Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide, driven by a complex interplay of genetic, environmental, and immune-related factors. Among the pivotal pathways implicated in CRC tumorigenesis, the Notch signaling pathway is instrumental in governing cell fate decisions, tissue renewal, homeostasis, and immune cell development. As a highly conserved mechanism, Notch signaling not only modulates tumor cell behavior but also shapes the immune landscape within the tumor microenvironment (TME). Aberrant Notch signaling in CRC fosters immune evasion and tumor progression through its effects on the balance and functionality of immune cells, including myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). Elevated Notch pathway activation correlates with advanced clinicopathological features and poorer clinical outcomes, highlighting its relevance as both a prognostic biomarker and a therapeutic target. Therapeutic approaches aimed at inhibiting the Notch pathway, such as γ-secretase inhibitors (GSIs) or monoclonal antibodies (mAbs) in combination with other therapies, have demonstrated promising efficacy in preclinical and clinical settings. This review examines the impact of Notch signaling on CRC immunity, elucidating its regulatory mechanisms within immune cells and its role in promoting tumor progression. Additionally, this review discusses therapeutic strategies targeting Notch signaling, including GSIs, mAbs, and potential combination therapies designed to overcome resistance and improve patient outcomes. By elucidating the multifaceted role of Notch within the CRC TME, this review underscores its potential as a target for innovative therapeutic strategies.

The long noncoding RNA (lncRNA)/Wingless (Wnt) axis is often dysregulated in digestive system tumors impacting critical cellular processes. Abnormal expression of specific Wnt-related lncRNAs such as LINC01606 (promotes motility), SLCO4A1-AS1 (promotes motility), and SH3BP5-AS1 (induces chemoresistance), plays a crucial role in these malignancies. These lncRNAs are promising targets for cancer diagnosis and therapy, offering new treatment perspectives. The lncRNAs, NEF and GASL1, differentially expressed in plasma show diagnostic potential for esophageal squamous cell carcinoma and gastric cancer, respectively. Additionally, Wnt pathway inhibitors like XAV-939 have demonstrated preclinical efficacy, underscoring their therapeutic potential. This review comprehensively analyzes the lncRNA/Wnt axis, highlighting its impact on cell proliferation, motility, and chemoresistance. By elucidating the complex molecular mechanisms of the lncRNA/Wnt axis, we aim to identify potential therapeutic targets for digestive system tumors to pave the way for the development of targeted treatment strategies.

Cancer is a global health threat, with its treatment modalities transitioning from single therapies to integrated treatments. This paper systematically explores the key technological systems in modern cancer treatment and their application value. Modern cancer treatment relies on four core technological pillars: omics, bioinformatics, network pharmacology (NP), and molecular dynamics (MD) simulation. Omics technologies integrate various biological molecular information, such as genomics, proteomics and metabolomics, providing foundational data support for drug research. But the differences in data and the challenges of integrating it often lead to biased predictions, and that’s a big limitation for this technology. Bioinformatics utilizes computer science and statistical methods to process and analyze biological data, aiding in the identification of drug targets and the elucidation of mechanisms of action. It is important to note that the prediction accuracy largely depends on the algorithm chosen. Consequently, this dependence may affect the reliability of the research results. NP, based on systems biology, studies drug-target-disease networks, revealing the potential for multitargeted therapies. That said, this method may overlook important aspects of biological complexity, such as variations in protein expression. This oversight can lead to overestimating the effectiveness of multi-targeted therapies, resulting in false positives in efficacy assessments, which somewhat limits its practical usefulness. MD simulation examines how drugs interact with target proteins by tracking atomic movements, thus enhancing the precision of drug design and optimization. Nevertheless, this technology faces practical challenges, such as high computational costs and sensitivity of model accuracy to the parameters of the force field. The synergistic application of these technologies significantly shortens the drug development cycle and promotes precision and personalization in cancer therapy, bringing new hope to patients for successful treatment. However, researchers still face challenges like the variability of data. Future efforts need to use Artificial Intelligence (AI) to establish standardized data integration platforms, develop multimodal analysis algorithms, and strengthen preclinical-clinical translational research to drive breakthrough advancements in cancer treatment. With the ongoing technological improvements, the vision of personalized medicine—tailored treatments based on individual patient characteristics—will gradually be realized, significantly enhancing treatment efficacy and improving patients’ quality of life.

The significance of m6A modifications in several biological processes has been increasingly recognized, particularly in the context of cancer. For instance, m6A modifications in gastric cancer (GC) have been significantly implicated in tumor progression, metastasis, and treatment resistance. GC is characterized by the differential expression of m6A regulators. High expression writers such as METTL3 and WTAP are associated with poor prognosis and aggressive clinical features. Conversely, low expression of METTL14 is linked to worse clinical outcomes, whereas elevated levels of demethylases, such as FTO and ALKBH5, correlate with better survival rates. These m6A regulators influence several cellular biological functions, including proliferation, invasion, migration, glycolysis, and chemotherapy resistance, thereby affecting tumor growth and therapeutic outcomes. The assessment of m6A modification patterns and the expression profiles of m6A-related genes hold substantial potential for improving the clinical diagnosis and treatment of GC. In this review, we provide an updated and comprehensive summary of the role of m6A modifications in GC, emphasizing their molecular mechanisms, clinical significance, and translational applications in developing novel diagnostic and therapeutic strategies.

Background: 5-Fluorouracil (5-FU) is a widely used chemotherapeutic drug in clinical cancer treatment, including hepatocellular carcinoma (HCC). A correct understanding of the mechanisms leading to a low or lack of sensitivity of HCC to 5-FU-based treatment is a key element in the current personalized medical treatment.Methods: Weighted gene co-expression network analysis (WGCNA) was used to analyze the expression profiles of the cancer cell line from GDSC2 to identify 5-FU-related modules and hub genes. According to hub genes, HCC was classified and the machine learning model was developed by ConsensusClusterPlus and five different machine learning algorithms. Furthermore, we performed quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis on the genes in our model.Results: A total of 19 modules of the cancer cell line were divided by WGCNA, and the most negative correlation with 5-FU was the midnight blue module, from which 45 hub genes were identified. HCC was divided into three subgroups (C1, C2, and C3) with significant overall survival (OS) differences. OS of C1 was the shortest, which was characterized by a high clinical grade and later T stage and stage. OS of C3 was the longest. OS of C2 was between the two subtypes, and its immune infiltration was the lowest. Five out of 45 hub genes, namely, TOMM40L, SNRPA, ILF3, CPSF6, and NUP205, were filtered to develop a risk regression model as an independent prognostic indicator for HCC. The qRT-PCR results showed that TOMM40L, SNRPA, ILF3, CPSF6, and NUP205 were remarkably highly expressed in hepatocellular carcinoma.Conclusion: The HCC classification based on the sensitivity to 5-FU was in line with the prognostic differences observed in HCC and most of the genomic variation, immune infiltration, and heterogeneity of pathological pathways. The regression model related to 5-FU sensitivity may be of significance in individualized prognostic monitoring of HCC.