Prognostic model for survival in patients with advanced pancreatic cancer receiving palliative chemotherapy.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 248-248
Author(s):  
Yu Uneno ◽  
Tadayuki Kou ◽  
Masashi Kanai ◽  
Michio Yamamoto ◽  
Peng Xue ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Poor Prognosis ◽  
Prognostic Model ◽  
Palliative Chemotherapy ◽  
Cox Regression ◽  
Advanced Pancreatic Cancer ◽  
Prognostic Index ◽  
Prognostic Models

248 Background: The prognosis of patients with advanced pancreatic cancer (APC) is extremely poor. Several clinical and laboratory factors have been known to be associated with prognosis of APC patients. However, there are few clinically available prognostic models predicting survival in APC patients receiving palliative chemotherapy. Methods: To construct a prognostic model to predict survival in APC patients receiving palliative chemotherapy, we analyzed the clinical data from 306 consecutive patients with pathologically confirmed APC who received palliative chemotherapy. We selected six independent prognostic factors which remained independent prognostic factors after multivariate analysis. Thereafter, we rounded the regression coefficient (β) for each independent prognostic factor derived from the Cox regression equation (HR = eβ) and developed a prognostic index (PI). Results: Developed prognostic index (PI) was as follows: PI = 2 (if performance status score 2–3) + 1 (if metastatic disease) + 1 (if initially unresectable disease) + 1 (if carcinoembryonic antigen level ≥5.0 ng/ml) + 1 (if carbohydrate antigen 19-9 level ≥1000 U/ml) + 2 (if neutrophil–lymphocyte ratio ≥5). The patients were classified into three prognostic groups: favorable (PI 0–1, n = 73), intermediate (PI 2–3, n = 145), and poor prognosis (PI 4–8, n = 88). The median overall survival for each prognostic group was 16.5, 12.3 and 6.2 months, respectively, and the 1-year survival rates were 67.3%, 51.3%, and 19.1%, respectively (P < 0.01). The c index of the model was 0.658. This model was well calibrated to predict 1-year survival, in which overestimation (2.4% and 0.2% in the favorable and poor prognosis groups, respectively) and underestimation (3.6% in the intermediate prognosis group) were observed. Conclusions: This prognostic model based on readily available clinical factors would help clinicians in estimating the overall survival in APC patients receiving palliative chemotherapy.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as prognostic factors for locally advanced pancreatic cancer patients.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 326-326
Author(s):  
Byung Min Lee ◽  
Seung Yeun Chung ◽  
Jee Suk Chang ◽  
Kyong Joo Lee ◽  
Si Young Song ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Cancer Patients ◽  
Locally Advanced ◽  
Advanced Pancreatic Cancer ◽  
Locally Advanced Pancreatic Cancer ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Pre Treatment

326 Background: It is well known that locally advanced pancreatic cancer patients have a poor prognosis. Recently, hematologic markers showing systemic inflammatory status such as neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) have aroused much attention due to its potential to predict patient survival. In this study, we investigated whether pre-treatment NLR and PLR independently and in combination would be significant prognostic factors for survival in locally advanced pancreatic cancer patients. Methods: A total of 497 locally advanced (borderline resectable and unresectable) pancreatic cancer patients who received neoadjuvant or definitive chemoradiotherapy (CCRT) between January 2005 and December 2015 were included in this study. NLR and PLR prior to the start of treatment within 2 weeks were defined as pre-treatment NLR and PLR. We divided the patients with the median values of pre-treatment NLR and PLR; NLR < 2.44 group (n = 248), NLR ≥ 2.44 group (n = 249), PLR < 149 group (n = 248) and PLR ≥ 149 (n = 249) group. Overall survival (OS) and progression-free survival (PFS) were compared between each group for NLR and PLR. Results: Median overall survival was 15.7 months (range, 2.3-128.5 months). For NLR, the OS, PFS rates were significantly lower in the NLR ≥ 2.44 group, with 1-year OS rates of 67.9% and 61.5% (p = 0.003) and 1-year PFS rates of 38.1% and 32.4% (p = 0.003), for NLR < 2.44 and ≥ 2.44 group, respectively. The PLR ≥ 149 group also showed significantly poorer OS and PFS than PLR < 149 group. The 1-year OS rates were 68.1% and 61.3% (p = 0.029) and 1-year PFS rates were 37.9% and 32.5% (p = 0.027), for PLR < 149 and ≥ 149 group, respectively. When multivariate analysis was performed, NLR ≥ 2.44 remained as a significant adverse factor for OS (p = 0.011) and PFS (p = 0.026). PLR > 149 also proved to be a significant factor for poorer OS (p = 0.003) and PFS (p = 0.021). Conclusions: Elevated pre-treatment NLR and PLR independently and in combination significantly predicted poor OS and PFS. Pre-treatment NLR and PLR are useful prognostic factors for OS and PFS in locally advanced pancreatic cancer patients.

