In silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimisation approach

Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI

Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota's composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.

Fetuin-A Promotes 3-Dimensional Growth in LNCaP Prostate Cancer Cells by Sequestering Extracellular Vesicles to Their Surfaces to Act as Signaling Platforms

The present studies were conducted to evaluate key serum proteins and other components that mediate anchorage-independent growth (3-D growth) of LNCaP prostate cancer cells as spheroids. The cells were cultured on ultra-low attachment plates in the absence and presence of fetuin-A and with or without extracellular vesicles. The data show that fetuin-A (alpha 2HS glycoprotein) is the serum protein that mediates 3-D growth in these cells. It does so by sequestering extracellular vesicles of various sizes on the surfaces of rounded cells that grow as spheroids. These vesicles in turn transmit growth signals such as the activation of AKT and MAP kinases in a pattern that differs from the activation of these key growth signaling pathways in adherent and spread cells growing in 2-D. In the process of orchestrating the movement and disposition of extracellular vesicles on these cells, fetuin-A is readily internalized in adhered and spread cells but remains on the surfaces of non-adherent cells. Taken together, our studies suggest the presence of distinct signaling domains or scaffolding platforms on the surfaces of prostate tumor cells growing in 3-D compared to 2-D.

Investigation of the expression levels of CPEB4, APC, TRIP13, EIF2S3, EIF4A1, IFNg, PIK3CA and CTNNB1 genes in different stage colorectal tumors

Background/aim

The aim of the study is to assess expression levels of CPEB4, APC, TRIP13, EIF2S3, EIF4A1, IFNg, PIK3CA and CTNNB1 genes in tumors and peripheral bloods of colorectal cancer patients in stages I–IV.

Materials and methods

The mRNA levels of the genes were determined in tumor tissues and peripheral blood samples of 45 colorectal cancer patients and colon tissues and peripheral blood samples of 5 healthy individuals. Real-time polymerase chain reaction method was used for the analysis.

Results

The mRNA level of the CPEB4 gene was significantly downregulated in colorectal tumor tissues and was upregulated in the peripheral blood of colorectal cancer patients relative to the controls (P < 0.05). APC mRNA level was significantly downregulated in tissues and upregulated in the peripheral blood (P < 0.05). TRIP13 mRNA level was upregulated in peripheral blood and also significantly upregulated in colorectal tumor tissues (P < 0.05). EIF2S3 mRNA level was upregulated in tissues and also significantly upregulated in peripheral blood (P < 0.05). PIK3CA mRNA level was downregulated in tissues and upregulated in peripheral blood. EIF4A1 mRNA level was downregulated in tissues and significantly upregulated in peripheral blood (P < 0.05). CTNNB1 mRNA level was downregulated in tissues and upregulated in peripheral blood. IFNg mRNA level was upregulated in both colorectal cancer tumor tissues and peripheral blood.

Conclusion:

TRIP13 and CPEB4 mRNA up regulation in the peripheral blood of patients with colorectal cancer may be a potential target for early stage diagnosis. In addition to this evaluation, although there is not much study on EIF2S3 and EIF4A1 mRNA changes in cases with colorectal cancer, upregulation in peripheral blood draws attention in our study. These data will shed light on the new comprehensive studies.

Application of artificial intelligence to the diagnosis and therapy of colorectal cancer

Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.

The rapid endocytic uptake of fetuin-A by adherent tumor cells is mediated by Toll-like receptor 4 (TLR4)

Fetuin‐A is a serum glycoprotein synthesized and secreted into blood by the liver and whose main physiological function is the inhibition of ectopic calcification. However, a number of studies have demonstrated that it is a multifunctional protein. For example, endocytic uptake of fetuin‐A by tumor cells resulting in rapid cellular adhesion and spreading has been reported. The precise uptake mechanism, however, has been elusive. The present studies were done to determine whether Toll‐like receptor‐4 (TLR4), which has been previously shown to be a receptor for fetuin‐A and is commonly expressed in immune cells, could take part in the rapid uptake (< 3 min) of fetuin‐A by tumor cells. Rapid uptake of fetuin‐A was inhibited by the specific TLR4 inhibitor CLI‐095 and also attenuated in TLR4 knockdown prostate tumor cells. Inhibition of TLR4 by CLI‐095 also attenuated the rapid adhesion of tumor cells as well as invasion through a bed of Matrigel. The data suggest mechanisms by which TLR4 modulates the adhesion and growth of tumor cells.

The emerging role of super enhancer-derived noncoding RNAs in human cancer

Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.

Prediction of drug synergy score using ensemble based differential evolution

Prediction of drug synergy score is an ill-posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression-based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Histone-lysine N-methyltransferase SETD7 is a potential serum biomarker for colorectal cancer patients

Background

There is an urgent need for the identification of new, clinically useful biomarkers of CRC to enhance diagnostic and prognostic capabilities.

Methods

We performed proteomic profiling on serum samples from paired pre- and post-operative CRC patients, colorectal polyps patients and healthy controls using an approach combining magnetic bead-based weak cation exchange and matrix-assisted laser desorption ionization-time of flight mass spectrometry. We next performed liquid chromatography-electrospray ionization-tandem mass spectrometry to identify the proteins and selected potential biomarker based on bioinformatics analysis of the TCGA and GEO dataset. We examined SETD7 expression in serum and tissue samples by ELISA and immunohistochemistry respectively and explored the biological function of SETD7 in vitro.

Findings

85 differentially expressed peptides were identified. Five peptides showing the most significant changes in abundance across paired pre- and post-operation CRC patients, colorectal polyps patients and healthy controls were identified as peptide regions of FGA, MUC5AC and SETD7. Bioinformatics analysis suggested that the up-regulation of SETD7 in CRC is relatively specific. Validation studies showed that SETD7 expression increased from healthy controls to those with colorectal polyps and finally CRC patients, and decreased after surgery. The sensitivity and specificity of SETD7 were 92.17% and 81.08%, with a high diagnostic value (AUC = 0.9477). In addition, SETD7 expression was significantly correlated with tumor stage and microsatellite instability. Knockdown of SETD7 inhibited cancer cell proliferation, induced G1/S cell cycle arrest and increased apoptosis.

Interpretation

Our data indicate that SETD7 could serve as a potential diagnostic and prognostic biomarker for CRC.

Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy

Here, a two-phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi-objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache-based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.

Role of mathematical medicine in gastrointestinal carcinoma: Current status and perspectives

Mathematical medicine has already played an important role in clinical and basic research as a major interdisciplinary branch of medicine. Mathematical medicine has an important role not only in imaging diagnosis, image storage and transmission in gastrointestinal (GI) cancer, but also in tumor precision therapy. Specifically, in the field of minimally invasive treatment such as precise ablation, 3-dimension modeling, navigation, and surgical simulation significantly improve the therapeutic safety and efficiency in GI cancer. In addition, in the era of big data, data analysis and individualized therapy using mathematical medicine will become a trend in the future, offering an effective method for diagnosing and treating GI cancer and promoting clinical and scientific research.