A novel disulfidptosis-related lncRNA signature in colorectal cancer for predicting prognosis, tumor immune microenvironment features and drug sensitivity

A novel disulfidptosis-related lncRNA signature in colorectal cancer for predicting prognosis, tumor immune microenvironment features and drug sensitivity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A novel disulfidptosis-related lncRNA signature in colorectal cancer for predicting prognosis, tumor immune microenvironment features and drug sensitivity Yuewen Qi, Wenzheng Zhang, Haowen Qi, Lei Wang, Bingqing Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4435447/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) is a common cancer with high mortality rates worldwide. Disulfidptosis is an emerging mode of cancer cell death. In this study, disulfidptosis-related lncRNAs were identified by screening and incorporated into a prognostic model to predict the prognosis and immunotherapy response of colorectal cancer (CRC), providing a new and effective guide for clinical decision making. Transcriptome and clinical data of CRC patients and normal controls were obtained from The Cancer Genome Atlas (TCGA). Pearson correlation, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify disulfidptosis-related lncRNAs. A risk scoring model was constructed, and its predictive performance was comprehensively validated. An accurate nomogram was constructed for CRC prognosis prediction. Model reliability was verified via principal component, survival and receiver operating characteristic (ROC) curve analyses. GO analysis and GSEA were used to identify cellular pathways relevant to the model. Immune cell infiltration was studied via the ESTIMATE and CIBERSORT algorithms. The association of tumor mutational burden (TMB) with the model-derived risk scores was assessed using single-nucleotide variant data. Finally, tThe clinical value of the model was evaluated through the GDSC and CTRP databases, and effective drugs were predicted. A prognostic risk model containing 9 disulfidptosis-related lncRNAs (ATP2A1-AS1, AC011815.1, AC013652.1, AC109992.2, AC069549.1, AC005034.5, SUCLG2-AS1, AP003555.1 and AL590101.1) was successfully constructed. There were significant difference in survival rates between the high-risk and low-risk groups (based on the median risk score) in the training and validation datasets. The risk score serves as an independent prognostic factor when combined with clinical variables. GSEA revealed that the high-risk group was enriched in the cellular processes of epidermis development, kidney differentiation and skin development. The prognostic model could stratify CRC patients into two distinct risk score groups. A high risk score independently predicted poor overall survival and was correlated with reduced immune cell infiltration, high TMB, and decreased tumor immune response activity. Immune checkpoint blockade might improve survival in high-risk CRC patients, whereas low-risk patients might be more responsive to targeted therapy and diverse kinase inhibitors. In summary, we established a disulfidptosis-related lncRNA model that holds promise as a reliable marker of CRC prognosis and immunotherapy response and can be also be used to predict the immune cell infiltration landscape and targeted therapy response. Biological sciences/Cancer Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Colorectal cancer (CRC) ranks highly among cancers in terms of its prevalence and incidence. It represents approximately 10% of all cancers and is the second most frequent cause of cancer-related death, with approximately 1.9 million new cases and 0.9 million deaths in 2020 worldwide [ 1 – 2 ] . Epidemiological statistics indicate that the burden of CRC is expected to increase, with 2.2 million new cases and 1.1 million deaths expected globally by 2030 [ 3 ] . Early detection and accurate prediction of prognosis can help physicians make correct clinical decisions and improve patient prognosis. Biomarkers are key tools for early detection, prognostication, survival, and treatment response prediction. The tumor − node − metastasis (TNM) staging system of tumors has been widely used to assess patient survival and prognosis; however, the prognosis of patients can differ among those with the same TNM stage, so more refined prediction models are needed [ 4 ] . Therefore, the search for biomarkers with prognostic significance is particularly important for predicting overall or progression-free survival or recurrence rates, informing patients, and supporting proper medical decision-making. Disulfidptosis, which is independent of rapid programmed cell death, is a recently identified type of regulated cell death induced by the aberrant accumulation of intracellular disulfides in SLC7A11-overexpressing cells under glucose starvation conditions. It has been revealed that in glucose-deficient SLC7A11-high cancer cells, accumulation of a large number of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, leading to actin filament contraction and detachment from the plasma membrane and cell death [ 5 ] . Given the important role of various modes of cell death in tumor cells, further understanding the intrinsic mechanisms related to disulfide bonds will help identify potential therapeutic targets for CRC. Long noncoding RNAs (lncRNAs) are nonprotein-coding RNA molecules with transcripts consisting of more than 200 nucleotides. They play critical roles in regulating gene expression and protein function by interacting with DNA, RNA and proteins [ 6 – 7 ] . Mutations and dysregulated expression of lncRNAs are associated with the development of cancer, and aberrant expression of lncRNAs interferes with the cell cycle and metabolism and leads to the development of various diseases, including CRC [ 8 ] . Previous studies have shown that lncRNAs have therapeutic potential for early CRC diagnosis and treatment response prediction [ 9 ] . However, the potential mechanisms by which specific lncRNAs regulate disulfidptosis and affect CRC prognosis remain unclear. In this study, we aimed to identify disulfidptosis-related lncRNAs related to the prognosis of CRC. The lncRNAs were used to construct a model to assess prognosis, the tumor immune microenvironment, and the sensitivity to immunotherapy and chemotherapeutic drugs. Finally, we conducted a preliminary validation of our prediction model according to the differential expression of disulfidptosis-related lncRNAs in the TCGA CRC dataset. Our data demonstrated the potential value of this strategy for predicting the prognosis of CRC and guiding clinical decision-making. Materials and methods Data collection and processing The RNA-sequencing (RNA-seq)-based transcriptome profiling data and clinical information of 517 CRC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 450 samples (409 tumor tissues and 41 normal tissues) remained after exclusion of the sample of patients with missing information on follow-up; these remaining samples were subjected to subsequent analyses. Identification of disulfidptosis-related lncRNAs Nine disulfidptosis-related genes (NDUFS1, OXSM, LRPPRC, NDUFA11, NUBPL, NCKAP1, RPN1, SLC3A2, and SLC7A11) were obtained from previous studies. We used the 9 disulfidptosis-related genes to construct a disulfidptosis-related lncRNA signature. Pearson correlation analysis was used to identify the disulfidptosis-related lncRNAs with a coefficient >|0.4| and p < 0.001. In addition, we visualized the results using a Sankey plot. Establishment and verification of a disulfidptosis-related lncRNA prognostic risk scoring model The tumor patients were randomly divided into a training group (n = 225) and a test group (n = 225) for model validation. First, we used univariate Cox regression to identify disulfidptosis-related lncRNAs that were associated with patient overall survival. Then, through multivariate Cox regression analysis, we established a prognostic model based on 9 disulfidptosis-related lncRNAs and performed LASSO regression analysis for screening of lncRNAs with minimum deviation. Finally, a prognostic model was constructed, and the risk score was calculated as the sum of the products of the expression values of the disulfidptosis-related lncRNAs and the coefficients of multivariate Cox regression analysis (risk score= \(\sum _{\text{i}=0}^{\text{n}}{\beta }\text{i} \text{E} \text{x} \text{p} \text{i}\) [ 10 ] ). Patients were divided into high- and low-risk groups based on the median risk score. To assess the significance of the prognostic model, we conducted survival analysis. The Kaplan − Meier curves for overall survival (OS) and progression-free survival (PFS) were generated for the TCGA, testing and training cohorts. We also assessed the difference in survival between the two risk groups. Dispersion plots, hazard curves, and heatmaps were drawn to determine the distribution of risk values among patients in different risk groups and infer the risk of CRC-related mortality. The differences between the high- and low-risk groups were assessed via principal component analysis (PCA) and biological pathway analysis. Multivariate Cox regression analysis was used to evaluate whether the risk score derived from the model was an independent prognostic factor for patients. The ROC curve specifies the value of the risk factor for the prognostic model. A calibration curve was used to assess whether the survival prediction was consistent with the actual survival time, and the C-index was used to assess the reliability of the nomogram's discrimination and prediction functions. Enrichment function analysis Gene ontology (GO) functional enrichment analysis was performed for the differentially expressed