To Örebro University

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Early diagnosis and better therapy response have been believed to be associated with better prognosis. CRC biomarkers are considered as precise indicators for the early diagnosis and better therapy response. It is, therefore, of importance to find out, analyze and evaluate the CRC biomarkers to further provide the more precis evidence for predicting novel potential biomarkers and eventually to improve early diagnosis, personalized therapy and prognosis for CRC.

In this study, we started with creating and establishing a CRC biomarker database. (CBD: http://sysbio.suda.edu.cn/CBD/index.html) In the CBD database, there were 870 reported CRC biomarkers collected from the published articles in PubMed. In this version of the CBD, CRC biomarker data was carefully collected, sorted, displayed, and analyzed. The major applications of the CBD are to provide 1) the records of CRC biomarkers (DNA, RNA, protein and others) concerning diagnosis, treatment and prognosis; 2) the basic and clinical research information concerning the CRC biomarkers; 3) the primary results for bioinformatics and biostatics analysis of the CRC biomarkers; 4) downloading/uploading the biomedicine information for CRC biomarkers.

Based on our CBD and other public databases, we further analyzed the presented CRC biomarkers (DNAs, RNAs, proteins) and predicted novel potential multiple biomarkers (the combination of single biomarkers) with biological networks and pathways analysis for diagnosis, therapy response and prognosis in CRC. We found several hub biomarkers and key pathways for the diagnosis, treatment and prognosis in CRC. Receiver operating characteristic (ROC) test and survival analysis by microarray data revealed that multiple biomarkers could be better biomarkers than the single biomarkers for the diagnosis and prognosis of CRC.

There are 62 diagnosis biomarkers for colon cancer in our CBD. In the previous studies, we found these present biomarkers were not enough to improve significantly the diagnosis of colon cancer. In order to find out novel biomarkers for the colon cancer diagnosis, we have performed /machine learning (ML) techniques such as support vector machine (SVM) and regression tree to predict candidate to discover diagnostic biomarkers for colon cancer. Based on the protein-protein interaction (PPI) network topology features of the identified biomarkers, we found 12 protein biomarkers which were considered as the candidate colon cancer diagnosis biomarkers. Among these protein biomarkers Chromogranin-A (CHGA)  was the most powerful biomarker, which showed good performance in bioinformatics test and Immunohistochemistry(IHC). We are now expanding this study to CRC.

Expression of CHGA protein in colon cancer was further verified with a novel logistic regressionbased meta-analysis, and convinced as a valuable diagnostic biomarker as compared with the typical diagnostic biomarkers, such as TP53, KRAS and MKI67.

microRNAs (miRNAs/miRs) have been considered as potential biomarkers. A novel miRNA-mRNA interaction network-based model was used to predict miRNA biomarkers for CRC and found that miRNA-186-5p, miRNA-10b-5p and miRNA-30e-5p might be the novel biomarkers for CRC diagnosis. In conclusion, we have created a useful CBD database for CRC biomarkers and provided detailed information for how to use the CBD in CRC biomarker investigations. Our studies have been focusing on the biomarkers in diagnosis, therapy and prognosis. Based on our CBD and other powerful cancer associated databases, ML has been used to analyze the characteristics of the CRC biomarkers and predict novel potential CRC biomarkers. The predicted potential biomarkers were further confirmed at biomedical laboratory.

