Single-cell analysis uncovers high-proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer.

Gastric cancer (GC) remains a prominent malignancy that poses a significant threat to human well-being worldwide. Despite advancements in chemotherapy and immunotherapy, which have effectively augmented patient survival rates, the mortality rate associated with GC remains distressingly high. This can be attributed to the elevated proliferation and invasive nature exhibited by GC. Our current understanding of the drivers behind GC cell proliferation remains limited. Hence, in order to reveal the molecular biological mechanism behind the swift advancement of GC, we employed single-cell RNA-sequencing (scRNA-seq) to characterize the tumour microenvironment in this study. The scRNA-seq data of 27 patients were acquired from the Gene Expression Omnibus database. Differential gene analysis, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis were employed to investigate 38 samples. The copy number variation level exhibited by GC cells was determined using InferCNV. The CytoTRACE, Monocle and Slingshot analysis were used to discern the cellular stemness and developmental trajectory of GC cells. The CellChat package was utilized for the analysis of intercellular communication crosstalk. Moreover, the findings of the data analysis were validated through cellular functional tests conducted on the AGS cell line and SGC-7901 cell line. Finally, this study constructed a risk scoring model to evaluate the differences of different risk scores in clinical characteristics, immune infiltration, immune checkpoints, functional enrichment, tumour mutation burden and drug sensitivity. Within the microenvironment of GC, we identified the presence of 8 cell subsets, encompassing NK_T cells, B_Plasma cells, epithelial cells, myeloid cells, endothelial cells, mast cells, fibroblasts, pericytes. By delving deeper into the characterization of GC cells, we identified 6 specific tumour cell subtypes: C0 PSCA+ tumour cells, C1 CLDN7+ tumour cells, C2 UBE2C+ tumour cells, C3 MUC6+ tumour cells, C4 CHGA+ tumour cells and C5 MUC2+ tumour cells. Notably, the C2 UBE2C+ tumour cells demonstrated a close association with cell mitosis and the cell cycle, exhibiting robust proliferative capabilities. Our findings were fortified through enrichment analysis, pseudotime analysis and cell communication analysis. Meanwhile, knockdown of the transcription factor CREB3, which is highly active in UBE2C+ tumour cells, significantly impedes the proliferation, migration and invasion of GC cells. And the prognostic score model constructed with CREB3-related genes showcased commendable clinical predictive capacity, thus providing valuable guidance for patients' prognosis and clinical treatment decisions. We have identified a highly proliferative cellular subgroup C2 UBE2C+ tumour cells in GC for the first time. The employment of a risk score model, which is based on genes associated with UBE2C expression, exhibits remarkable proficiency in predicting the prognosis of GC patients. In our investigation, we observed that the knockdown of the transcription factor CREB3 led to a marked reduction in cellular proliferation, migration and invasion in GC cell line models. Implementing a stratified treatment approach guided by this model represents a judicious and promising methodology.

Genetic association of circulating interleukins and risk of colorectal cancer: A bidirectional Mendelian randomization study.

BACKGROUND: Previous studies have reported that inflammation, especially interleukin family members, plays an important role in the development of colorectal cancer (CRC). However, because of various confounders and the lack of clinical randomized controlled trial, the causal relationship between genetically predicted level of interleukin family and CRC risk has not been fully explained. OBJECTIVE: Bi-directional Mendelian randomization (MR) was conducted to investigate the causal association between interleukin family members and CRC. METHODS: Several genetic variables were extracted as instrumental variables (IVs) from summary data of genome-wide association studies (GWAS) for interleukin and CRC. IVs of interleukin family were obtained from recently published GWAS studies and the summary data of CRC was from FinnGen Biobank. After a series of quality control measures and strict screening, six models were used to evaluate the causal relationship. Pleiotropy, heterogeneity test, and a variety of sensitivity analysis were also used to estimate the robustness of the model results. RESULTS: Genetically predicted higher circulating levels of IL-2 (odds ratio [OR]: 0.76; 95% confidence interval [CI]: 0.63-0.92; p = .0043), IL-17F(OR: 0.78; 95% CI: 0.62-1.00; p = .015), and IL-31 (OR: 0.88; 95% CI: 0.79-0.98; p = .023) were suggestively associated with decreased CRC risk. However, higher level of IL-10 (OR: 1.40; 95% CI: 1.18-1.65; p = .000094) was causally associated with increased risk of CRC. Reverse MR results indicated that the exposure of CRC was suggestively associated with higher levels of IL-36α (OR: 1.23; 95% CI: 1.01-1.49; p = .040) and IL-17RD (OR: 1.22; 95% CI, 1.00-1.48; p = .048) and lower level of IL-13 (OR: 0.78; 95% CI: 0.65-0.95; p = .013). The overall MR results did not provide evidence for causal relationships between other interleukins and CRC (p > .05). CONCLUSION: We offer suggestive evidence supporting a potential causal relationship between circulating interleukins and CRC, underscoring the significance of targeting circulating interleukins as a strategy to mitigate the incidence of CRC.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

TRIM28 Regulates Proliferation of Gastric Cancer Cells Partly Through SRF/IDO1 Axis.