genes between the high- and low-risk groups of patients. The threshold for significance GO term enrichment was set at p < 0.05. We also performed gene set enrichment analysis (GSEA) based on gene expression profiles of the two groups. A gene set was considered enriched when the P value and FDR were less than 0.05. Tumor microenvironment in the high- and low-risk groups First, the abundance of infiltrating stromal cells and immune cells in CRC tissues was analyzed with gene expression data via the estimate R package. After collecting the study data, we compared and analyzed immune cell infiltration and immune pathway activation in each patient, and the differences between the high- and low-risk groups were assessed. For each CRC sample, the CIBERSORT tool was used to estimate the abundances of 22 immune cell types. Furthermore, multiple immune functions were evaluated for each of the samples, and the activities of these immune functions were compared between the two risk groups. Tumor mutational burden analysis We calculated the tumor mutation burden (TMB) and the mutation frequency for each sample based on the total number of somatic base substitutions. The difference in TMB between the high- and low-risk groups was analyzed. CRC patients were divided into high-risk groups and low-risk groups according to the median TMB value. To further analyze the effect of TMB on overall survival, we performed survival analysis for the high- and low-risk groups. We also combined the risk score with the TMB and analyzed the value of this two-factor model in predicting prognosis. Drug sensitivity analysis The oncoPredict R package was used to detect drug sensitivity in patients. Drug sensitivity data were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) ( https://www.cancerrxgene.org/ ). We obtained the sensitivity score for each patient using the calcPhenotype function. Drug sensitivity scores were compared between the high-risk and low-risk groups. External database validation of disulfidptosis-related lncRNAs in CRC The expression of disulfidptosis-related lncRNAs in CRC tissues and normal colon tissues was analyzed via the online software UALCAN ( http://ualcan.path.uab.edu/analysis.html ). In this study, we analyzed the differences in the expression of disulfidptosis-related lncRNAs between primary CRC tissues and normal tissues. The difference in survival between CRC patient risk groups was validated using the Kaplan–Meier Plotter database, a tool equipped for performing network survival analysis through both univariate and multivariate methods. Statistical analysis R was used for the data analysis (4.3.1). Survival analysis was performed using Kaplan‒Meier estimation and the log-rank test. p < 0.05 (marked with *) was considered to indicate statistical significance. Pearson correlation coefficients were calculated for pairs of variables. p < 0.05 (marked with *) was considered to indicate statistical significance. Kaplan‒Meier curves were generated using the log-rank test. Results Identification of disulfidptosisrelated genes and lncRNAs in CRC To identify lncRNAs associated with disulfidptosisrelated genes, Pearson correlation analysis was performed according to the expression levels of the lncRNAs and disulfidptosisrelated genes. A total of 895 lncRNAs were identified by screening, and their expression was related to that of 9 disulfidptosisrelated genes (Fig. 1 A) Establishment of the disulfidptosis-related lncRNA prognostic model We randomly divided all tumor patients into a training group (n = 225) and a test group (n = 225) at a 5:5 ratio for model validation. lncRNAs associated with the survival of CRC patients were used to construct the model. Then, we performed univariate Cox regression to assess their correlation with prognosis, and those that showed no significant effect on survival were excluded. We found that 20 disulfidptosis-related lncRNAs were significantly correlated with prognosis. Among them, 4 lncRNAs were prognostically favorable, while 16 lncRNAs were prognostically unfavorable (Fig. 1 B). Based on these 20 disulfidptosis-related lncRNAs, LASSO Cox regression analysis was performed on the training set to determine the optimal prediction score and further establish the prognostic model (Fig. 1 C, D). We then assigned each patient a risk score based on the formula for the prognostic model [risk score = (ATP2A1-AS1*0.418)-(AC011815.1*1.296)+(AC013652.1*1.237)+(AC109992.2*1.974)-(AC069549.1*0.688)-(AC005034.5*0.456)-(SUCLG2-AS1*1.929)+(AP003555.1*1.042)+(AL590101.1*0.500)]. In addition, we analyzed the correlations between the expression levels of the 9 lncRNAs and 10 disulfidptosis-related genes (Fig. 1 E). To further confirm the predictive value of the model for CRC, we evaluated its performed in the overall set, training set, and test set. The overall survival curves of the overall cohort and training cohort showed that the prognosis of the low-risk subgroup was significantly better than that of the high-risk subgroup (Fig. 2 A, B). However, the difference between the high-risk and low-risk groups in the test group was not obvious (Fig. 2 C). In addition, we plotted the progression-free survival curve of patients and found that patients in the low-risk group survived better than those in the high-risk group (Fig. 2 D). Then, we further analyzed the expression patterns of the 9 disulfidptosis-related lncRNAs in the overall set, training set, and test set. As the disease progressed, the survival rate of patients in the high-risk group decreased, and this trend was not observed for the low-risk group in the three cohorts. Moreover, there was a positive correlation between the risk score and the probability of death in each cohort (Fig. 2 E-M) . The model risk score is an independent prognostic factor Cox regression analysis was performed to further evaluate whether the prognostic model was influenced by other clinical factors. We included four clinical characteristics of CRC patients, namely, age, sex, tumor stage, and risk score. According to univariate analysis, tumor stage, with a hazard ratio of 2.079 (1.637 − 2.641), and the risk score, with a hazard ratio of 1.057 (1.039 − 1.074), are two risk factors that affect prognosis. In addition, the multivariate Cox regression analysis revealed two independent prognostic factors— the risk score with a hazard ratio of 1.057 (1.036 − 1.079) and tumor stage with a hazard ratio of 2.144 (1.690 − 2.720) (Fig. 3 A, B). To further evaluate the predictive accuracy of the lncRNA-based prognostic model, ROC curve analysis was performed. The AUC values for 1-, 3-, and 5-year survival were 0.652, 0.717, and 0.710, respectively (Fig. 3 C). The ROC curve showed the high accuracy of our prognostic model. Our prognostic model was almost as accurate as tumor stage in terms of prognosis prediction (AUC values of 0.717 versus 0.737) (Fig. 3 D). Finally, the concordance index of the risk score and other clinical features showed that the risk score performed better than other clinical features, such as sex, age and tumor stage (Fig. 3 E) . Construction of a nomogram by combining the risk score with other clinical characteristics Our findings suggested that the risk score can serve as a good predictor of prognosis. We combined the risk score with clinical information to construct a nomogram. The nomogram was applied for randomly selected patients in the cohort, which revealed 1-, 3-, and 5-year survival rates of 0.751, 0.478, and 0.276, respectively (Fig. 4 A). A calibration chart was used to demonstrate the accuracy of the nomogram predictions, and the results showed good consistency with the actual results (Fig. 4 B). Patient risk scores correlate well with clinical characteristics The risk score was also a good predictor of survival in patients with tumor stages I − II. There was a significant difference in survival between the high- and low-risk groups, but we found no significant difference in survival between the high- and low-risk groups in our analysis of patients with stage III − IV disease (Fig. 5 A, B). PCA and biological process enrichment analysis PCA was used to compare samples from the low-risk group and the high-risk group, and a three-dimensional scatter plot was drawn to display the spatial distribution of the samples (Fig. 6 A-C). The results showed that the samples grouped according to the risk score had obvious clustering characteristics (Fig. 6 D) . We further investigated the molecular mechanism of disulfidptosis-related lncRNAs and their effect on CRC tumorigenesis and progression. The results of GO analysis showed that the lncRNA-related genes were related to many factors, such as receptor ligand activity, distal axons and positive regulation of secretion. Furthermore, in the molecular biological process (BP) category, the lncRNA-related genes were mainly enriched in BPs such as positive regulation of secretion, skin development and positive regulation of secretion by the cell. In the cellular component (CC) category, the genes were enriched mainly in distal axons. In the molecular function (MF) category, they were enriched in receptor ligand activity, glycosaminoglycan binding and G protein − coupled receptor binding (Fig. 6 E, F). In addition, we performed GSEA. GSEA showed that the top five significantly enriched cellular processes were epidermis development, keratinocyte differentiation, skin development, cornified envelope and intermediate filament composition in the high-risk group and DNA replication-dependent chromatin assembly, nucleosome assembly, nucleosome organization, the nucleosome and the structural constituent of chromatin in the low-risk group (Fig. 6 G, H) . Differences in immune microenvironment