CBX2 Deletion Suppresses Growth and Metastasis of Colorectal Cancer by Mettl3-p38/ERK MAPK Signalling Pathway.

Colorectal cancer (CRC) seriously endangers human health owing to its high morbidity and mortality. Previous studies have suggested that high expression of CBX2 may be associated with poor prognosis in CRC patients. However, its functional role in CRC remains to be elucidated. Herein, we found that CBX2 overexpression in colorectal cancer tissue compared with adjacent tissues. Additionally, forest maps and the nomogram model indicated that elevated CBX2 expression was an independent prognostic factor in CRC. Moreover, we confirmed that the deletion of CBX2 markedly suppressed the proliferation and migration of CRC cells in vitro and in vivo. Furthermore, downregulation of CBX2 promotes CRC cell apoptosis and hinders the cell cycle. Mechanistically, our data demonstrated that deletion of CBX2 inhibited the MAPK signaling pathway by regulating the protein levels of Mettl3. In conclusion, our study demonstrated that CBX2 is a vital tumor suppressor in CRC and could be a promising anti-cancer therapeutic target.

Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer.

Background: T cells are crucial components of antitumor immunity. A list of genes associated with T cell proliferation was recently identified; however, the impact of T cell proliferation-related genes (TRGs) on the prognosis and therapeutic responses of patients with colorectal cancer (CRC) remains unclear. Methods: 33 TRG expression information and clinical information of patients with CRC gathered from multiple datasets were subjected to bioinformatic analysis. Consensus clustering was used to determine the molecular subtypes associated with T cell proliferation. Utilizing the Lasso-Cox regression, a predictive signature was created and verified in external cohorts. A tumor immune environment analysis was conducted, and potential biomarkers and therapeutic drugs were identified and confirmed via in vitro and in vivo studies. Results: CRC patients were separated into two TRG clusters, and differentially expressed genes (DEGs) were identified. Patient information was divided into three different gene clusters, and the determined molecular subtypes were linked to patient survival, immune cells, and immune functions. Prognosis-associated DEGs in the three gene clusters were used to evaluate the risk score, and a predictive signature was developed. The ability of the risk score to predict patient survival and treatment response has been successfully validated using multiple datasets. To discover more possible biomarkers for CRC, the weighted gene co-expression network analysis algorithm was utilized to screen key TRG variations between groups with high- and low-risk. CDK1, BATF, IL1RN, and ITM2A were screened out as key TRGs, and the expression of key TRGs was confirmed using real-time reverse transcription polymerase chain reaction. According to the key TRGs, 7,8-benzoflavone was identified as the most significant drug molecule, and MTT, colony formation, wound healing, transwell assays, and in vivo experiments indicated that 7,8-benzoflavone significantly suppressed the proliferation and migration of CRC cells. Conclusion: T cell proliferation-based molecular subtypes and predictive signatures can be utilized to anticipate patient results, immunological landscape, and treatment response in CRC. Novel biomarker candidates and potential therapeutic drugs for CRC were identified and verified using in vitro and in vivo tests.

Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer.

BACKGROUND: A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). METHODS: 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified. RESULTS: The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. CONCLUSION: We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.

Causal Effects of Gut Microbiota on Age-Related Macular Degeneration: A Mendelian Randomization Study.

