Image_5_Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.tif

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan–Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs in vitro was examined.</p

Image_3_Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.tif

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan–Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs in vitro was examined.</p

Image_1_Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.tif

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan–Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs in vitro was examined.</p

Table_1_Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.xlsx

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan–Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs in vitro was examined.</p

Image_2_Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.tif

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan–Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs in vitro was examined.</p

Image_4_Predictors based on cuproptosis closely related to angiogenesis predict colorectal cancer recurrence.tif

Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan–Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs in vitro was examined.</p

DataSheet1_Development of a prognostic model based on different disulfidptosis related genes typing for kidney renal clear cell carcinoma.docx

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC.Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated.Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets.Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.</p

Table1_Development of a prognostic model based on different disulfidptosis related genes typing for kidney renal clear cell carcinoma.docx

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC.Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated.Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets.Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.</p

Association of caffeine intake with erectile dysfunction among men with and without diabetes in NHANES 2001–2004.

<p>^†Adjusted for age, vigorous and moderate physical activity, smoking status, education, race/ethnicity, obesity (BMI ≥ 30 kg/m²), total water intake (plain and tap), total energy (continuous), alcohol (continuous). Error bars represent 95% confidence intervals. <i>P</i>_<i>trend</i><i>= 0</i>.<i>57 and P</i>_{<i>interaction</i>}<i>= 0</i>.<i>65</i>. ^*Erectile dysfunction was defined as “sometimes” or “never” able to maintain an erection for satisfactory sexual intercourse. ^‡ Approximately 170–375 mg/day of caffeine intake is equivalent to 2–3 cups of coffee. ^aDiabetes = fasting plasma glucose ≥126 mg/dl, self-reported diagnosis, or medication treatment.</p

Association of caffeine intake with erectile dysfunction in NHANES 2001–2004 (n = 3724).

<p>^†Adjusted for age, vigorous and moderate physical activity, smoking status, education, race/ethnicity, obesity (BMI ≥ 30 kg/m²), total water intake (plain and tap), total energy (continuous), alcohol (continuous). Error bars represent 95% confidence intervals. <i>P</i>_<i>trend</i><i>= 0</i>.<i>19</i>. ^*Erectile dysfunction was defined as “sometimes” or “never” able to maintain an erection for satisfactory sexual intercourse. ^‡Approximately 170–375 mg/day of caffeine intake is equivalent to 2–3 cups of coffee.</p