of distance on ICT adoption.

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

Purpose

There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier.

Methods

In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid).

Results

The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF), Bayes net (BN), multilayer perceptron (MLP), naïve Bayes (NB), reduced error pruning tree (REPT), and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP). Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1, MMP7, and TGFB1 were predicted to be CRC biomarkers.

Conclusion

This model could be used to further develop a diagnostic tool for predicting CRC based on gene expression data from patient samples.

Integrated analysis of transcriptome in cancer patient-derived xenografts

Patient-derived xenograft (PDX) tumor model is a powerful technology in evaluating anti-cancer drugs and facilitating personalized medicines. Multiple research centers and commercial companies have put huge efforts into building PDX mouse models. However, PDX models have not been widely available and their molecular features have not been systematically characterized. In this study, we provided a comprehensive survey of PDX transcriptome by integrating analysis of 58 patients involving 8 different tumors. The median correlation coefficient between patients and xenografts is 0.94, which is higher than that between patients and cell line panel or between patients with the same tumor. Major differential gene expressions in PDX occur in the engraftment of human tumor tissue into mice, while gene expressions are relatively stable over passages. 48 genes are frequently differentially expressed in PDX mice of multiple cancers. They are enriched in extracellular matrix and immune response, and some are reported as targets for anticancer drugs. A simulation study showed that expression change between PDX and patient tumor (6%) would result in acceptable change in drug sensitivity (3%). Our findings demonstrate that PDX mice represent the gene-expression and drug-response features of primary tumors effectively, and it is recommended to monitoring the overall expression profiles and drug target genes in clinical application.

Prognostic value of carbonic anhydrase VII expression in colorectal carcinoma

BACKGROUND

Carbonic anhydrases (CAs) have been implicated in the pathogenesis of human cancers. Carbonic anhydrase VII (CA7), a member of the CA gene family, was recently demonstrated to be expressed in several human tissues including colon. Nevertheless, the expression and clinical relevance of CA7 in colorectal carcinoma (CRC) has not been investigated.

METHODS

Real-time PCR, western blot, and immunohistochemistry analyses were used to determine CA7 expression in CRC clinical samples. The correlation of CA7 expression with clinicopathologic features was assessed in 228 patients from Luoyang, China (training cohort) and validated in 151 patients from Shanghai, China (validation cohort). Kaplan-Meier and Cox proportional regression analyses were used to estimate the association between CA7 expression and patients' survival.

RESULTS

CA7 expression was frequently downregulated in CRC tissues at both the mRNA and protein levels. Reduced expression of CA7 was significantly correlated with poor differentiation, positive lymph node metastasis, advanced TNM stage and unfavorable clinical outcome not only in the training cohort but also in the validation set. Survival analysis indicated that patients with lower CA7 expression had a significantly shorter disease-specific survival (DSS) than those with higher CA7 expression. Importantly, further stage-based analyses revealed that decreased CA7 expression significantly predicted poor DSS and was an independent adverse prognostic indicator for patients with early stage tumors in both cohorts.

CONCLUSIONS

Our results indicate that decreased expression of CA7 correlates with disease progression and predicts poor prognosis in CRC, especially for patients with early stage tumors.