features between the high- and low-risk groups The tumor immune microenvironment plays a key role in determining tumor progression. In the investigation of immune characteristics in the high-risk group, the samples were ranked by risk score (low to high) and the proportions of various immune cells were estimated (Fig. 7 A). Next, we used the CIBERSORT algorithm to determine the proportions of different immune cell types in colorectal tissues. We found that the high-risk group had less infiltration of resting memory CD4 T cells, while the proportions of resting NK cells and monocytes were increased (Fig. 7 B). In addition, we analyzed 22 immune functions, eight of which had lower scores in the high-risk group than in the low-risk group (Fig. 7 C). In conclusion, these results showed that high-risk CRC according to our disulfidptosis-related lncRNA model classification may cause the progression of tumor disease and a decrease in overall survival by impairing the immune response of the tumor microenvironment.. TMB analysis of CRC patients in the high- and low-risk groups classified by the disulfidptosis-related lncRNA model The high-risk groups had high TMB scores, and the 15 most frequently mutated genes and their mutation types are shown in Fig. 8 A. Similarly, the results for the low-risk group are shown in Fig. 8 B. We found that patients with a low TMB had a much greater overall survival rate than those with a high TMB. Next, we divided CRC patients into four subgroups based on the risk score and TMB to investigate the impact of these two factors on overall survival. We found that patients with low TMB and a low risk score exhibited a better prognosis, and their 8-year survival rate was approximately 65%. Compared with patients in the other groups, patients with high TMB and a high risk score had the poorest prognosis, and the 3- and 5-year survival rates were the lowest (Fig. 8 C, D) . Assessment of drug sensitivity in different subgroups To evaluate the clinical value of this model and identify effective drugs, we analyzed the sensitivity to several common CRC drugs in the GDSC and CTRP databases. There were differences in drug sensitivity among patients with different risk scores. Compared with patients in the low-risk group, patients in the high-risk group were more sensitive to savolitinib, TAF1_5496, AGI − 5198, AZD4547, AZD5991, AZD6482, GSK591, IAP_5620, KU-55933, OF-1 and venetoclax. Patients in the low-risk group were more sensitive to OSI-027 (Fig. 9 ). Differential expression and validation of prognostic disulfidptosis-related lncRNAs in CRC In this study, we analyzed the differences in the expression of nine disulfidptosis-related lncRNAs between primary CRC tissues and normal tissues via the UALCAN online database. AC005034.5, AC013652.1, AC109992.2, AC003555.1 and ATP2A1-AS1 levels were significantly higher in CRC than in normal tissues. The expression of AC011815.1, AC069549.1 and SUCLG2-AS1 was significantly lower in CRC (AL590101.1 was not found in the database)(Fig. 10 ). To further corroborate the independent prognostic value of disulfidptosis-related lncRNAs in CRC patients, we performed a prognostic analysis of ATP2A1-AS1 and SUCLG2-AS1 using the Kaplan–Meier Plotter database (AC011815.1, AC013652.1, AC109992.2, AC069549.1, AC005034.5, AP003555.1 and AL590101.1 were not found in the database). ATP2A1-AS1 was identified as a protective prognostic factor (HR = 0.78 (0.61–1), p = 0.045), while SUCLG2-AS1 (HR = 1.49 (1.18–1.89), p = 0.00094) indicated a poor prognosis (Fig. 11 ). Discussion Colorectal cancer (CRC) is a common cancer with a high lethality rate worldwide. While combined treatment approaches, including surgery and chemotherapy, have significantly improved survival rates for CRC patients, 40% of patients still struggle with challenges related to tumor resistance and recurrence [ 11 ] . The identification of novel biomarkers is crucial for understanding the biological behavior and progression of the disease and for guiding prognosis-based treatment decision making. Disulfidptosis is a prognostic marker in various tumor types, and targeting this newly discovered form of cell death is considered a potential therapeutic strategy for cancer treatment [ 12 – 13 ] . LncRNAs play an important role in regulating the malignant behavior of tumor cells and have been proven to be potential biomarkers and targets for cancer diagnosis and treatment. To date, lncRNAs associated with disulfidptosis remain largely unknown, and their prognostic significance in CRC is unclear. In this study, we identified lncRNAs whose levels were correlated with those of disulfidptosis-related genes and established a prognostic model for CRC consisting of nine disulfidptosis-related lncRNAs. Moreover, tumor immune infiltration and drug sensitivity were evaluated in the model risk groups, indicating that this model is an intuitive scientific tool that can guide prognosis evaluation and treatment planning. In our study, we randomly divided CRC patients into a training group and a test group for model validation. lncRNAs associated with the survival of CRC patients were included in the model. We found that 20 disulfidptosis-related lncRNAs were significantly correlated with prognosis. A risk score model containing 9 disulfidptosis-related prognostic lncRNAs was established using LASSO regression analysis. In previous studies, ATP2A1-AS1 was used to construct prognostic models related to cuproptosis and autophagy in cervical cancer and cellular senescence-related multiple myeloma [ 14 – 16 ] . AC013652.1 has been demonstrated to have unique functional features and clinical significance in various cancers, including gastric cancer and colorectal cancer [ 17 – 18 ] . AC005034.5 has been used to construct prognostic models in osteosarcoma [ 19 ] . SUCLG2-AS1 was found to be abnormally expressed in acute myeloid leukemia and a variety of tumors, affecting the development of nasopharyngeal carcinoma, triple-negative breast cancer, clear cell renal cell carcinoma and gastric cancer [ 20 – 23 ] . Moreover, AP003555.1 was identified and validated as a novel ferroptosis-related and oxidative stress-related lncRNA and incorporated into a prognostic model for colon cancer [ 24 – 25 ] . However, the roles and functions of AC011815.1, AC109992.2, AC069549.1 and AL590101.1 have not been previously reported. Univariate and multivariate Cox regression analyses were performed. This model risk score, which was calculated for each patient and used to classify them into high- and low-risk groups, was identified as an independent prognostic factor for CRC. ROC curve analysis revealed that the AUCs were close to or exceeded 0.7 at 1, 3, and 5 years, highlighting the high accuracy of our prognostic model. The C-index curves demonstrated that this factor had greater specificity than other clinical factors. Moreover, our findings suggested that the risk score can serve as a good predictor of prognosis. We combined the risk score with clinical factors to construct a nomogram. A calibration chart was used to assess the accuracy of the nomogram predictions, the predictions showed good consistency with the actual results. By further investigating immune characteristics in high-risk populations, we found that the high-risk group (according to the risk score) had less infiltration of resting CD4 + memory T cells but greater infiltration of resting NK cells and monocytes than the low-risk group, which showed that the decrease in overall survival and increase in disease stage in the high-risk CRC group based on our disulfidptosis-related lncRNA model may be related to impairment of the immune response in the tumor microenvironment. TMB is a potential predictive biomarker of solid tumors. In our study, patients with a low TMB had a much greater overall survival rate than those with a high TMB. We divided CRC patients into four subgroups based on the risk score and TMB to investigate the impact of these two factors on overall survival. We found that patients with low TMB and low risk exhibited a better prognosis, and their 8-year survival rate was approximately 65%. Compared with patients in the other groups, patients with high TMB and high risk scores had the poorest prognosis. Drug resistance is one of the main reasons for the failure of tumor therapy. To evaluate the clinical value of this model and identify effective drugs, we analyzed the sensitivity of several common CRC drugs in the GDSC and CTRP databases. There were differences in drug sensitivity among patients with different risk scores. Compared with patients in the low-risk group, patients in the high-risk group were more sensitive to savolitinib, TAF1_5496, AGI − 5198, AZD4547, AZD5991, AZD6482, GSK591, IAP_5620, KU-55933, OF-1 and venetoclax. Patients in the low-risk group were more sensitive to OSI-027. These results suggest that our disulfidptosis-related lncRNA model is a potential tool for predicting the response of CRC patients to other common antineoplastic drugs. In summary, a prediction model based on 9 disulfidptosis-related lncRNAs showed high accuracy, and the model in this paper revealed the immune microenvironment of high-risk and low-risk patients and predicted the immunotherapy response of CRC patients. This study provides preliminary insights into the association between disulfidptosis and the tumor immune response. However, this study also has limitations. Our study mainly relied on database data for correlation analysis and lacked validation of basic experimental data. In the future, validation at the tissue, cellular and animal levels will be necessary to elucidate the biological pathways by which disulfidptosis-related lncRNAs exert their effects. Conclusion In conclusion, we established a disulfidptosis-related lncRNA model that holds promise as a reliable prognostic marker and predictor of immunotherapy response in CRC patients; it can also be used to assess the immune cell infiltration landscape and the response to targeted therapy in CRC patients. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Competing interests The authors declare no competing interests. Funding This work was supported by the 2023 Clinical Medical Excellent Talent Cultivating Program of Hebei (grant no. ZF2023246) and the 2024 Medical Science Research Project of Hebei Provincial Health Commission (no. 20240586). Author Contribution Yuewen Qi wrote, drafted, reviewed and edited the interpretation of the results and analyzed the data; Wenzheng Zhang performed the experiments and wrote the paper; Haowen Qi and Lei Wang collected the tissue samples and performed the experiments; Bingqing Li designed the research study and provided clinical advice. All the authors contributed to this article and approved the final manuscript. All the authors have final responsibility for the decision to submit all the data to the study for publication. All participants provided informed consent, participated in the study and agreed to publish their images/data in an online open access publication. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References George A.T., Aggarwal S., Dharmavaram S., Menon A., Dube M., Vogler M., Foden P., Field A. Regional variations in UK colorectal cancer screening and mortality. 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Cell Cycle. 2023 Jun;22(12):1434-1449. Zhou J, Xu L, Zhou H, Wang J, Xing X. Prediction of Prognosis and Chemotherapeutic Sensitivity Based on Cuproptosis-Associated lncRNAs in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma. Genes (Basel). 2023 Jun 30;14(7):1381. Feng Q, Wang J, Cui N, Liu X, Wang H. Autophagy-related long non-coding RNA signature for potential prognostic biomarkers of patients with cervical cancer: a study based on public databases. Ann Transl Med. 2021 Nov;9(22):1668. Wang W, Pei Q, Wang L, Mu T, Feng H. Construction of a Prognostic Signature of 10 Autophagy-Related lncRNAs in Gastric Cancer. Int J Gen Med. 2022 Apr 5;15:3699-3710. doi: 10.2147/IJGM.S348943. PMID: 35411177; PMCID: PMC8994655. Li N, Shen J, Qiao X, Gao Y, Su HB, Zhang S. Long Non-Coding RNA Signatures Associated with Ferroptosis Predict Prognosis in Colorectal Cancer. Int J Gen Med. 2022 Jan 4;15:33-43. doi: 10.2147/IJGM.S331378. PMID: 35018112; PMCID: PMC8742603. Wang X, Xie C, Lin L. Development and validation of a cuproptosis-related lncRNA model correlated to the cancer-associated fibroblasts enable the prediction prognosis of patients with osteosarcoma. J Bone Oncol. 2022 Dec 9;38:100463. doi: 10.1016/j.jbo.2022.100463. PMID: 36569351; PMCID: PMC9772846. Hu X, Wu J, Feng Y, Ma H, Zhang E, Zhang C, Sun Q, Wang T, Ge Y, Zong D, Chen W, He X. METTL3-stabilized super enhancers-lncRNA SUCLG2-AS1 mediates the formation of a long-range chromatin loop between enhancers and promoters of SOX2 in metastasis and radiosensitivity of nasopharyngeal carcinoma. Clin Transl Med. 2023 Sep;13(9):e1361. doi: 10.1002/ctm2.1361. PMID: 37658588; PMCID: PMC10474317. Wu J, Cai Y, Zhao G, Li M. A ten N6-methyladenosine-related long non-coding RNAs signature predicts prognosis of triple-negative breast cancer. J Clin Lab Anal. 2021 Jun;35(6):e23779. doi: 10.1002/jcla.23779. Epub 2021 May 2. PMID: 33934391; PMCID: PMC8183938. Yang W, Zhou J, Zhang K, Li L, Xu Y, Ma K, Xie H, Cai L, Gong Y, Gong K. Identification and validation of the clinical roles of the VHL-related LncRNAs in clear cell renal cell carcinoma. J Cancer. 2021 Mar 5;12(9):2702-2714. doi: 10.7150/jca.55113. PMID: 33854630; PMCID: PMC8040721. Xing C, Cai Z, Gong J, Zhou J, Xu J, Guo F. Identification of Potential Biomarkers Involved in Gastric Cancer Through Integrated Analysis of Non-Coding RNA Associated Competing Endogenous RNAs Network. Clin Lab. 2018 Oct 1;64(10):1661-1669. doi: 10.7754/Clin.Lab.2018.180419. PMID: 30336538. Wu Z, Lu Z, Li L, Ma M, Long F, Wu R, Huang L, Chou J, Yang K, Zhang Y, Li X, Hu G, Zhang Y, Lin C. Identification and Validation of Ferroptosis-Related LncRNA Signatures as a Novel Prognostic Model for Colon Cancer. Front Immunol. 2022 Jan 26;12:783362. doi: 10.3389/fimmu.2021.783362. PMID: 35154072; PMCID: PMC8826443. Chen R, Wei JM. Integrated analysis identifies oxidative stress-related lncRNAs associated with progression and prognosis in colorectal cancer. BMC Bioinformatics. 2023 Mar 3;24(1):76. doi: 10.1186/s12859-023-05203-5. PMID: 36869292; PMCID: PMC9985255. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4435447","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309006300,"identity":"14dec99b-1ef4-459f-9bba-0d69abb47d99","order_by":0,"name":"Yuewen Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACZijNz8zY+CChwoYELZLtzYcNHpxJI8E2gzPH0iQfth0irJLvOI+ZxMc2mzyDGzlmFQlsBxj427sT8GqRPMxjJjmzLa1YEqjlRgLPHQaJM2c34HcPUIs077bDiX1gLRLPGAwkconQ8nfb/8QGoJaCBIPDRGph3HYgcQLQ+wwJCURokTzMVmzZ+y85cSYwkCUSDqTxEPQL3/nDG2/8OGOX2A+Myo8//9nI8bf34tfCcIDDAIXPg185WAv7A8KKRsEoGAWjYGQDAMMqUCKtXTCCAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Chengde Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yuewen","middleName":"","lastName":"Qi","suffix":""},{"id":309006301,"identity":"66acdb0a-1f9c-40e2-a944-78a1ef991907","order_by":1,"name":"Wenzheng Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenzheng","middleName":"","lastName":"Zhang","suffix":""},{"id":309006302,"identity":"16f5c39d-0631-48a5-9ad7-ddb66b5f0c44","order_by":2,"name":"Haowen Qi","email":"","orcid":"","institution":"Chengde Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haowen","middleName":"","lastName":"Qi","suffix":""},{"id":309006303,"identity":"c702d9d4-b2a9-4c2b-8a76-0d310402a114","order_by":3,"name":"Lei Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":309006304,"identity":"a7b4bad2-1676-48f2-8d8e-614be6de8117","order_by":4,"name":"Bingqing Li","email":"","orcid":"","institution":"The Affiliated Hospital of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bingqing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-05-17 08:53:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4435447/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4435447/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57873296,"identity":"34bf50dd-4ba2-4730-961d-02ea7253c602","added_by":"auto","created_at":"2024-06-06 18:38:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":359432,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of disulfidptosis-related lncRNAs in CRC. (A) Sankey diagram of disulgdptosisrelated lncRNAs. (B)Forest plot of prognostic lncRNAs associated with disulfidptosis. (C) The LASSO regression coefficient spectrum. (D) Cross-validation of parameter selection in the LASSO model. (E) Correlation between risk lncRNAs and disulgdptosis-related genes.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/7aa62f64f663154bea574263.jpg"},{"id":57873294,"identity":"72e957eb-f136-42d3-83c3-556862305fac","added_by":"auto","created_at":"2024-06-06 18:38:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":420770,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the prognostic risk model in the training, testing, and entire groups. (A–C) Survival status distribution maps. (D) progression-free survival curve. (E–G) Distribution of association between risk score and survival status between high- and low-risk groups. (H–J) Risk heatmap of the nine disulfidptosis-related lncRNAs. (K–M) Kaplan–Meier survival curves between high- and low-risk groups\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/e78fbc3d3fb3a3fd4c677bd6.jpg"},{"id":57874435,"identity":"34716b15-665a-4b01-9565-caa7435d4874","added_by":"auto","created_at":"2024-06-06 18:46:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256192,"visible":true,"origin":"","legend":"\u003cp\u003eThe model based on nine disulfidptosis-related lncRNAs is an independent prognosis indicator with high accuracy. (A) Forest plot showing prognostic value of age, gender, tumor stage and the model-derived risk score according to univariate regression analysis. (B) Forest plot showing tumor stage and our model-derive risk score are independent prognostic factors based on multivariate regression analysis. (C) The prognostic accuracy of our model-derived risk score for predicting 1-year, 3-year, and 5-year survival. (D) The accuracy of risk score, tumor stage, age and gender in predicting CRC patients’ survival. (E) C-index curves of the risk score and other clinical features.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/81cec771dbbc564900fb2a2d.jpg"},{"id":57874438,"identity":"3694161d-ff6c-426d-b287-228db4f9452a","added_by":"auto","created_at":"2024-06-06 18:46:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211216,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The nomogram combined with other clinical features. (B) calibration curves for overall survival at 1，3，and 5 years.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/f50712f547fca6f859a0e75e.jpg"},{"id":57874436,"identity":"2579cb4b-7230-4c55-becd-c9488be64688","added_by":"auto","created_at":"2024-06-06 18:46:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":179158,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves showing the difference in overall survival between high- and low-risk CRC patients at early stages (A) and at advanced stages (B).