scholarly journals Identify Prognostic Factors and Establish a Risk Stratification Model to Predict Cancer-Specific Survival for Patients with Pancreatic Cancer Liver Metastasis: A Population-Based Study

2021 ◽  
Author(s):  
Biyang Cao ◽  
Chenchen Wu ◽  
Letian Zhang ◽  
Jing Wang
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factors ◽  
Liver Metastasis ◽  
Classification System ◽  
Cox Regression ◽  
Risk Classification ◽  
Prognostic Models ◽  
Cancer Specific Survival ◽  
Discriminative Ability ◽  
Cox Regression Analysis

Abstract Background Pancreatic cancer liver metastasis (PCLM) is a commonly fatal disease, but there are few prognostic models for these entities. The purpose of this study is to investigate prognostic factors based on clinicopathological characteristics and establish a prognostic nomogram predicting the cancer-specific survival (CSS) for PCLM patients. Methods The characteristics of 6015 patients with PCLM between 2010 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database were analyzed. Prognostic factors and nomogram predicting CSS were developed by Cox proportional hazard regression model. The predictive accuracy and discriminative ability of nomogram were assessed by concordance index (C-index), calibration curve, decision curve analyses (DCAs) and receiver operating characteristic (ROC) curve. Moreover, a risk classification system was built according to the cut-off values off the nomogram. Results Based on the univariate and multivariate Cox regression analysis, significant prognostic factors were identified and included to establish the nomogram for CSS. The median survival time (MST) for all patients is 4.0 months (95% confidence interval [CI]:3.8–4.2) and CSS at 6, 12 and 18 months was 34.12%, 15.63% and 7.83%, respectively. The C-index of nomogram was 0.693 (95%CI: 0.689–0.697) and all verification results showed an accurate and discriminative ability in predicting CSS. Significant differences in Kaplan–Meier curves were observed in patients stratified into different risk groups (p < 0.001), with MST of 7.0 months (95% CI: 6.7–7.3), 3.0 months (95% CI: 2.7–3.3), and 2.0 months (95% CI: 1.8–2.2), respectively. Conclusions A prognostic nomogram and corresponding risk classification system were proposed to predict CSS for PCLM.

scholarly journals Competing Risk Analysis of Outcomes of Unresectable Pancreatic Cancer Patients Undergoing Definitive Radiotherapy

2022 ◽  
Vol 11 ◽  
Author(s):  
Yi-Lun Chen ◽  
Chiao-Ling Tsai ◽  
Jason Chia-Hsien Cheng ◽  
Chun-Wei Wang ◽  
Shih-Hung Yang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factors ◽  
Cox Regression ◽  
Locally Advanced ◽  
Reduction Rate ◽  
Advanced Pancreatic Cancer ◽  
Competing Risk ◽  
Treatment Modalities ◽  
Definitive Radiotherapy ◽  
Systemic Treatments

