Pancreatic cancer (PC) remains a formidable challenge in oncology due to its notoriously poor prognosis, often resulting from late-stage diagnosis. Early detection through effective screening methods is crucial not only to improving patient outcomes but also to enhancing their quality of life. This review focuses on the latest advancements in PC screening and early diagnostic strategies. Key areas include the integration of artificial intelligence in radiology, the search for novel biomarkers, and the development of predictive models. This review aimed to provide a comprehensive overview, serving as a stepping stone toward transforming early detection strategies for PC in the digital age.

The incidence of cardia gastric cancer (CGC) is rising worldwide, particularly in East Asia. There has been a debate over whether Helicobacter pylori (H. pylori) constitutes a risk factor for CGC. This study aimed to evaluate the relative risk of H. pylori infection and CGC in Asian countries.

Relevant studies examining H. pylori and CGC were searched in PubMed, Embase, and Web of Science from their inception to June 30, 2024. Either a random-effect model or a fixed-effect model was used to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs). Sensitivity analyses and assessments of publication bias were performed. The stability of results was evaluated in cases where publication bias was detected.

A total of 24 studies were included in the meta-analysis. A significant association between H. pylori and CGC was observed (OR = 2.20, 95% CI 1.73–2.80). In a subgroup analysis of different countries, a significant association was observed in East Asian countries, including China (OR = 2.12, 95% CI 1.63–2.77), Japan (OR = 2.21, 95% CI 1.16–4.20), and Korea (OR = 2.36, 95% CI 1.58–3.54), but not in Iran (OR = 1.48, 95% CI 0.77–2.84). The pooled OR from five prospective cohort studies revealed a strong association between H. pylori and CGC (OR = 2.32, 95% CI 1.47–3.66).

East Asia bears a significant burden of H. pylori-related CGC. A clear association between H. pylori infection and CGC was observed in this region.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with difficulties in early diagnosis, poor prognosis, and limited effective therapies. Early detection and effective treatment offer the optimal chance to improve survival rates. Various studies have shown that gut microbiota dysbiosis is closely related to PDAC, with potential mechanism involving immune regulation, metabolic process impact, and reshaping the tumor microenvironment. A comprehensive understanding of the microbiota in PDAC might lead to the establishment of screening or early-stage diagnosis methods, implementation of cancer bacteriotherapy such as fecal microbiota transplantation, creating new opportunities and fostering hope for desperate PDAC patients.

DNA methylation is essential for regulating tissue-specific gene expression, genomic imprinting, X chromosome inactivation and retroviral element silencing. The transformation from normal to cancer cells is accompanied by changes in DNA methylation resulting in the activation of oncogenes and inactivation of tumor suppressor genes. This process is regulated by methylation and contributes to the support and development of tumors. Epigenetic modifications account for the development of resistance in cancer cells treated with anticancer drugs. Dysregulated signaling pathways involved in tumor drug resistance include the Wnt canonical and non-canonical pathways and the PI3K/PTEN/AKT/mTOR pathway. This review considers the mechanisms and specific methylated biomarkers that participate in such resistances and how resistance to individual treatments for breast, ovarian, uterine and cervix tumors are introduced.

Cancer is a hereditary multifactorial disease, and due to its rising incidence in both young people and adults along with its substantial burden, oncological emphasis has been placed more on preventive efforts. It has been suggested that several food and lifestyle choices contribute to the onset of cancer, presumably via complex metabolic and inflammatory pathways. Diet is one of the crucial variables in determining cancer risk. In addition, as research intensifies, a more distinct link between diet and patients’ molecular alterations is emerging and becoming quantifiable, dispelling the previous conventional wisdom that linked phenotypic changes to dietary variation. Although the evidence is not consistent, appropriate doses of vitamin B12, vitamin D, vitamin C, selenium, folic acid, and antioxidants such as carotenoids have shown a preventive effect in certain types of cancer. However, improper use of dietary supplements in well-nourished people provides no effects or even poses harmful effects to increase the risk of some cancer. Contrarily, other factors like alcohol, obesity, certain fatty acids, and some techniques used for food preparation may increase the risk of cancer. It is now appropriate to make dietary modifications that are consistent with suggestions for preventing cancer incidence with an emphasis on lifestyle improvement including proper management of problems associated with diet, nutrition, smoking, and drinking. However, there is currently a need for more clinical research to demonstrate the safety and effectiveness of using various phytochemicals or plant extracts as dietary supplements to prevent primary stages of cancer.

In recent years, miRNAs have been shown to play an important role in many diseases, most notably cancers. Traditional experiments for cancer diagnostics are time-consuming and expensive, thus more attention is being diverted towards discovering computational methods for predicting miRNA–disease association and using them in cancer diagnostics.

miRNA numerical sequence information and genes targeted by miRNA were used for the construction of descriptors for machine-learning models. Next, we generated a table of miRNA descriptors using all of the miRNAs in a specific cancer dataset for disease classification. To show the effectiveness of the system, we constructed miRNA descriptor systems for pancreatic cancer, lung cancer, and breast cancer.

With the Random Forest classifier, we obtained classification accuracies of 86.9%, 86.3%, and 85.1% for the above-mentioned cancers, respectively. Next, different disease datasets were tested on each model, including new miRNA sets for each cancer type from other studies. The models were able to classify the corresponding cancer miRNAs with >90% accuracy and other disease and cancer datasets with <60% accuracy.

With this information, we constructed a hard-voting scheme using the three cancer classification models that is able to perform cancer diagnostics. The results suggest that our method is effective in miRNA–disease association prediction and performing cancer diagnostics.