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/89a53c5a41d46ab259393c2e.jpg"},{"id":57873300,"identity":"f9cc62f3-d0ed-44a2-9caf-ee554a04fd62","added_by":"auto","created_at":"2024-06-06 18:38:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":292597,"visible":true,"origin":"","legend":"\u003cp\u003ePCA between low-risk and high-risk groups. (A) PCA of all genes. (B) PCA of disulfidptosis genes. (C) PCA of disulfidptosis-related lncRNAs. (D) PCA of 9 risk lncRNAs. (E, F) GO analysis；(G, H) GSEA. BP，biological process；CC，cellular component；MF，molecular function.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/240d0e3b916cb5dc0e24bc31.jpg"},{"id":57875177,"identity":"c81cb1f4-f9d0-4961-95a8-7532dd8702c4","added_by":"auto","created_at":"2024-06-06 18:54:19","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":311249,"visible":true,"origin":"","legend":"\u003cp\u003e(A) relative proportions of infiltrating immune cells in different risk subgroups；(B) Differences in infiltration degrees of 22 immune cell types in the tumor microenvironment of high- and low-risk CRCs. (C) Differences in diverse immune functions activities in the tumor microenvironment of high- and low-risk CRCs.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/3092d230d9a95368371ae265.jpg"},{"id":57873297,"identity":"a0c996eb-e212-42ec-b126-878bd5a3f996","added_by":"auto","created_at":"2024-06-06 18:38:19","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":414534,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential tumor mutational burden and somatic mutation frequencies in the high- and low-risk CRCs. (A, B) Mutation frequencies of the top 20 most frequently mutated genes in the high-risk (A) and low-risk (B) CRCs were shown in the waterfall plots. The upper histograms of the plots represent tumor mutational burden of each sample. The mutation types of each gene are indicated with different colors and the right histograms show the sample number with a certain mutation type of the corresponding genes. (C) Kaplan-Meier curves showing the difference in overall survival between CRC patients with high and low tumor mutational burden. (D) Kaplan-Meier curves of overall survival of the four subgroups that are classified based on different tumor mutational burden and different risk score derived from the lncRNA model.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/0f878a9aba6b003f2275b70a.jpg"},{"id":57873303,"identity":"277bc5d9-953c-4fc9-bf9d-98cf087a7f1c","added_by":"auto","created_at":"2024-06-06 18:38:20","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":268605,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity prediction of patients in the low- and high-risk groups.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/ba826ade24952667628dd94c.jpg"},{"id":57874439,"identity":"d36f4775-43e9-4a5d-a0ee-580137edb4a2","added_by":"auto","created_at":"2024-06-06 18:46:20","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":158909,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of disulfidptosis-related lncRNAs in UALCAN database.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/7f4c52653471f3203580a717.jpg"},{"id":57873302,"identity":"2af81fcd-1401-4d4d-9a99-bdca0c3f7a59","added_by":"auto","created_at":"2024-06-06 18:38:20","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":211771,"visible":true,"origin":"","legend":"\u003cp\u003eOS analysis of ATP2A1-AS1 (A) and SUCLG2-AS1 (B) from the Kaplan–Meier Plotter database.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/24f6fd5745029e3c3b923de4.jpg"},{"id":58271328,"identity":"5561223c-2012-46e5-a925-4f45f975bc8c","added_by":"auto","created_at":"2024-06-13 08:40:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3748839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4435447/v1/136f0b9c-97e3-4d27-b07e-222ef7de5d2d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel disulfidptosis-related lncRNA signature in colorectal cancer for predicting prognosis, tumor immune microenvironment features and drug sensitivity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks highly among cancers in terms of its prevalence and incidence. It represents approximately 10% of all cancers and is the second most frequent cause of cancer-related death, with approximately 1.9\u0026nbsp;million new cases and 0.9\u0026nbsp;million deaths in 2020 worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Epidemiological statistics indicate that the burden of CRC is expected to increase, with 2.2\u0026nbsp;million new cases and 1.1\u0026nbsp;million deaths expected globally by 2030\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Early detection and accurate prediction of prognosis can help physicians make correct clinical decisions and improve patient prognosis. Biomarkers are key tools for early detection, prognostication, survival, and treatment response prediction. The tumor\u0026thinsp;\u0026minus;\u0026thinsp;node\u0026thinsp;\u0026minus;\u0026thinsp;metastasis (TNM) staging system of tumors has been widely used to assess patient survival and prognosis; however, the prognosis of patients can differ among those with the same TNM stage, so more refined prediction models are needed\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, the search for biomarkers with prognostic significance is particularly important for predicting overall or progression-free survival or recurrence rates, informing patients, and supporting proper medical decision-making.\u003c/p\u003e \u003cp\u003eDisulfidptosis, which is independent of rapid programmed cell death, is a recently identified type of regulated cell death induced by the aberrant accumulation of intracellular disulfides in SLC7A11-overexpressing cells under glucose starvation conditions. It has been revealed that in glucose-deficient SLC7A11-high cancer cells, accumulation of a large number of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, leading to actin filament contraction and detachment from the plasma membrane and cell death\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Given the important role of various modes of cell death in tumor cells, further understanding the intrinsic mechanisms related to disulfide bonds will help identify potential therapeutic targets for CRC.\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) are nonprotein-coding RNA molecules with transcripts consisting of more than 200 nucleotides. They play critical roles in regulating gene expression and protein function by interacting with DNA, RNA and proteins\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Mutations and dysregulated expression of lncRNAs are associated with the development of cancer, and aberrant expression of lncRNAs interferes with the cell cycle and metabolism and leads to the development of various diseases, including CRC\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that lncRNAs have therapeutic potential for early CRC diagnosis and treatment response prediction\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, the potential mechanisms by which specific lncRNAs regulate disulfidptosis and affect CRC prognosis remain unclear.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to identify disulfidptosis-related lncRNAs related to the prognosis of CRC. The lncRNAs were used to construct a model to assess prognosis, the tumor immune microenvironment, and the sensitivity to immunotherapy and chemotherapeutic drugs. Finally, we conducted a preliminary validation of our prediction model according to the differential expression of disulfidptosis-related lncRNAs in the TCGA CRC dataset. Our data demonstrated the potential value of this strategy for predicting the prognosis of CRC and guiding clinical decision-making.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and processing\u003c/h2\u003e \u003cp\u003eThe RNA-sequencing (RNA-seq)-based transcriptome profiling data and clinical information of 517 CRC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 450 samples (409 tumor tissues and 41 normal tissues) remained after exclusion of the sample of patients with missing information on follow-up; these remaining samples were subjected to subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of disulfidptosis-related lncRNAs\u003c/h2\u003e \u003cp\u003eNine disulfidptosis-related genes (NDUFS1, OXSM, LRPPRC, NDUFA11, NUBPL, NCKAP1, RPN1, SLC3A2, and SLC7A11) were obtained from previous studies. We used the 9 disulfidptosis-related genes to construct a disulfidptosis-related lncRNA signature. Pearson correlation analysis was used to identify the disulfidptosis-related lncRNAs with a coefficient \u0026gt;|0.4| and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. In addition, we visualized the results using a Sankey plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and verification of a disulfidptosis-related lncRNA prognostic risk scoring model\u003c/h2\u003e \u003cp\u003eThe tumor patients were randomly divided into a training group (n\u0026thinsp;=\u0026thinsp;225) and a test group (n\u0026thinsp;=\u0026thinsp;225) for model validation. First, we used univariate Cox regression to identify disulfidptosis-related lncRNAs that were associated with patient overall survival. Then, through multivariate Cox regression analysis, we established a prognostic model based on 9 disulfidptosis-related lncRNAs and performed LASSO regression analysis for screening of lncRNAs with minimum deviation. Finally, a prognostic model was constructed, and the risk score was calculated as the sum of the products of the expression values of the disulfidptosis-related lncRNAs and the coefficients of multivariate Cox regression analysis (risk score=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{\\text{i}=0}^{\\text{n}}{\\beta }\\text{i} \\text{E} \\text{x} \\text{p} \\text{i}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003ePatients were divided into high- and low-risk groups based on the median risk score. To assess the significance of the prognostic model, we conducted survival analysis. The Kaplan\u0026thinsp;\u0026minus;\u0026thinsp;Meier curves for overall survival (OS) and progression-free survival (PFS) were generated for the TCGA, testing and training cohorts. We also assessed the difference in survival between the two risk groups. Dispersion plots, hazard curves, and heatmaps were drawn to determine the distribution of risk values among patients in different risk groups and infer the risk of CRC-related mortality. The differences between the high- and low-risk groups were assessed via principal component analysis (PCA) and biological pathway analysis.