Purpose: Recently, the association between gut microbiota and age-related macular degeneration (AMD) through the gut-retina axis has attracted great interest. However, the causal relationship between them has not been elucidated. Using publicly available genome-wide association study summary statistics, we conducted a two-sample Mendelian randomization (MR) analysis to examine the causal relationship between the gut microbiota and the occurrence of AMD. Methods: The study used a variety of quality control techniques to select instrumental single nucleotide polymorphisms (SNPs) with strong exposure associations. We used a set of SNPs as instrumental variable that were below the genome-wide statistical significance threshold (5 × 10-8). Additionally, a separate group of SNPs below the locus-wide significance level (1 × 10-5) were selected as instrumental variables to ensure a comprehensive conclusion. Inverse variance-weighted (IVW) analysis was the primary technique we used to examine causality in order to confirm the validity of our findings. The MR-Egger intercept test, Cochran's Q test, and leave-one-out sensitivity analysis were used to evaluate the horizontal pleiotropy, heterogeneities, and stability of the genetic variants. Results: IVW results showed that genus Anaerotruncus (P = 5.00 × 10-3), genus Candidatus Soleaferrea (P = 1.83 × 10-2), and genus unknown id.2071 (P = 3.12 × 10-2) were protective factors for AMD. The Eubacterium oxidoreducens group (P = 3.17 × 10-2), genus Faecalibacterium (P = 2.67 × 10-2), and genus Ruminococcaceae UCG-011 (P = 4.04 × 10-2) were risk factors of AMD. No gut microbiota (GM) taxa were found to be causally related to AMD at the phylum, class, order, and family levels (P > 0.05). The robustness of MR results were confirmed by heterogeneity and pleiotropy analysis. (P > 0.05). We also performed a bidirectional analysis, which showed that genus Anaerotruncus, genus Candidatus Soleaferrea, genus unknown id.2071 and the Eubacterium oxidoreducens group had an interaction with AMD, whereas genus Faecalibacterium showed only a unilateral unfavorable effect on AMD. Conclusions: We confirmed a causal relationship between AMD and GM taxa, including the Eubacterium oxidoreducens group, Faecalibacterium, Ruminococcaceae UCG-011, Anaerotruncus, and Candidatus Soleaferrea. These strains have the potential to serve as new biomarkers, offering valuable insights into the treatment and prevention of AMD.

CTSG Suppresses Colorectal Cancer Progression through Negative Regulation of Akt/mTOR/Bcl2 Signaling Pathway.

Colorectal cancer (CRC) is the most common gastrointestinal tumor worldwide, which is a severe malignant disease that threatens mankind. Cathepsin G (CTSG) has been reported to be associated with tumorigenesis, whereas its role in CRC is still unclear. This investigation aims to determine the function of CTSG in CRC. Our results indicated that CTSG was inhibited in CRC tissues, and patients with CTSG low expression have poor overall survival. Functional experiments revealed that CTSG overexpression suppressed CRC cell progression in vitro and in vivo, whereas CTSG suppression supports CRC development cells in vitro and in vivo. Mechanistically, CTSG overexpression suppressed Akt/mTOR signaling mechanism and elevated apoptotic-associated markers, and CTSG silencing activated Akt/mTOR signaling mechanisms and inhibited apoptotic-associated markers. Furthermore, the Akt suppression signaling pathway by MK2206 abolishes CTSG-silenced expression-induced cell viability and Bcl2 up-regulation in vitro and in vivo. Altogether, these outcomes demonstrate that CTSG may act as a tumor suppressor gene via Akt/mTOR/Bcl2-mediated anti-apoptotic signaling inactivation, and CTSG represents a potential therapeutic target in CRC.

Development and validation of a novel T cell proliferation-related prognostic model for predicting survival and immunotherapy benefits in melanoma.

BACKGROUND: T cell plays a crucial role in the occurrence and progression of Skin cutaneous melanoma (SKCM). This research aims to identify the actions of T cell proliferation-related genes (TRGs) on the prognosis and immunotherapy response of tumor patients. METHOD: The clinical manifestation and gene expression data of SKCM patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. T cell proliferation-related molecular subtypes were identified utilizing consensus clustering. Subsequently, Cox and Lasso regression analysis was conducted to identify six prognostic genes, and a prognostic signature was constructed. A series of experiments, such as qRT-PCR, Western blotting and CCK8 assay, were then conducted to verify the reliability of the six genes. RESULTS: In this study, a grading system was established to forecast survival time and responses to immunotherapy, providing an overview of the tumoral immune landscape. Meanwhile, we identified six prognostic signature genes. Notably, we also found that C1RL protein may inhibit the growth of melanoma cell lines. CONCLUSION: The scoring system depending on six prognostic genes showed great efficiency in predicting survival time. The system could help to forecast prognosis of SKCM patients, characterize SKCM immunological condition, assess patient immunotherapy response.

Identification of cuproptosis-based molecular subtypes, construction of prognostic signature and characterization of immune landscape in colon cancer.

