The effect of resveratrol supplementation on serum levels of asymmetric de-methyl-arginine and paraoxonase 1 activity in patients with type 2 diabetes: A randomized, double-blind controlled trial.

The present study sought to investigate the effect of micronized resveratrol supplementation on serum levels of asymmetric de-methyl-arginine (ADMA) and paraoxonase-1 (PON1) activity in patients with type 2 diabetes (T2D). In this double-blinded randomized trial, 76 patients with T2D were recruited. Participants were randomly assigned to consume 1,000 mg resveratrol or placebo capsules (methylcellulose) per day, for 8 weeks. Serum levels of ADMA and PON1 enzyme activity were measured at the beginning and end of the intervention using the enzyme-linked immunosorbent assay method. In total, 71 participants completed the study. Our results showed that resveratrol significantly decreased serum levels of ADMA (-0.16 ± 0.11, p < .001) and improved PON1 enzyme activity (15.39 ± 13.99, p < .001) compared with placebo, after adjusting for confounding factors (age, sex, and baseline body mass index). Our findings suggest that 8-week resveratrol supplementation may produce beneficial effects on serum levels of ADMA and PON1 enzyme activity in patients with T2DM. However, further research is needed to confirm the veracity of these results.

Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.

The incorporation of AI models into bioinformatics has brought about a revolutionary era in the analysis and interpretation of biological data. This mini-review offers a succinct overview of the indispensable role AI plays in the convergence of computational techniques and biological research. The search strategy followed PRISMA guidelines, encompassing databases such as PubMed, Embase, and Google Scholar to include studies published between 2018 and 2024, utilizing specific keywords. We explored the diverse applications of AI methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), across various domains of bioinformatics. These domains encompass genome sequencing, protein structure prediction, drug discovery, systems biology, personalized medicine, imaging, signal processing, and text mining. AI algorithms have exhibited remarkable efficacy in tackling intricate biological challenges, spanning from genome sequencing to protein structure prediction, and from drug discovery to personalized medicine. In conclusion, this study scrutinizes the evolving landscape of AI-driven tools and algorithms, emphasizing their pivotal role in expediting research, facilitating data interpretation, and catalyzing innovations in biomedical sciences.

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration.

PURPOSE: Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes. METHODS: The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment. RESULTS: Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes. CONCLUSIONS: The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease.

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

INTRODUCTION: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC). METHOD: The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs. RESULT: The results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and HSP90AA1. CONCLUSION: To better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.

Diagnostic, prognostic, and predictive biomarkers in gastric cancer: from conventional to novel biomarkers.

Gastric cancer is a major health concern worldwide. The survival rate of Gastric cancer greatly depends on the stage at which it is diagnosed. Early diagnosis is critical for improving survival outcomes. To improve the chances of early diagnosis, regular screening tests, such as an upper endoscopy or barium swallow, are recommended for individuals at a higher risk due to factors like family history or a previous diagnosis of gastric conditions. Biomarkers can be detected and measured using non-invasive methods such as blood tests, urine tests, breath analysis, or imaging techniques. These non-invasive approaches offer many advantages, including convenience, safety, and cost-effectiveness, making them valuable tools for disease diagnosis, monitoring, and research. Biomarker-based tests have emerged as a useful tool for identifying gastric cancer early, monitoring treatment response, assessing the recurrence risk, and personalizing treatment plans. In this current review, we have explored both classical and novel biomarkers for gastric cancer. We have centralized their potential clinical application and discussed the challenges in Gastric cancer research.

The Therapeutic Potential of Targeting Tumor Microenvironment and Modulation of Immunotherapy in Gastrointestinal Cancer.