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis was used to evaluate whether the risk score derived from the model was an independent prognostic factor for patients. The ROC curve specifies the value of the risk factor for the prognostic model. A calibration curve was used to assess whether the survival prediction was consistent with the actual survival time, and the C-index was used to assess the reliability of the nomogram's discrimination and prediction functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment function analysis\u003c/h2\u003e \u003cp\u003eGene ontology (GO) functional enrichment analysis was performed for the differentially expressed genes between the high- and low-risk groups of patients. The threshold for significance GO term enrichment was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We also performed gene set enrichment analysis (GSEA) based on gene expression profiles of the two groups. A gene set was considered enriched when the P value and FDR were less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTumor microenvironment in the high- and low-risk groups\u003c/h2\u003e \u003cp\u003eFirst, the abundance of infiltrating stromal cells and immune cells in CRC tissues was analyzed with gene expression data via the estimate R package. After collecting the study data, we compared and analyzed immune cell infiltration and immune pathway activation in each patient, and the differences between the high- and low-risk groups were assessed.\u003c/p\u003e \u003cp\u003eFor each CRC sample, the CIBERSORT tool was used to estimate the abundances of 22 immune cell types. Furthermore, multiple immune functions were evaluated for each of the samples, and the activities of these immune functions were compared between the two risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTumor mutational burden analysis\u003c/h2\u003e \u003cp\u003eWe calculated the tumor mutation burden (TMB) and the mutation frequency for each sample based on the total number of somatic base substitutions. The difference in TMB between the high- and low-risk groups was analyzed. CRC patients were divided into high-risk groups and low-risk groups according to the median TMB value. To further analyze the effect of TMB on overall survival, we performed survival analysis for the high- and low-risk groups. We also combined the risk score with the TMB and analyzed the value of this two-factor model in predicting prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe oncoPredict R package was used to detect drug sensitivity in patients. Drug sensitivity data were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We obtained the sensitivity score for each patient using the calcPhenotype function. Drug sensitivity scores were compared between the high-risk and low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eExternal database validation of disulfidptosis-related lncRNAs in CRC\u003c/h2\u003e \u003cp\u003eThe expression of disulfidptosis-related lncRNAs in CRC tissues and normal colon tissues was analyzed via the online software UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/analysis.html\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/analysis.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, we analyzed the differences in the expression of disulfidptosis-related lncRNAs between primary CRC tissues and normal tissues. The difference in survival between CRC patient risk groups was validated using the Kaplan\u0026ndash;Meier Plotter database, a tool equipped for performing network survival analysis through both univariate and multivariate methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eR was used for the data analysis (4.3.1). Survival analysis was performed using Kaplan‒Meier estimation and the log-rank test. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (marked with *) was considered to indicate statistical significance. Pearson correlation coefficients were calculated for pairs of variables. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (marked with *) was considered to indicate statistical significance. Kaplan‒Meier curves were generated using the log-rank test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of disulfidptosisrelated genes and lncRNAs in CRC\u003c/h2\u003e \u003cp\u003eTo identify lncRNAs associated with disulfidptosisrelated genes, Pearson correlation analysis was performed according to the expression levels of the lncRNAs and disulfidptosisrelated genes. A total of 895 lncRNAs were identified by screening, and their expression was related to that of 9 disulfidptosisrelated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) \u003cb\u003eEstablishment of the disulfidptosis-related lncRNA prognostic model\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe randomly divided all tumor patients into a training group (n\u0026thinsp;=\u0026thinsp;225) and a test group (n\u0026thinsp;=\u0026thinsp;225) at a 5:5 ratio for model validation. lncRNAs associated with the survival of CRC patients were used to construct the model. Then, we performed univariate Cox regression to assess their correlation with prognosis, and those that showed no significant effect on survival were excluded. We found that 20 disulfidptosis-related lncRNAs were significantly correlated with prognosis. Among them, 4 lncRNAs were prognostically favorable, while 16 lncRNAs were prognostically unfavorable (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Based on these 20 disulfidptosis-related lncRNAs, LASSO Cox regression analysis was performed on the training set to determine the optimal prediction score and further establish the prognostic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). We then assigned each patient a risk score based on the formula for the prognostic model [risk score = (ATP2A1-AS1*0.418)-(AC011815.1*1.296)+(AC013652.1*1.237)+(AC109992.2*1.974)-(AC069549.1*0.688)-(AC005034.5*0.456)-(SUCLG2-AS1*1.929)+(AP003555.1*1.042)+(AL590101.1*0.500)].\u003c/p\u003e \u003cp\u003eIn addition, we analyzed the correlations between the expression levels of the 9 lncRNAs and 10 disulfidptosis-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). To further confirm the predictive value of the model for CRC, we evaluated its performed in the overall set, training set, and test set. The overall survival curves of the overall cohort and training cohort showed that the prognosis of the low-risk subgroup was significantly better than that of the high-risk subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). However, the difference between the high-risk and low-risk groups in the test group was not obvious (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In addition, we plotted the progression-free survival curve of patients and found that patients in the low-risk group survived better than those in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Then, we further analyzed the expression patterns of the 9 disulfidptosis-related lncRNAs in the overall set, training set, and test set. As the disease progressed, the survival rate of patients in the high-risk group decreased, and this trend was not observed for the low-risk group in the three cohorts. Moreover, there was a positive correlation between the risk score and the probability of death in each cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-M) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe model risk score is an independent prognostic factor\u003c/h2\u003e \u003cp\u003eCox regression analysis was performed to further evaluate whether the prognostic model was influenced by other clinical factors. We included four clinical characteristics of CRC patients, namely, age, sex, tumor stage, and risk score. According to univariate analysis, tumor stage, with a hazard ratio of 2.079 (1.637\u0026thinsp;\u0026minus;\u0026thinsp;2.641), and the risk score, with a hazard ratio of 1.057 (1.039\u0026thinsp;\u0026minus;\u0026thinsp;1.074), are two risk factors that affect prognosis. In addition, the multivariate Cox regression analysis revealed two independent prognostic factors\u0026mdash; the risk score with a hazard ratio of 1.057 (1.036\u0026thinsp;\u0026minus;\u0026thinsp;1.079) and tumor stage with a hazard ratio of 2.144 (1.690\u0026thinsp;\u0026minus;\u0026thinsp;2.720) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). To further evaluate the predictive accuracy of the lncRNA-based prognostic model, ROC curve analysis was performed. The AUC values for 1-, 3-, and 5-year survival were 0.652, 0.717, and 0.710, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The ROC curve showed the high accuracy of our prognostic model. Our prognostic model was almost as accurate as tumor stage in terms of prognosis prediction (AUC values of 0.717 versus 0.737) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Finally, the concordance index of the risk score and other clinical features showed that the risk score performed better than other clinical