PurposeWe investigated potential factors, including clinicopathological features, treatment modalities, neutrophil-to-lymphocyte ratio (NLR), carbohydrate antigen (CA) 19-9 level, tumor responses correlating with overall survival (OS), local progression (LP), and distant metastases (DMs), in patients with locally advanced pancreatic cancer (LAPC) who received definitive radiotherapy (RT).MethodsWe retrospectively analyzed demographic characteristics; biologically effective doses (BED10, calculated with an α/β of 10) of RT; and clinical outcomes of 57 unresectable LAPC (all pancreatic adenocarcinoma) patients receiving definitive RT using modern techniques with and without systemic therapy between January 2009 and March 2019 at our institution. We used Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 to evaluate the radiographic tumor response after RT. The association between prognostic factors and OS was assessed using the Kaplan–Meier analysis and a Cox regression model, whereas baseline characteristics and treatment details were collected for competing-risk regression of the association with LP and DM using the Fine–Gray model.ResultsA median BED10 of 67.1 Gy resulted in a disease control rate of 87.7%, and the median OS was 11.8 months after a median follow-up of 32.1 months. The 1-year OS rate, cumulative incidences of LP, and DM were 49.2%, 38.5%, and 62.9%, respectively. Multivariate analyses showed that pre-RT NLR ≥3.5 (adjusted hazard ratio [HR] = 8.245, p &lt; 0.001), CA19-9 reduction rate ≥50% (adjusted HR = 0.261, p = 0.005), RT without concurrent chemoradiotherapy (adjusted HR = 5.903, p = 0.004), and administration of chemotherapy after RT (adjusted HR = 0.207, p = 0.03) were independent prognostic factors for OS. Positive lymph nodal metastases (adjusted subdistribution HR [sHR] = 3.712, p = 0.003) and higher tumor reduction after RT (adjusted sHR = 0.922, p &lt; 0.001) were significant prognostic factors for LP, whereas BED10 ≥ 67.1 Gy (adjusted sHR = 0.297, p = 0.002), CA19-9 reduction rate ≥50% (adjusted sHR = 0.334, p = 0.023), and RT alone (adjusted sHR = 2.633, p = 0.047) were significant prognostic factors for DM.ConclusionOur results indicate that pre-RT NLR and post-RT monitoring of CA19-9 and tumor size reduction can help identify whether patients belong to the good or poor prognostic group of LAPC. The incorporation of new systemic treatments during and after a higher BED10 RT dose for LAPC patients is warranted.

scholarly journals Clinical Prognostic Model of Autophagy-Related LncRNA Genes in The Esophageal Adenocarcinoma (EAC) to Predicting Overall Survival (OS) of The Patients：The Evidence From Bioinformatic Analysis

2021 ◽  
Author(s):  
Liusheng Wu ◽  
Xiaoqiang Li ◽  
Jixian Liu ◽  
Da Wu ◽  
Dingwang Wu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Esophageal Adenocarcinoma ◽  
Clinical Data ◽  
Roc Curve ◽  
Prognostic Model ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Prognostic Models ◽  
Cox Regression Analysis

Abstract Objective: Autophagy-related LncRNA genes play a vital role in the development of esophageal adenocarcinoma.Our study try to construct a prognostic model of autophagy-related LncRNA esophageal adenocarcinoma, and use this model to calculate patients with esophageal adenocarcinoma. The survival risk value of esophageal adenocarcinoma can be used to evaluate its survival prognosis. At the same time, to explore the sites of potential targeted therapy genes to provide valuable guidance for the clinical diagnosis and treatment of esophageal adenocarcinoma.Methods: Our study have downloaded 261 samples of LncRNA-related transcription and clinical data of 87 patients with esophageal adenocarcinoma from the TCGA database, and 307 autophagy-related gene data from www.autuphagy.com. We applied R software (Version 4.0.2) for data analysis, merged the transcriptome LncRNA genes, autophagy-related genes and clinical data, and screened autophagy LncRNA genes related to the prognosis of esophageal adenocarcinoma. We also performed KEGG and GO enrichment analysis and GSEA enrichment analysis in these LncRNA genes to analysis the risk characteristics and bioinformatics functions of signal transduction pathways. Univariate and multivariate Cox regression analysis were used to determine the correlation between autophagy-related LncRNA and independent risk factors. The establishment of ROC curve facilitates the evaluation of the feasibility of predicting prognostic models, and further studies the correlation between autophagy-related LncRNA and the clinical characteristics of patients with esophageal adenocarcinoma. Finally, we also used survival analysis, risk analysis and independent prognostic analysis to verify the prognosis model of esophageal adenocarcinoma.Results: We screened and identified 22 autophagic LncRNA genes that are highly correlated with the overall survival (OS) of patients with esophageal adenocarcinoma. The area under the ROC curve（AUC=0.941）and the calibration curve have a good lineup, which has statistical analysis value. In addition, univariate and multivariate Cox regression analysis showed that the autophagy LncRNA feature of this esophageal adenocarcinoma is an independent predictor of esophageal adenocarcinoma.Conclusion: These LncRNA screened and identified may participate in the regulation of cellular autophagy pathways, and at the same time affect the tumor development and prognosis of patients with esophageal adenocarcinoma. These results indicate that risk signature and nomogram are important indicators related to the prognosis of patients with esophageal adenocarcinoma.