Background: Cuproptosis is a newly discovered form of cell death induced by targeting lipoacylated proteins involved in the tricarboxylic acid cycle. However, the roles of cuproptosis-related genes (CRGs) in the clinical outcomes and immune landscape of colon cancer remain unknown. Methods: We performed bioinformatics analysis of the expression data of 13 CRGs identified from a previous study and clinical information of patients with colon cancer obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Colon cancer cases were divided into two CRG clusters and prognosis-related differentially expressed genes. Patient data were separated into three corresponding distinct gene clusters, and the relationships between the risk score, patient prognosis, and immune landscape were analyzed. The identified molecular subtypes correlated with patient survival, immune cells, and immune functions. A prognostic signature based on five genes was identified, and the patients were divided into high- and low-risk groups based on the calculated risk score. A nomogram model for predicting patient survival was developed based on the risk score and other clinical features. Results: The high-risk group showed a worse prognosis, and the risk score was related to immune cell abundance, microsatellite instability, cancer stem cell index, checkpoint expression, immune escape, and response to chemotherapeutic drugs and immunotherapy. Findings related to the risk score were validated in the imvigor210 cohort of patients with metastatic urothelial cancer treated with anti-programmed cell death ligand 1. Conclusion: We demonstrated the potential of cuproptosis-based molecular subtypes and prognostic signatures for predicting patient survival and the tumor microenvironment in colon cancer. Our findings may improve the understanding of the role of cuproptosis in colon cancer and lead to the development of more effective treatment strategies.

5-Fluorouracil Combined with Rutaecarpine Synergistically Suppresses the Growth of Colon Cancer Cells by Inhibiting STAT3.

Purpose: To evaluate the effect of 5-fluorouracil (5-FU) combined with rutaecarpine (RUT) on the antiproliferative, anti-migratory, and apoptosis-promoting ability of colorectal cancer (CRC) cells and explore the underlying mechanism. Methods: The antiproliferative effects of RUT and 5-FU on CRC cells were evaluated using MTT and colony formation assays. Anti-migration was assessed by cell scratch and transwell tests. The synergistic effect of RUT and 5-FU was assessed by isobologram and combination index analysis using CompuSyn software. The effects of RUT and 5-FU on cell apoptosis were detected by flow cytometry. Differences in protein expression levels with or without RUT and/or 5-FU treatment were assessed by Western blot. Moreover, a mouse xenograft model of CRC was established to investigate the antitumor effect of RUT and 5-FU in vivo, and Ki67 and cleaved caspase-3 expression was detected by immunofluorescence. Results: In this study, we found that 5-FU combined with RUT can inhibit the proliferative, migratory, and antiapoptotic abilities of CRC cells to a significantly greater extent than either RUT or 5-FU alone both in vivo and in vitro. Western blot analysis showed that the level of signal transducer and activator of transcription 3 (STAT3) phosphorylation in CRC cells was significantly reduced after combination therapy compared with that seen with the respective monotherapies. In addition, combination therapy influenced the STAT3 signaling pathway, namely, it inhibited the expression of c-Myc, CDK4, and Bcl-2 while enhancing that of the proapoptotic protein cleaved caspase-3. Immunofluorescence staining further showed that the expression of Ki67 and cleaved caspase-3 was significantly downregulated and upregulated, respectively, in tumor tissues of mice treated with combination therapy compared with that observed with 5-FU treatment alone. Conclusion: Combined therapy with 5-FU and RUT exerted a superior curative effect in CRC than treatment with either single drug alone and has potential as a novel therapeutic modality for the treatment of CRC.

Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning.

Ulcerative colitis (UC) is an inflammatory bowel disease of unknown cause that typically affects the colon and rectum. Innate intestinal immunity, including macrophages, plays a significant role in the pathological development of UC. Using the CIBERSORT algorithm, we observed elevated levels of 22 types of immune cell infiltrates, as well as increased M1 and decreased M2 macrophages in UC compared to normal colonic mucosa. Weighted gene coexpression network analysis (WGCNA) was used to identify modules associated with macrophages and UC, resulting in the identification of 52 macrophage-related genes (MRGs) that were enriched in macrophages at single-cell resolution. Consensus clustering based on these 52 MRGs divided the integrated UC cohorts into three subtypes. Machine learning algorithms were used to identify ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (SLC6A14), and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) in the training set, and their diagnostic value was validated in independent validation sets. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) revealed the main biological effects, and that interleukin-17 was one of several signaling pathways enriched by the three genes. We also constructed a competitive endogenous RNA (CeRNA) network reflecting a potential posttranscriptional regulatory mechanism. Expression of diagnostic markers was validated in vivo and in biospecimens, and our immunohistochemistry (IHC) results confirmed that HMGCS2 gradually decreased during the transformation of UC to colorectal cancer. In conclusion, ENPP1, SLC6A14, and HMGCS2 are associated with macrophages and the progression of UC pathogenesis and have good diagnostic value for patients with UC.