features, such as sex, age and tumor stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a nomogram by combining the risk score with other clinical characteristics\u003c/h2\u003e \u003cp\u003eOur findings suggested that the risk score can serve as a good predictor of prognosis. We combined the risk score with clinical information to construct a nomogram. The nomogram was applied for randomly selected patients in the cohort, which revealed 1-, 3-, and 5-year survival rates of 0.751, 0.478, and 0.276, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A calibration chart was used to demonstrate the accuracy of the nomogram predictions, and the results showed good consistency with the actual results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePatient risk scores correlate well with clinical characteristics\u003c/h2\u003e \u003cp\u003eThe risk score was also a good predictor of survival in patients with tumor stages I\u0026thinsp;\u0026minus;\u0026thinsp;II. There was a significant difference in survival between the high- and low-risk groups, but we found no significant difference in survival between the high- and low-risk groups in our analysis of patients with stage III\u0026thinsp;\u0026minus;\u0026thinsp;IV disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePCA and biological process enrichment analysis\u003c/h2\u003e \u003cp\u003ePCA was used to compare samples from the low-risk group and the high-risk group, and a three-dimensional scatter plot was drawn to display the spatial distribution of the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). The results showed that the samples grouped according to the risk score had obvious clustering characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further investigated the molecular mechanism of disulfidptosis-related lncRNAs and their effect on CRC tumorigenesis and progression. The results of GO analysis showed that the lncRNA-related genes were related to many factors, such as receptor ligand activity, distal axons and positive regulation of secretion. Furthermore, in the molecular biological process (BP) category, the lncRNA-related genes were mainly enriched in BPs such as positive regulation of secretion, skin development and positive regulation of secretion by the cell. In the cellular component (CC) category, the genes were enriched mainly in distal axons. In the molecular function (MF) category, they were enriched in receptor ligand activity, glycosaminoglycan binding and G protein\u0026thinsp;\u0026minus;\u0026thinsp;coupled receptor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003eIn addition, we performed GSEA. GSEA showed that the top five significantly enriched cellular processes were epidermis development, keratinocyte differentiation, skin development, cornified envelope and intermediate filament composition in the high-risk group and DNA replication-dependent chromatin assembly, nucleosome assembly, nucleosome organization, the nucleosome and the structural constituent of chromatin in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, H) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in immune microenvironment features between the high- and low-risk groups\u003c/h2\u003e \u003cp\u003eThe tumor immune microenvironment plays a key role in determining tumor progression. In the investigation of immune characteristics in the high-risk group, the samples were ranked by risk score (low to high) and the proportions of various immune cells were estimated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Next, we used the CIBERSORT algorithm to determine the proportions of different immune cell types in colorectal tissues. We found that the high-risk group had less infiltration of resting memory CD4 T cells, while the proportions of resting NK cells and monocytes were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In addition, we analyzed 22 immune functions, eight of which had lower scores in the high-risk group than in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In conclusion, these results showed that high-risk CRC according to our disulfidptosis-related lncRNA model classification may cause the progression of tumor disease and a decrease in overall survival by impairing the immune response of the tumor microenvironment..\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTMB analysis of CRC patients in the high- and low-risk groups classified by the disulfidptosis-related lncRNA model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe high-risk groups had high TMB scores, and the 15 most frequently mutated genes and their mutation types are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA. Similarly, the results for the low-risk group are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB. We found that patients with a low TMB had a much greater overall survival rate than those with a high TMB. Next, we divided CRC patients into four subgroups based on the risk score and TMB to investigate the impact of these two factors on overall survival. We found that patients with low TMB and a low risk score exhibited a better prognosis, and their 8-year survival rate was approximately 65%. Compared with patients in the other groups, patients with high TMB and a high risk score had the poorest prognosis, and the 3- and 5-year survival rates were the lowest (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of drug sensitivity in different subgroups\u003c/h2\u003e \u003cp\u003eTo evaluate the clinical value of this model and identify effective drugs, we analyzed the sensitivity to several common CRC drugs in the GDSC and CTRP databases. There were differences in drug sensitivity among patients with different risk scores. Compared with patients in the low-risk group, patients in the high-risk group were more sensitive to savolitinib, TAF1_5496, AGI\u0026thinsp;\u0026minus;\u0026thinsp;5198, AZD4547, AZD5991, AZD6482, GSK591, IAP_5620, KU-55933, OF-1 and venetoclax. Patients in the low-risk group were more sensitive to OSI-027 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression and validation of prognostic disulfidptosis-related lncRNAs in CRC\u003c/h2\u003e \u003cp\u003eIn this study, we analyzed the differences in the expression of nine disulfidptosis-related lncRNAs between primary CRC tissues and normal tissues via the UALCAN online database. AC005034.5, AC013652.1, AC109992.2, AC003555.1 and ATP2A1-AS1 levels were significantly higher in CRC than in normal tissues. The expression of AC011815.1, AC069549.1 and SUCLG2-AS1 was significantly lower in CRC (AL590101.1 was not found in the database)(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further corroborate the independent prognostic value of disulfidptosis-related lncRNAs in CRC patients, we performed a prognostic analysis of ATP2A1-AS1 and SUCLG2-AS1 using the Kaplan\u0026ndash;Meier Plotter database (AC011815.1, AC013652.1, AC109992.2, AC069549.1, AC005034.5, AP003555.1 and AL590101.1 were not found in the database). ATP2A1-AS1 was identified as a protective prognostic factor (HR\u0026thinsp;=\u0026thinsp;0.78 (0.61\u0026ndash;1), p\u0026thinsp;=\u0026thinsp;0.045), while SUCLG2-AS1 (HR\u0026thinsp;=\u0026thinsp;1.49 (1.18\u0026ndash;1.89), p\u0026thinsp;=\u0026thinsp;0.00094) indicated a poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eColorectal cancer (CRC) is a common cancer with a high lethality rate worldwide. While combined treatment approaches, including surgery and chemotherapy, have significantly improved survival rates for CRC patients, 40% of patients still struggle with challenges related to tumor resistance and recurrence\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The identification of novel biomarkers is crucial for understanding the biological behavior and progression of the disease and for guiding prognosis-based treatment decision making. Disulfidptosis is a prognostic marker in various tumor types, and targeting this newly discovered form of cell death is considered a potential therapeutic strategy for cancer treatment\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. LncRNAs play an important role in regulating the malignant behavior of tumor cells and have been proven to be potential biomarkers and targets for cancer diagnosis and treatment. To date, lncRNAs associated with disulfidptosis remain largely unknown, and their prognostic significance in CRC is unclear. In this study, we identified lncRNAs whose levels were correlated with those of disulfidptosis-related genes and established a prognostic model for CRC consisting of nine disulfidptosis-related lncRNAs. Moreover, tumor immune infiltration and drug sensitivity were evaluated in the model risk groups, indicating that this model is an intuitive scientific tool that can guide prognosis evaluation and treatment planning.\u003c/p\u003e \u003cp\u003eIn our study, we randomly divided CRC patients into a training group and a test group for model validation. lncRNAs associated with the survival of CRC patients were included in the model. We found that 20 disulfidptosis-related lncRNAs were significantly correlated with prognosis. A risk score model containing 9 disulfidptosis-related prognostic lncRNAs was established using LASSO regression analysis. In previous studies, ATP2A1-AS1 was used to construct prognostic models related to cuproptosis and autophagy in cervical cancer and cellular senescence-related multiple myeloma\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. AC013652.1 has been demonstrated to have unique functional features and clinical significance in various cancers, including gastric cancer and colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. AC005034.5 has been used to construct prognostic models in osteosarcoma\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. SUCLG2-AS1 was found to be abnormally expressed in acute myeloid leukemia and a variety of tumors, affecting the development of nasopharyngeal carcinoma, triple-negative breast cancer, clear cell renal cell carcinoma and gastric cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Moreover, AP003555.1 was identified and validated as a novel ferroptosis-related and oxidative stress-related lncRNA and incorporated into a prognostic model for colon cancer\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, the roles and functions of AC011815.1, AC109992.2, AC069549.1 and AL590101.1 have not been previously reported.