Differential expressions and functions of various micoRNAs (miRNAs) have been intensively studied in both colon and rectal cancers. However, the importance of miRNAs on radiotherapy (RT) response and clinical outcome in rectal cancer patients remains unclear. In this study, we used real-time polymerase chain reaction to examine the expressions of miR-302a, miR-105, and miR-888 in normal mucosa and cancer tissue from rectal cancer patients with and without preoperative RT. The biological function of miR-302a, miR-105, and miR-888 expression was further analyzed and identified through the public databases: TCGA (The Cancer Genome Atlas) and GEPIA (Gene Expression Profiling Interactive Analysis). The results showed that the expression of miR-105 in rectal cancer was higher than that in normal mucosa in RT (P = 0.042) and non-RT patients (P = 0.003) and was associated with mucinous histological type (P = 0.004), COX-2 (P = 0.042), and p73 expression (P = 0.030). The expression of miR-302a was shown more frequently in cancers with necrosis (P = 0.033) and with WRAP53 expression (P = 0.015), whereas miR-888 expression occurred more frequently in tumors with protein the expression of survivin (P = 0.015), AEG-1 (astrocyte elevated gene-1) (P = 0.003), and SATB1 (special AT-rich sequence binding protein 1) (P = 0.036). Moreover, TargetScan also predicted AEG-1 and SATB1 as putative targets for miR-888. The miRNA-gene network analysis showed that ABI2 was associated with all the three miRNAs, with lower expression and good diagnostic value in rectal cancers. The TCGA database demonstrated the association of miR-105 expression with high carcinoembryonic antigen level (P = 0.048). RT reduced the expressions of miR-302a, miR-105, and miR-888. Prognostic analysis showed that miR-888 expression was independently associated with worse survival of patients without RT [overall survival, P = 0.001; disease-free survival, P = 0.009]. Analysis of biological function revealed that the protein serine/threonine kinase activity and PI3K-AKT signaling pathway were the most significantly enriched functions and pathways, respectively. Our findings suggest that miR-105 is involved in rectal cancer pathogenesis and miR-888 is associated with prognosis. MiR-302a, miR-105, and miR-888 have potential influence on the pathogenesis, RT, and prognosis of rectal cancer.

In acute aristolochic acid nephropathy (AAN), aristolochic acid (AA) induces renal injury and tubulointerstitial fibrosis. However, the roles of microRNAs (miRNAs/miRs) and mRNAs involved in AAN are not clearly understood. The aim of the present study was to examine AA-induced genome-wide differentially expressed (DE) miRNAs and DE mRNAs using deep sequencing in mouse kidneys, and to analyze their regulatory networks. In the present self-controlled study, mice were treated with 5 mg/kg/day AA for 5 days, following unilateral nephrectomy. AA-induced renal injury and tubulointerstitial fibrosis were detected using hematoxylin and eosin staining and Masson's trichrome staining in the mouse kidneys. A total of 82 DE miRNAs and 4,605 DE mRNAs were identified between the AA-treated group and the self-control group. Of these DE miRNAs and mRNAs, some were validated using reverse transcription-quantitative PCR. Expression levels of the profibrotic miR-21, miR-433 and miR-132 families were significantly increased, whereas expression levels of the anti-fibrotic miR-122-5p and let-7a-1-3p were significantly decreased. Functions and signaling pathways associated with the DE miRNAs and mRNAs were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 767 DE pairs (in opposing directions) of miRNAs and their mRNA targets were identified. Among these, regulatory networks of miRNAs and mRNAs were analyzed using KEGG to identify enriched signaling pathways and extracellular matrix-associated pathways. In conclusion, the present study identified genome-wide DE miRNAs and mRNAs in the kidneys of AA-treated mice, as well as their regulatory pairs and signaling networks. The present results may improve the understanding of the role of DE miRNAs and their mRNA targets in the pathophysiology of acute AAN.

Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis.