Background: Gastric cancer (GC) is one of the most common malignancies worldwide, with high incidence and mortality rate. Tripartite motif-containing 28 (TRIM28) is an important molecule that affects the occurrence and development of tumors, but its function in GC has not been elucidated clearly. The purpose of this study is to explore the molecular mechanism by which TRIM28 affect the GC. Methods: TRIM28 expression was tested in RNA-seq data from TCGA database, tumor tissue samples from patients and GC cell lines. Genes were silenced or overexpressed by siRNA, lentivirus-mediated shRNA, or plasmids. Cell Counting Kit-8 (CCK-8) and colony formation assays were performed to explore the proliferation of GC cells after TRIM28 knockdown. RNA-seq and TCGA database were used to identify target genes. Luciferase report assay was employed to detect the possible mechanism between TRIM28 and Indoleamine 2,3-dioxygenase (IDO1). Tryptophan concentration in cell supernatant was measured using a fluorometric assay kit. MGC-803 and 746T cells were injected into mice to establish xenograft animal models. Results: The expression of TRIM28 was positively correlated with tumor size and poorer prognosis. Upregulation of TRIM28 was observed in GC tissues and cells. In vitro, we proved that knockdown of TRIM28 significantly inhibited the proliferation of GC cells. Then TRIM28 was found to be positively correlated with the expression of IDO1 in GC cells. In accordance with this, tryptophan levels in cell supernatants were increased in TRIM28 knockdown GC cells and overexpression of IDO1 could reverse this phenotype. Serum response factor (SRF), a reported regulator of IDO1, was also regulated by TRIM28 in GC cells. And decreased expression of IDO1 induced by TRIM28 knockdown could be partly reversed through overexpression of serum response factor (SRF) in GC cells. Functional research demonstrated that the expression of IDO1 was increased in GC and IDO1 knockdown could also inhibited the proliferation of GC cells. Furthermore, overexpression of IDO1 could partly reverse proliferation inhibited by TRIM28 knockdown in GC cells. In vivo, knockdown of TRIM28 significantly inhibited the tumor growth and overexpression of IDO1 and SRF both could reverse proliferation inhibited by TRIM28 knockdown. Conclusions: TRIM28 is crucial in the development of GC, and may regulate IDO1 through SRF. TRIM28 promote GC cell proliferation through SRF/IDO1 axis.

Development and Validation of a Prognostic Model based on 11 E3-related Genes for Colon Cancer Patients.

BACKGROUND: Colon cancer is a common tumor in the gastrointestinal tract with a poor prognosis. According to research reports, ubiquitin-dependent modification systems have been found to play a crucial role in the development and advancement of different types of malignant tumors, including colon cancer. However, further investigation is required to fully understand the mechanism of ubiquitination in colon cancer. METHODS: We collected the RNA expression matrix of the E3 ubiquitin ligase-related genes (E3RGs) from the patients with colon adenocarcinoma (COAD) using The Cancer Genome Atlas program (TCGA). The "limma" package was used to obtain differentially expressed E3RGs between COAD and adjacent normal tissues. Then, univariate COX regression and least absolute shrinkage and selection operator (LASSO) analysis were performed to construct the prognostic signature and nomogram model. Afterward, we used the original copy number variation data of COAD to find potential somatic mutation and employed the "pRRophetic" package to investigate the disparity in the effectiveness of chemotherapy drugs between high and low-risk groups. The RT-qPCR was also implied to detect mRNA expression levels in tumor tissues. RESULTS: A total of 137 differentially expressed E3RG3 were screened and 11 genes (CORO2B, KCTD9, RNF32, BACH2, RBCK1, DPH7, WDR78, UCHL1, TRIM58, WDR72, and ZBTB18) were identified for the construction of prognostic signatures. The Kaplan-Meier curve showed a worse prognosis for patients with high risk both in the training and test cohorts (P = 1.037e-05, P = 5.704e-03), and the area under the curve (AUC) was 0.728 and 0.892 in the training and test cohorts, respectively. Based on the stratified analysis, this 11- E3RGs signature was a novel and attractive prognostic model independent of several clinicopathological parameters (age, sex, stage, TNM) in COAD. The DEGs were subjected to GO and KEGG analysis, which identified pathways associated with cancer progression. These pathways included the cAMP signaling pathway, calcium signaling pathway, Wnt signaling pathway, signaling pathways regulating stem cell pluripotency, and proteoglycans in cancer. Additionally, immune infiltration analysis revealed significant differences in the infiltration of macrophages M0, T cells follicular helper, and plasma cells between the two groups. CONCLUSION: We developed a novel independent risk model consisting of 11 E3RGs and verified the effectiveness of this model in test cohorts, providing important insights into survival prediction in COAD and several promising targets for COAD therapy.