Comprehensive assessments of germline deletion structural variants reveal the association between prognostic MUC4 and CEP72 deletions and immune response gene expression in colorectal cancer patients

Effect of colorectal cancer stem cells on the development and metastasis of colorectal cancer

The relevant mechanism of tumor-associated macrophages (TAMs) in the treatment of colorectal cancer patients with immune checkpoint inhibitors (ICIs) is discussed, and the application prospects of TAMs in reversing the treatment tolerance of ICIs are discussed to provide a reference for related studies. As a class of drugs widely used in clinical tumor immunotherapy, ICIs can act on regulatory molecules on cells that play an inhibitory role - immune checkpoints - and kill tumors in the form of an immune response by activating a variety of immune cells in the immune system. The sensitivity of patients with different types of colorectal cancer to ICI treatment varies greatly. The phenotype and function of TAMs in the colorectal cancer microenvironment are closely related to the efficacy of ICIs. ICIs can regulate the phenotypic function of TAMs, and TAMs can also affect the tolerance of colorectal cancer to ICI therapy. TAMs play an important role in ICI resistance, and making full use of this target as a therapeutic strategy is expected to improve the immunotherapy efficacy and prognosis of patients with colorectal cancer.

SGSM2 in Uveal Melanoma: Implications for Survival, Immune Infiltration, and Drug Sensitivity

BACKGROUND

The abnormal expression of small G protein signaling modulator 2 (SGSM2) is related to the occurrence of thyroid cancer and breast cancer. However, the role of SGSM2 in uveal melanoma (UVM) is unclear.

OBJECTS

To elucidate this ambiguity, our study utilized bioinformatics analysis and experimental validation.

METHODS

The expression of SGSM2 was detected in UVM cell lines through quantitative real-- time PCR (qRT-PCR). We utilized the Cancer Genome Atlas (TCGA) database to assess the relationship between SGSM2 expression and clinical characteristics, as well as its prognostic significance in UVM. Furthermore, the study examined potential regulatory networks involving SGSM2 in relation to immune infiltration, immune checkpoint genes, microsatellite instability (MSI), and drug sensitivity in UVM. The study also examined SGSM2 expression in UVM single-cell sequencing data.

RESULTS

SGSM2 was highly expressed in UVM cell lines. Moreover, elevated levels of SGSM2 in UVM patients were significantly linked to poorer overall survival (OS) (p < 0.001), progress- free survival (PFS) (p < 0.001), and disease-specific survival (DSS) (p < 0.001). Additionally, SGSM2 expression was identified as an independent prognostic factor in UVM patients (p < 0.001). SGSM2 was associated with several pathways, including the calcium signaling pathway, natural killer cell-mediated cytotoxicity, cell adhesion molecules (CAMs), and others. The study revealed that SGSM2 expression in UVM is linked to immune infiltration, immune checkpoint genes, and MSI. Additionally, a significant inverse correlation was observed between SGSM2 expression and the compounds GSK690693, TL-2-105, PHA-793887, Tubastatin A, and SB52334 in UVM patients.

CONCLUSION

SGSM2 may not only serve as an important indicator for prognostic assessment. Still, it may also be a key target for the development of new therapeutic approaches, providing new perspectives on the treatment of UVM patients.

Genetic variants of MUC4 are associated with susceptibility to and mortality of colorectal cancer and exhibit synergistic effects with LDL-C levels

As a disease with high mortality and prevalence rates worldwide, colorectal cancer (CRC) has been thoroughly investigated. Mucins are involved in the induction of CRC and the regulation of intestinal homeostasis but a member of the mucin gene family MUC4 has a controversial role in CRC. MUC4 has been associated with either decreased susceptibility to or a worse prognosis of CRC. In our study, the multifunctional aspects of MUC4 were elucidated by genetic polymorphism analysis in a case-control study of 420 controls and 464 CRC patients. MUC4 rs1104760 A>G polymorphism had a protective effect on CRC risk (AG, AOR = 0.537; GG, AOR = 0.297; dominant model, AOR = 0.493; recessive model, AOR = 0.382) and MUC4 rs2688513 A>G was associated with an increased mortality rate of CRC (5 years, GG, adjusted HR = 6.496; recessive model, adjusted HR = 5.848). In addition, MUC4 rs1104760 A>G showed a high probability of being a potential biomarker for CRC patients with low-density lipoprotein cholesterol (LDL-C) in the risk range while showing a significant synergistic effect with the LDL-C level. This is the first study to indicate a significant association between MUC4 genetic polymorphisms and CRC prevalence, suggesting a functional genetic variant with the LDL-C level, for CRC prevention.

Identification of differentially expressed HERV-K(HML-2) loci in colorectal cancer

Colorectal cancer is one of the malignant tumors with the highest mortality rate in the world. Survival rates vary significantly among patients at various stages of the disease. A biomarker capable of early diagnosis is required to facilitate the early detection and treatment of colorectal cancer. Human endogenous retroviruses (HERVs) are abnormally expressed in various diseases, including cancer, and have been involved in cancer development. Real-time quantitative PCR was used to detect the transcript levels of HERV-K(HML-2) gag, pol, and env in colorectal cancer to systematically investigate the connection between HERV-K(HML-2) and colorectal cancer. The results showed that HERV-K(HML-2) transcript expression was significantly higher than healthy controls and was consistent at the population and cell levels. We also used next-generation sequencing to identify and characterize HERV-K(HML-2) loci that were differentially expressed between colorectal cancer patients and healthy individuals. The analysis revealed that these loci were concentrated in immune response signaling pathways, implying that HERV-K impacts the tumor-associated immune response. Our results indicated that HERV-K might serve as a screening tumor marker and a target for tumor immunotherapy in colorectal cancer.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.