Colon cancer is one of the major causes of cancer death worldwide. The five-year survival rate for the early-stage patients is more than 90%, and only around 10% for the later stages. Moreover, half of the colon cancer patients have been clinically diagnosed at the later stages. It is; therefore, of importance to enhance the ability for the early diagnosis of colon cancer. Taking advantages from our previous studies, there are several potential biomarkers which have been associated with the early diagnosis of the colon cancer. In order to investigate these early diagnostic biomarkers for colon cancer, human chromogranin-A (CHGA) was further analyzed among the most powerful diagnostic biomarkers. In this study, we used a logistic regression-based meta-analysis to clarify associations of CHGA expression with colon cancer diagnosis. Both healthy populations and the normal mucosa from the colon cancer patients were selected as the double normal controls. The results showed decreased expression of CHGA in the early stages of colon cancer as compared to the normal controls. The decline of CHGA expression in the early stages of colon cancer is probably a new diagnostic biomarker for colon cancer diagnosis with high predicting possibility and verification performance. We have also compared the diagnostic powers of CHGA expression with the typical oncogene KRAS, classic tumor suppressor TP53, and well-known cellular proliferation index MKI67, and the CHGA showed stronger ability to predict early diagnosis for colon cancer than these other cancer biomarkers. In the protein-protein interaction (PPI) network, CHGA was revealed to share some common pathways with KRAS and TP53. CHGA might be considered as a novel, promising, and powerful biomarker for early diagnosis of colon cancer.

In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and multiple DNA, RNA and protein biomarkers as well as their positions in cancer related pathways and protein-protein interaction (PPI) networks to describe their potential applications in diagnosis, therapy and prognosis. CRC biomarkers were collected from the CBD. The RNA and protein biomarkers were matched to their corresponding DNAs by the miRDB database and the PubMed Gene database, respectively. The PPI networks were used to investigate the relationships between protein biomarkers and further detect the multiple biomarkers. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were used to analyse biological functions of the biomarkers. AI classification techniques were utilized to further verify the significances of the multiple biomarkers in diagnosis and prognosis for CRC. We showed that a large number of the DNA, RNA and protein biomarkers were associated with the diagnosis, therapy and prognosis in various degrees in the CRC biomarker networks. The CRC biomarkers were closely related to the CRC initiation and progression. Moreover, the biomarkers played critical roles in cellular proliferation, apoptosis and angiogenesis and they were involved in Ras, p53 and PI3K pathways. There were overlaps among the DNA, RNA and protein biomarkers. AI classification verifications showed that the combined multiple protein biomarkers played important roles to accurate early diagnosis and predict outcome for CRC. There were several single and multiple CRC protein biomarkers which were associated with diagnosis, therapy and prognosis in CRC. Further, AI-assisted analysis revealed that multiple biomarkers had potential applications for diagnosis and prognosis in CRC.

Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.

This systematic analysis aimed to investigate the value of microRNA-21 (miR-21) in colorectal cancer for multiple purposes, including diagnosis and prognosis, as well as its predictive power in combination biomarkers. Fifty-seven eligible studies were included in our meta-analysis, including 25 studies for diagnostic meta-analysis and 32 for prognostic meta-analysis. For the diagnostic meta-analysis of miR-21 alone, the overall pooled results for sensitivity, specificity, and area under the curve (AUC) were 0.64 (95% CI: 0.53-0.74), 0.85 (0.79-0.90), and 0.85 (0.81-0.87), respectively. Circulating samples presented corresponding values of 0.72 (0.63-0.79), 0.84 (0.78-0.89), and 0.86 (0.83-0.89), respectively. For the diagnostic meta-analysis of miR-21-related combination biomarkers, the above three parameters were 0.79 (0.69-0.86), 0.79 (0.68-0.87), and 0.86 (0.83-0.89), respectively. Notably, subgroup analysis suggested that miRNA combination markers in circulation exhibited high predictive power, with sensitivity of 0.85 (0.70-0.93), specificity of 0.86 (0.77-0.92), and AUC of 0.92 (0.89-0.94). For the prognostic meta-analysis, patients with higher expression of miR-21 had significant shorter disease-free survival [DFS; pooled hazard ratio (HR): 1.60; 95% CI: 1.20-2.15] and overall survival (OS; 1.54; 1.27-1.86). The combined HR in tissues for DFS and OS were 1.76 (1.31-2.36) and 1.58 (1.30-1.93), respectively. Our comprehensive systematic review revealed that circulating miR-21 may be suitable as a diagnostic biomarker, while tissue miR-21 could be a prognostic marker for colorectal cancer. In addition, miRNA combination biomarkers may provide a new approach for clinical application.