Immunotherapy, as a novel treatment approach for various disorders, including cancers, is designed to either stimulate or suppress the immune system with high speci-ficity. The recent achievements of this therapy in clinical trials are set to transform tradi-tional treatment methods. Furthermore, it holds promise for enhancing the survival rates of patients suffering from both metastatic cancers and primary stages. Gastrointestinal Cancers (GI) account for 26% of global incidence and 35% of worldwide deaths. Treat-ment can be carried out using targeted immunotherapy in these cancers. If the tiers are superior, improvement could require more enterprise. On account that the function of immunotherapy in GI has been so promising, solely in sufferers with severe metastatic levels, within the literature, the immune checkpoint inhibitors in cancer immunotherapy of GI cancers, chimeric antigen receptor T-cell (vehicle-T), modulators of the tumor mi-croenvironment, and drug resistance mechanisms in immunotherapy as an effective treatment approach to GI cancers along with colon, pancreas, gastric, and esophageal cancers have been addressed. This review provides an overview of FDA-approved im-munotherapy drugs and ongoing preclinical developments. Additionally, we offer in-sights into the future of immunotherapy for GI cancer patients, addressing the associated challenges.

The Role of Epigenetic Mechanisms in the Treatment of GI Cancers.

Epigenetic mechanisms have been shown to play a critical role in the development and progression of gastrointestinal [GI] cancers. These mechanisms involve modifications to DNA and histones that can alter gene expression patterns and may contribute to the initiation and progression of cancers. In recent years, epigenetic therapies have emerged as a promising approach to treating GI cancers. These therapies target specific epigenetic modifications, such as DNA methylation and histone acetylation, to restore normal gene expression patterns and inhibit cancer cell growth. Several epigenetic drugs have been approved for the treatment of GI cancers. Moreover, the use of epigenetic therapies in combination with other treatments, such as chemotherapeutic agents, is being studied to improve treatment outcomes. We have provided an overview of the role of epigenetic mechanisms in GI cancer treatment aimed to focus on recent evidence of the use of epigenetic agents in clinical and preclinical GI cancer studies, including gastric, esophageal, hepatic, pancreatic, and colorectal cancers. Overall, the role of epigenetic mechanisms in GI cancer treatments is an active area of research with the potential to improve patients' treatment outcomes and advance cancer treatment strategies.

The therapeutic impact of programmed death - 1 in the treatment of colorectal cancer.

Colorectal cancer (CRC) is the most common type of newly diagnosed cancer. Metastatic spread and multifactorial chemoresistance have limited the benefits of current therapies. Hence, it is imperative to identify new therapeutic agents to increase treatment efficacy. One of CRC's most promising immunotherapeutic targets is programmed death-1 (PD-1), a cell surface receptor that regulates immune responses. In this paper, we provide an overview of the therapeutic impact of PD-1 in the treatment of CRC. Cancer cells can exploit the PD-1 pathway by upregulating its programmed death-ligand 1 (PD-L1) ligand to evade immune surveillance. The binding of PD-L1 to PD-1 inhibits T cell function, leading to tumor immune escape. PD-1 inhibitors, such as pembrolizumab and nivolumab, block the PD-1/PD-L1 interaction. Clinical trials evaluating PD-1 inhibitors in advanced CRC have shown promising results. In patients with microsatellite instability-high (MSI-H) or mismatch repair-deficient (dMMR) tumors characterized by high mutation rates and increased immunogenicity, PD-1 blockade has demonstrated remarkable efficacy. As a result, pembrolizumab and nivolumab have received accelerated approval by regulatory authorities for the treatment of MSI-H/dMMR metastatic CRC. Additionally, combination approaches, such as combining PD-1 inhibitors with other immunotherapies or targeted agents, are being explored. Despite the success of PD-1 inhibitors in CRC, challenges still exist. Immune-related adverse events can occur and require close monitoring. In conclusion, PD-1 inhibitors have demonstrated significant therapeutic impact, particularly in patients with MSI-H/dMMR tumors.

The Therapeutic Application of Hydrogen in Cancer: The Potential and Challenges.