A novel oxidative stress- and ferroptosis-related gene prognostic signature for distinguishing cold and hot tumors in colorectal cancer.

Oxidative stress and ferroptosis exhibit crosstalk in many types of human diseases, including malignant tumors. We aimed to develop an oxidative stress- and ferroptosis-related gene (OFRG) prognostic signature to predict the prognosis and therapeutic response in patients with colorectal cancer (CRC). Thirty-four insertion genes between oxidative stress-related genes and ferroptosis-related genes were identified as OFRGs. We then performed bioinformatics analysis of the expression profiles of 34 OFRGs and clinical information of patients obtained from multiple datasets. Patients with CRC were divided into three OFRG clusters, and differentially expressed genes (DEGs) between clusters were identified. OFRG clusters correlated with patient survival and immune cell infiltration. Prognosis-related DEGs in three clusters were used to calculate the risk score, and a prognostic signature was constructed according to the risk score. In this study, patients in the low-risk group had better prognosis, higher immune cell infiltration levels, and better responses to fluorouracil-based chemotherapy and immune checkpoint blockade therapy than high-risk patients; these results were successfully validated with multiple independent datasets. Thus, low-risk CRC could be defined as hot tumors and high-risk CRC could be defined as cold tumors. To further identify potential biomarkers for CRC, the expression levels of five signature genes in CRC and adjacent normal tissues were further verified via an in vitro experiment. In conclusion, we identified 34 OFRGs and constructed an OFRG-related prognostic signature, which showed excellent performance in predicting survival and therapeutic responses for patients with CRC. This could help to distinguish cold and hot tumors in CRC, and the results might be helpful for precise treatment protocols in clinical practice.

PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer.

PANoptosis is a newly-discovered cell death pathway that involves crosstalk and co-ordination between pyroptosis, apoptosis, and necroptosis processes. However, the roles of PANoptosis-related genes (PRGs) in prognosis and immune landscape of colon cancer remain widely unknown. Here, we performed a bioinformatics analysis of expression data of nineteen PRGs identified from previous studies and clinical data of colon cancer patients obtained from TCGA and GEO databases. Colon cancer cases were divided into two PRG clusters, and prognosis-related differentially expressed genes (PRDEGs) were identified. The patient data were then separated into two corresponding distinct gene clusters, and the relationship between the risk score, patient prognosis, and immune landscape was analyzed. The identified PRGs and gene clusters correlated with patient survival and immune system and cancer-related biological processes and pathways. A prognosis signature based on seven genes was identified, and patients were divided into high-risk and low-risk groups based on the calculated risk score. A nomogram model for prediction of patient survival was also developed based on the risk score and other clinical features. Accordingly, the high-risk group showed worse prognosis, and the risk score was related to immune cell abundance, cancer stem cell (CSC) index, checkpoint expression, and response to immunotherapy and chemotherapeutic drugs. Results of quantitative real-time polymerase chain reaction (qRT-PCR) showed that LGR5 and VSIG4 were differentially expressed between normal and colon cancer samples. In conclusion, we demonstrated the potential of PANoptosis-based molecular clustering and prognostic signatures for prediction of patient survival and tumor microenvironment (TME) in colon cancer. Our findings may improve our understanding of the role of PANoptosis in colon cancer, and enable the development of more effective treatment strategies.

Development and validation of a novel cellular senescence-related prognostic signature for predicting the survival and immune landscape in hepatocellular carcinoma.