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were performed. This model risk score, which was calculated for each patient and used to classify them into high- and low-risk groups, was identified as an independent prognostic factor for CRC. ROC curve analysis revealed that the AUCs were close to or exceeded 0.7 at 1, 3, and 5 years, highlighting the high accuracy of our prognostic model. The C-index curves demonstrated that this factor had greater specificity than other clinical factors. Moreover, our findings suggested that the risk score can serve as a good predictor of prognosis. We combined the risk score with clinical factors to construct a nomogram. A calibration chart was used to assess the accuracy of the nomogram predictions, the predictions showed good consistency with the actual results.\u003c/p\u003e \u003cp\u003eBy further investigating immune characteristics in high-risk populations, we found that the high-risk group (according to the risk score) had less infiltration of resting CD4\u0026thinsp;+\u0026thinsp;memory T cells but greater infiltration of resting NK cells and monocytes than the low-risk group, which showed that the decrease in overall survival and increase in disease stage in the high-risk CRC group based on our disulfidptosis-related lncRNA model may be related to impairment of the immune response in the tumor microenvironment. TMB is a potential predictive biomarker of solid tumors. In our study, patients with a low TMB had a much greater overall survival rate than those with a high TMB. We divided CRC patients into four subgroups based on the risk score and TMB to investigate the impact of these two factors on overall survival. We found that patients with low TMB and low risk exhibited a better prognosis, and their 8-year survival rate was approximately 65%. Compared with patients in the other groups, patients with high TMB and high risk scores had the poorest prognosis. Drug resistance is one of the main reasons for the failure of tumor therapy. To evaluate the clinical value of this model and identify effective drugs, we analyzed the sensitivity of several common CRC drugs in the GDSC and CTRP databases. There were differences in drug sensitivity among patients with different risk scores. Compared with patients in the low-risk group, patients in the high-risk group were more sensitive to savolitinib, TAF1_5496, AGI\u0026thinsp;\u0026minus;\u0026thinsp;5198, AZD4547, AZD5991, AZD6482, GSK591, IAP_5620, KU-55933, OF-1 and venetoclax. Patients in the low-risk group were more sensitive to OSI-027. These results suggest that our disulfidptosis-related lncRNA model is a potential tool for predicting the response of CRC patients to other common antineoplastic drugs.\u003c/p\u003e \u003cp\u003eIn summary, a prediction model based on 9 disulfidptosis-related lncRNAs showed high accuracy, and the model in this paper revealed the immune microenvironment of high-risk and low-risk patients and predicted the immunotherapy response of CRC patients. This study provides preliminary insights into the association between disulfidptosis and the tumor immune response. However, this study also has limitations. Our study mainly relied on database data for correlation analysis and lacked validation of basic experimental data. In the future, validation at the tissue, cellular and animal levels will be necessary to elucidate the biological pathways by which disulfidptosis-related lncRNAs exert their effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we established a disulfidptosis-related lncRNA model that holds promise as a reliable prognostic marker and predictor of immunotherapy response in CRC patients; it can also be used to assess the immune cell infiltration landscape and the response to targeted therapy in CRC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the 2023 Clinical Medical Excellent Talent Cultivating Program of Hebei (grant no. ZF2023246) and the 2024 Medical Science Research Project of Hebei Provincial Health Commission (no. 20240586).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYuewen Qi wrote, drafted, reviewed and edited the interpretation of the results and analyzed the data; Wenzheng Zhang performed the experiments and wrote the paper; Haowen Qi and Lei Wang collected the tissue samples and performed the experiments; Bingqing Li designed the research study and provided clinical advice. All the authors contributed to this article and approved the final manuscript. All the authors have final responsibility for the decision to submit all the data to the study for publication. All participants provided informed consent, participated in the study and agreed to publish their images/data in an online open access publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGeorge A.T., Aggarwal S., Dharmavaram S., Menon A., Dube M., Vogler M., Foden P., Field A. Regional variations in UK colorectal cancer screening and mortality. [(accessed on 30 June 2020) Lancet. 2020 392:277\u0026ndash;278. doi: 10.1016/S0140-6736(18)31208-X. \u003c/li\u003e\n\u003cli\u003eSiegel R.L., Miller K.D., Jemal A. Cancer statistics, 2019. CA Cancer J. Clin. 2019;69:7\u0026ndash;34. doi: 10.3322/caac.21551.\u003c/li\u003e\n\u003cli\u003eArnold M., Sierra M.S., Laversanne M., Soerjomataram I., Jemal A., Bray F. 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PMID: 36869292; PMCID: PMC9985255.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4435447/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4435447/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) is a common cancer with high mortality rates worldwide. Disulfidptosis is an emerging mode of cancer cell death. In this study, disulfidptosis-related lncRNAs were identified by screening and incorporated into a prognostic model to predict the prognosis and immunotherapy response of colorectal cancer (CRC), providing a new and effective guide for clinical decision making. Transcriptome and clinical data of CRC patients and normal controls were obtained from The Cancer Genome Atlas (TCGA). Pearson correlation, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify disulfidptosis-related lncRNAs. A risk scoring model was constructed, and its predictive performance was comprehensively validated. An accurate nomogram was constructed for CRC prognosis prediction. Model reliability was verified via principal component, survival and receiver operating characteristic (ROC) curve analyses. GO analysis and GSEA were used to identify cellular pathways relevant to the model. Immune cell infiltration was studied via the ESTIMATE and CIBERSORT algorithms. The association of tumor mutational burden (TMB) with the model-derived risk scores was assessed using single-nucleotide variant data. Finally, tThe clinical value of the model was evaluated through the GDSC and CTRP databases, and effective drugs were predicted. A prognostic risk model containing 9 disulfidptosis-related lncRNAs (ATP2A1-AS1, AC011815.1, AC013652.1, AC109992.2, AC069549.1, AC005034.5, SUCLG2-AS1, AP003555.1 and AL590101.1) was successfully constructed. There were significant difference in survival rates between the high-risk and low-risk groups (based on the median risk score) in the training and validation datasets. The risk score serves as an independent prognostic factor when combined with clinical variables. GSEA revealed that the high-risk group was enriched in the cellular processes of epidermis development, kidney differentiation and skin development. The prognostic model could stratify CRC patients into two distinct risk score groups. A high risk score independently predicted poor overall survival and was correlated with reduced immune cell infiltration, high TMB, and decreased tumor immune response activity. Immune checkpoint blockade might improve survival in high-risk CRC patients, whereas low-risk patients might be more responsive to targeted therapy and diverse kinase inhibitors. In summary, we established a disulfidptosis-related lncRNA model that holds promise as a reliable marker of CRC prognosis and immunotherapy response and can be also be used to predict the immune cell infiltration landscape and targeted therapy response.\u003c/p\u003e","manuscriptTitle":"A novel disulfidptosis-related lncRNA signature in colorectal cancer for predicting prognosis, tumor immune microenvironment features and drug sensitivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 18:38:15","doi":"10.21203/rs.3.rs-4435447/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0402a21-2ec0-4d17-bfa8-1c1f7cd7b9cb","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32648868,"name":"Biological sciences/Cancer"},{"id":32648869,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-06-13T08:32:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 18:38:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4435447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4435447","identity":"rs-4435447","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}