Hydrogen therapy has emerged as a possible approach for both preventing and treating cancer. Cancers are often associated with oxidative stress and chronic inflammation. Hydrogen, with its unique physiological functions and characteristics, exhibits antioxidant, anti-inflammatory, and anti-apoptotic properties, making it an attractive candidate for cancer treatment. Through its ability to mitigate oxidative damage, modulate inflammatory responses, and sustain cellular viability, hydrogen demonstrates significant potential in preventing cancer recurrence and improving treatment outcomes. Preclinical studies have shown the efficacy of hydrogen therapy in several cancer types, highlighting its ability to enhance the effectiveness of conventional treatments while reducing associated side effects. Furthermore, hydrogen therapy has been found to be safe and well-tolerated in clinical settings. Nonetheless, additional investigations are necessary to improve a comprehensive understanding of the mechanisms underlying hydrogen's therapeutic potential and refine the administration and dosage protocols. However, further clinical trials are still needed to explore its safety profile and capacity. In aggregate, hydrogen therapy represents an innovative and promising treatment for several malignancies.

The prognostic, diagnostic, and therapeutic impact of Long noncoding RNAs in gastric cancer.

Gastric cancer (GC), ranking as the third deadliest cancer globally, faces challenges of late diagnosis and limited treatment efficacy. Long non-coding RNAs (lncRNAs) emerge as valuable treasured targets for cancer prognosis, diagnosis, and therapy, given their high specificity, convenient non-invasive detection in body fluids, and crucial roles in diverse physiological and pathological processes. Research indicates the significant involvement of lncRNAs in various aspects of GC pathogenesis, including initiation, metastasis, and recurrence, underscoring their potential as novel diagnostic and prognostic biomarkers, as well as therapeutic targets for GC. Despite existing challenges in the clinical application of lncRNAs in GC, the evolving landscape of lncRNA molecular biology holds promise for advancing the survival and treatment outcomes of gastric cancer patients. This review provides insights into recent studies on lncRNAs in gastric cancer, elucidating their molecular mechanisms and exploring the potential clinical applications in GC.

Interaction of High- and Low-Risk Human Papillomavirus Genotypes is Associated with a Reduced Risk of Developing Cervical Cancer.

INTRODUCTION: Cervical cancer is among the most common types of cancer in women and is associated with human papillomavirus (HPV) infection. The association between cervical cancer and high-risk HPV infection has been well documented. However, the effect of simultaneous infection with high- and low-risk HPV or low-risk HPV alone on the risk of developing cervical malignancy remains unanswered in guidelines. METHOD: We investigated the association of high and low-risk HPVs (HR or LR) genotypes with cervical carcinoma risk and pathological and cytological information in cases recruited from a population-based cohort study of 790 patients. Correlation matrix and t-test were used for analysis. RESULTS: The percentage of HR+LR and HR-HPV16/18 were 9.30% and 11.20% in class II, 7.15% and 7.10% in class IV, and 7.15% and 5.80% in As-CUS smears. Interestingly, concurrent infection with HR-HPV and LR-HPV types led to a significant reduction in the risk of developing malignancy compared to the high-risk group (OR=0.3 (0.098-0.925), pvalue=0.04). The percentage of individuals with cervical malignancy was 10.2% and 28.2% within the co-infected and the HR-HPV participants. CONCLUSION: Our findings suggest that simultaneous infection with high- and low-risk HPV may reduce the risk of cervical malignancy.

Breast cancer prediction using different machine learning methods applying multi factors.

OBJECTIVE: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS: The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT: Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION: Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

Association of dietary intake and cervical cancer: a prevention strategy.