scholarly journals Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer

2019 ◽  
Vol 50 (3) ◽  
pp. 261-269
Author(s):  
Jieyun Zhang ◽  
Yue Yang ◽  
Xiaojian Fu ◽  
Weijian Guo
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Prognosis ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Development And Validation

Abstract Purpose Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. Methods Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. Results The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. Conclusions Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

A New Prognostic Model for Extranodal Natural Killer/T Cell Lymphoma, Nasal Type

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1769-1769
Author(s):  
Qingqing Cai ◽  
Xiaolin Luo ◽  
Ken H. Young ◽  
Huiqiang Huang ◽  
Guanrong Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
High Risk ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
Prognostic Index ◽  
Roc Curves ◽  
Risk Groups ◽  
Survival Outcomes ◽  
Nasal Type

Abstract Background Extranodal natural killer (NK)/T–cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis. A better risk stratification is beneficial for clinical management in affected patients. Our recent study has shown that fasting blood glucose (FBG) was a novel, prognostic factor, (Cai et al, British Journal of Cancer, 108: 380–386,2013). This finding has not been integrated in the previous prognostic models for ENKTL Therefore, we aimed to design a new prognostic model, including FBG, for ENKTL which supports to identify high–risk patients eligible for advanced or more aggressive therapy. Patients and methods 158 newly diagnosed patients with ENKTL were analyzed between January 2003 and January 2011 at Sun Yat–sen University Cancer Center, China. Overall survival (OS) and progression free survival (PFS) were estimated using the Kaplan–Meier method. The significance of differences between survival was tested using the Log–rank test. Significant variables in the univariate analysis were selected as variables for the multivariate analysis of survival. The latter was performed by the Cox regression mode. We constructed receiver operating characteristic (ROC) curves and compared the areas under the ROC curves of total protein (TP), FBG, Korean Prognostic Index (KPI) and their combinations in comparison to the survival outcome. Results Of 158 patients, 156 patients had complete clinical information for the parameters of the International Prognostic Index (IPI) model and KPI model. The estimated 5–year overall survival rate in 158 patients was 59.2%. Independent prognostic factors included TP < 60 g/L, FBG > 100 mg/dL, KPI score ≥ 2. A new prognostic model was constructed by combining these prognostic factors: Group 1 (64 cases, 41.0%), no adverse factors; Group 2 (58 cases, 37.2%), one adverse factor; and Group 3 (34 cases, 21.8%), two or three adverse factors. The 5–year overall survival of these groups were 88.9%, 35.6% and 12.7%, respectively (p < 0.001). The survival curves according to the new prognostic model are shown in Fig. 1. The new model categorized three groups with significantly different survival outcomes. The new prognostic model was also efficient in discriminating the patients with low to low–intermediate risk IPI group and high–intermediate to high risk IPI group into three subgroups with different survival outcomes (p < 0.001). The KPI model balanced the distribution of patients into different risk groups better than IPI prognostic model (score 0: 12 cases, 7.7%; score 1: 38 cases, 24.4%; score 2: 42 cases, 26.9%; score 3–4: 64 cases, 41.0%), and it was able to differentiate patients with different survival outcomes (p < 0.001). In addition, the new prognostic model had a better prognostic value than did KPI model alone (p < 0.001), suggesting that TP and FBG reinforced the prognostic ability of KPI model (Table 1). Conclusions The new prognostic model we proposed for ENKTL, including the new prognostic indicator total protein and FBG, demonstrated balanced distribution of patients into different risk groups with better prognostic discrimination as compared to KPI model alone. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Diabetes mellitus indicate poor prognosis in uterine serous cancer patients