Background: Cellular senescence is a typical irreversible form of life stagnation, and recent studies have suggested that long non-coding ribonucleic acids (lncRNA) regulate the occurrence and development of various tumors. In the present study, we attempted to construct a novel signature for predicting the survival of patients with hepatocellular carcinoma (HCC) and the associated immune landscape based on senescence-related (sr) lncRNAs. Method: Expression profiles of srlncRNAs in 424 patients with HCC were retrieved from The Cancer Genome Atlas database. Lasso and Cox regression analyses were performed to identify differentially expressed lncRNAs related to senescence. The prediction efficiency of the signature was checked using a receiver operating characteristic (ROC) curve, Kaplan-Meier analysis, Cox regression analyses, nomogram, and calibration. The risk groups of the gene set enrichment analysis, immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) were also analyzed. Quantitative real-time polymerase chain reaction (qPCR) was used to confirm the levels of AC026412.3, AL451069.3, and AL031985.3 in normal hepatic and HCC cell lines. Results: We identified 3 srlncRNAs (AC026412.3, AL451069.3, and AL031985.3) and constructed a new risk model. The results of the ROC curve and Kaplan-Meier analysis suggested that it was concordant with the prediction. Furthermore, a nomogram model was constructed to accurately predict patient prognosis. The risk score also correlated with immune cell infiltration status, immune checkpoint expression, and chemosensitivity. The results of qPCR revealed that AC026412.3 and AL451069.3 were significantly upregulated in hepatoma cell lines. Conclusion: The novel srlncRNA (AC026412.3, AL451069.3, and AL031985.3) signatures may provide insights into new therapies and prognosis predictions for patients with HCC.

Rutaecarpine suppresses the proliferation and metastasis of colon cancer cells by regulating the STAT3 signaling.

Colorectal cancer (CRC) is a malignant disease that is a serious threat to human health. Rutaecarpine (RUT) is an important bioactive alkaloid of Evodia rutaecarpa. According to previous studies, RUT suppressed the proliferation of several human tumors. However, its role in colorectal tumorigenesis remained unknown. The aim of the present study was to determine the functions of RUT in CRC. Here, we have demonstrated that RUT inhibited the proliferation, migration and invasion of CRC cells in vitro. Further, RUT was found to induce the apoptosis of CRC cells. Mechanistically, RUT decreased the phosphorylation levels of NF-κB and STAT3. Moreover, treatment with RUT upregulated the expression of cleaved-Caspase3 and downregulated the expression of Bcl-2 in CRC. In addition, our findings suggested that RUT inhibited the growth and lung metastasis of CRC Cells in vivo. Based on immunofluorescence analysis, the expression of Ki67 was downregulated while that of cleaved-Caspase3 was upregulated in RUT-treated tumors compared with control-treated tumors. Taken together, our findings indicate that RUT can inhibit the proliferation and migration of CRC cells, and induce the apoptosis of CRC cells by inactivating NF-κB/STAT3 signaling. Our study highlights the potential clinical application of RUT for the treatment of CRC.

Replication the association of 2q32.2-q32.3 and 14q32.11 with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor. The morbidity and mortality of HCC tend to ascend and become a serious threat to the population health. Genetic studies of HCC have identified several susceptibility loci of HCC. In this study, we aim to replicate the association of these loci in our samples from Chinese population and further investigate the genetic interaction. We selected 16 SNPs within 1p36.22, 2q32.2-q32.3, 3p21.33, 8p12, 14q32.11 and 21q21.3 and genotyped in 507 HCC patients and 3014 controls by using Sequenom MassARRAY system. Association analyses were performed by using PLINK 1.07. We observed that the STAT4 (2q32.2-q32.3) at rs7574865 (P=1.17×10(-3), OR=0.79) and EFCAB11 (14q32.11) at rs8013403 (P=1.54×10(-3), OR=0.80) were significantly associated with HCC in this study. In 3p21.33, genetic variant rs6795737 within GLB1 was also observed with suggestive evidence (P=9.98×10(-3), OR=0.84). In the interaction analysis, the pair of associated SNPs (rs7574865 within STAT4, rs8013403 within EFCAB11) generated evidence for interaction (P=4.10×10(-3)). In summary, our work first reported the association of 14q32.11 (EFCAB11) with HCC in Chinese Han population and revealed the genetic interaction between STAT4 (2q32.2-q32.3) and EFCAB11 (14q32.11) in HCC.