INTRODUCTION: Cervical cancer is one of lethal cancers in women. As a global concern, identifying important factors of cancer is a useful strategy for prevention. Due to the role of diet/nutrition factors for cancer, the purpose of our study was to determine the impact of 150 nutrition/vitamin factors and 50 non-nutritional factor in cervical cancer and phase. METHODS: Population samples of 2088 healthy subjects and patients with cervical cancer were investigated. 200 factors such as vitamin E, B1, B6, fruits, HPV, and age were gathered. Deep learning, Decision tree, and correlation matrix were used for modeling and identifying important factors. SPSS 26, R4.0.3, and Rapid miner were utilized for implementation. RESULTS: Our findings indicated that zinc, Iron, Niacin, Potassium, Phosphorous, and Cooper have a beneficial impact in reducing the risk of cervical cancer and progression of phase in Iranian women, as well as Salt, snacks and milk Were identified as high-risk food factors (P value < 0.05 and coefficient correlation > 0.6). Also, alcohol, and sex patient with two groups, HPV positive have an impact on cervical cancer incidence. Phosphorus and selenium in the Micronutrients category (R2 = 0.85, AUC = 0.993) and polyunsaturated fatty acid and salt in the Macronutrients category and other categories of nutrients were identified as the most effective factors in cervical cancer using deep learning (R2 = 0.93, AUC = 0.999). CONCLUSIONS: A diet and rich nutrition can be helpful for the prevention of cervix cancer and may reduce the risk of disease. Additional research is necessary for different countries.

Prognostic value of primary tumor location in colorectal cancer: an updated meta-analysis.

The clinical, histological, and molecular differences between right-sided colon cancer (RCC) and left-sided colon cancer (RCC) have received considerable attention. Over the past decade, many articles have been published concerning the association between primary tumor location (PTL) of colorectal cancer and survival outcomes. Therefore, there is a growing need for an updated meta-analysis integrating the outcomes of recent studies to determine the prognostic role of right vs left-sidedness of PTL in patients with colorectal cancer. We conducted a comprehensive database review using PubMed, SCOPUS, and Cochrane library databases from February 2016 to March 2023 for prospective or retrospective studies reporting data on overall survival (OS) and cancer-specific survival (CSS) of RCC compared with LCC. A total of 60 cohort studies comprising 1,494,445 patients were included in the meta-analysis. We demonstrated that RCC is associated with a significantly increased risk of death compared with LCC by 25% (hazard ratio (HR), 1.25; 95% confidence interval (CI), 1.19-1.31; I2 = 78.4%; Z = 43.68). Results showed that patients with RCC have a worse OS compared with LCC only in advanced stages (Stage III: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%; Stage IV: HR, 1.34; 95% CI 1.25-1.44; P < 0.0001; I2 = 69.2%) but not in primary stages (Stage I/II: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%). Moreover, a meta-analysis of 13 studies including 812,644 patients revealed that there is no significant difference in CSS between RCC and LCC (HR, 1.121; 95% CI 0.97-1.3; P = 0.112). Findings from the present meta-analysis highlight the importance of PTL in clinical decision-making for patients with CRC, especially in advanced stages. We provide further evidence supporting the hypothesis that RCC and LCC are distinct disease entities that should be managed differently.

Circulating Tumor Cells and Cell-free Nucleic Acids as Biomarkers in Colorectal Cancer.

Colorectal cancer (CRC) is currently the second most prevalent cancer diagnosed in women and the third most common kind of cancer in men. Despite tremendous efforts and advancements in diagnostic approaches and treatment options, the mortality rate of CRC accounts for around one million each year globally. The five-year survival rate of CRC is reported to be approximately 14 percent for patients diagnosed at an advanced stage. Due to its significant associated mortality and morbidity, diagnostic tools to identify the disease at its early stages are urgently required. Early diagnosis may lead to better outcomes. The gold standard approach for CRC diagnosis is colonoscopy with biopsy. However, it is an invasive process with a risk of complications and discomfort for the patient. Moreover, it is usually performed in symptomatic or high-risk individuals and therefore, asymptomatic patients might be missed. Thus, alternative non-invasive diagnostic techniques are required to improve CRC outcomes. The new era of personalized medicine is identifying novel biomarkers associated with overall survival and clinical outcomes. Recently, liquid biopsy, a minimally invasive analysis of body fluid biomarkers, has gained attention for diagnosis, evaluation of prognosis, and follow-up of patients with CRC. Several previous studies have demonstrated that this novel approach allows for better understanding of CRC tumor biology and leads to an improvement in clinical outcomes. Here, we explain the enrichment and detection methods of circulating biomarkers, including CTCs, ctDNA, miRNA, lncRNA, and circRNA. Furthermore, we provide an overview on their clinical potential as diagnostic, prognostic, and predictive biomarkers for CRC.

Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths. The five-year relative survival rate for CRC is estimated to be approximately 90% for patients diagnosed with early stages and 14% for those diagnosed at an advanced stages of disease, respectively. Hence, the development of accurate prognostic markers is required. Bioinformatics enables the identification of dysregulated pathways and novel biomarkers. RNA expression profiling was performed in CRC patients from the TCGA database using a Machine Learning approach to identify differential expression genes (DEGs). Survival curves were assessed using Kaplan-Meier analysis to identify prognostic biomarkers. Furthermore, the molecular pathways, protein-protein interaction, the co-expression of DEGs, and the correlation between DEGs and clinical data have been evaluated. The diagnostic markers were then determined based on machine learning analysis. The results indicated that key upregulated genes are associated with the RNA processing and heterocycle metabolic process, including C10orf2, NOP2, DKC1, BYSL, RRP12, PUS7, MTHFD1L, and PPAT. Furthermore, the survival analysis identified NOP58, OSBPL3, DNAJC2, and ZMYND19 as prognostic markers. The combineROC curve analysis indicated that the combination of C10orf2 -PPAT- ZMYND19 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.98, 1.00, and 0.99, respectively. Eventually, ZMYND19 gene was validated in CRC patients. In conclusion, novel biomarkers of CRC have been identified that may be a promising strategy for early diagnosis, potential treatment, and better prognosis.

Extracellular vesicles: Emerging mediators of cell communication in gastrointestinal cancers exhibiting metabolic abnormalities.

There is a complex interaction between pro-tumoural and anti-tumoural networks in the tumour microenvironment (TME). Throughout tumourigenesis, communication between malignant cells and various cells of the TME contributes to metabolic reprogramming. Tumour Dysregulation of metabolic pathways offer an evolutional advantage in the TME and enhance the tumour progression, invasiveness, and metastasis. Therefore, understanding these interactions within the TME is crucial for the development of innovative cancer treatments. Extracellular vesicles (EVs) serve as carriers of various materials that include microRNAs, proteins, and lipids that play a vital role in the communication between tumour cells and non-tumour cells. EVs are actively involved in the metabolic reprogramming process. This review summarized recent findings regarding the involvement of EVs in the metabolic reprogramming of various cells in the TME of gastrointestinal cancers. Additionally, we highlight identified microRNAs involved in the reprogramming process in this group of cancers and explained the abnormal tumour metabolism targeted by exosomal cargos as well as the novel potential therapeutic approaches.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer.

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Therapeutic Potential of Herbal Medicine against Non-alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder associated with obesity, diabetes mellitus, dyslipidemia, and cardiovascular disease. A "multiple hit" model has been a widely accepted explanation for the disease's complicated pathogenesis. Despite advances in our knowledge of the processes underlying NAFLD, no conventional pharmaceutical therapy exists. The only currently approved option is to make lifestyle modifications, such as dietary and physical activity changes. The use of medicinal plants in the treatment of NAFLD has recently gained interest. Thus, we review the current knowledge about these agents based on clinical and preclinical studies. Moreover, the association between NAFLD and colorectal cancer (CRC), one of the most common and lethal malignancies, has recently emerged as a new study area. We overview the shared dysregulated pathways and the potential therapeutic effect of herbal medicines for CRC prevention in patients with NAFLD.