2021 ◽  
Author(s):  
Yunyun Liu ◽  
Jing Li ◽  
Zhibo Cheng ◽  
Guocai Xu ◽  
Yongpai Peng ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Figo Stage ◽  
Chi Square ◽  
Institutional Review ◽  
Independent Risk Factors

Abstract Purpose. We aimed to find prognostic factors for uterine serous cancer(USC) patients in a retrospective study.Methods. 51 USC patients between 2010-2020 were enrolled. All pathological specimens were reviewd. The research protocol was approved by Institutional Review Board and all patients were informed consent before the study began. Statistics were done using SPSS 25.0, T test and chi-square analyses were used to compare differences, the overall survival(OS) was estimated with Kaplan-Meier(KM) analysis, univariate and multivariate Cox regression analyses were utilized to find prognostic factors.Results. The median overall survival(OS) and progressive free survival(PFS) were 75.94 and 63.49 months, respectively. Diagnosed with diabetes mellitus(P=0.006, HR=6.792, 95%CI=1.726-26.722) and CA125＞28U/ml(P=0.006, HR=7.136, 95%CI=1.780-28.607) before surgery were independent risk factors for OS, advanced FIGO stage(P=0.001, HR=10.628, 95%CI=2.894-39.026) and DM(P=0.003, HR=6.327, 95%CI=1.875-21.354) were independent factors for PFS. Age≤52, , tumor size≥2.5cm and cervical mucosal infiltration may indicate poor prognosis but were not independent risk factors. Hypertension patients with routine medical treatment tend to survive longer, but there was no statistical differences in OS and PFS compared to patients with normal blood pressure.Conclusion. In addition to surgical and adjuvant treatments, gynecologists should focus more on the comorbid conditions of USC patiens, especially for DM.

scholarly journals Construction of a Prognostic Model for Predicting Overall Survival of Patients with Colorectal Cancer

2020 ◽  
Author(s):  
Shuwen Han ◽  
Kefeng Ding
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Levulinic Acid ◽  
Prognostic Model ◽  
Cox Regression ◽  
Aminolevulinic Acid ◽  
Risk Group ◽  
Prognostic Models ◽  
Cox Regression Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies. The purpose of this study is to construct a prognostic model for predicting the overall survival (OS) in patients with CRC. Methods: The mRNA-seq and miRNA-seq data of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) between tumor and normal tissues were screened. The Kaplan-Meier and univariate Cox regression analysis were used to screen the survival-related genes. Functional enrichment analysis of survival-related genes was conducted, followed by protein-protein interaction (PPI) analysis. Subsequently, the potential drugs targeting differentially expressed mRNAs (DE-mRNAs) were investigated. Multivariate Cox regression analysis was then conducted to screen the independent prognostic factors, and these genes were used to establish a prognostic model. A receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) value of ROC was calculated to evaluate the specificity and sensitivity of the model. Results: A total of 855 survival-related genes were screened. These genes were mainly enriched in Gene Ontology (GO) terms, such as methylation, synapse organization, and methyltransferase activity; and pathway analysis showed that these genes were significantly involved in N-Glycan biosynthesis and the calcium signaling pathway. PPI analysis showed that aminolevulinate dehydratase (ALAD) and cholinergic receptor muscarinic 2 (CHRM2) served vital roles in the development of CRC. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for CRC treatment. The prognostic models were built and the patients were divided into high-risk and low-risk groups based on the median of risk score (RS) as screening threshold. The OS for patients in the high-risk group was markedly shorter than that for patients in the low-risk group. Meanwhile, kazal type serine peptidase inhibitor domain 1 (KAZALD1), hippocalcin like 4 (HPCAL4), cadherin 8 (CDH8), synaptopodin 2 (SYNPO2), cyclin D3 (CCND3), and hsa_mir_26b may be independent prognostic factors that could be considered as therapeutic targets for CRC.Conclusion: We established prognostic models that could predict the OS for CRC patients and may assist clinicians in providing personalized and precision treatment in this patient population.Highlights:1. ALAD served a vital role in the development of CRC.2. CHRM2 played a role in CRC development by affecting the calcium signaling pathway.3. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for treating CRC.4. KAZALD1 and HPCAL4 were associated with the OS of CRC.5. CDH8, SYNPO2, CCND3, and hsa-mir-26b were closely related to the prognostic of CRC staging.

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