Development and validation of a prognostic nomogram for early-onset colon cancer

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database

BACKGROUND AND AIM

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression.

METHODS

In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model.

RESULTS

In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival.

CONCLUSIONS

Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Construction and validation of a nomogram for predicting overall survival of patients with stage III/IV early-onset colorectal cancer

Purpose

This study aimed to identify prognostic factors and develop a nomogram for predicting overall survival (OS) in stage III/IV early-onset colorectal cancer (EO-CRC).

Methods

Stage III/IV EO-CRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The datasets were randomly divided (2:1) into training and validation sets. A nomogram predicting OS was developed based on the prognostic factors identified by Cox regression analysis in the training cohort. Moreover, the predictive performance of the nomogram was assessed using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Subsequently, the internal validation was performed using the validation cohort. Finally, a risk stratification system was established based on the constructed nomogram.

Results

Of the 10,387 patients diagnosed with stage III/IV EO-CRC between 2010 and 2015 in the SEER database, 8,130 patients were included. In the training cohort (n=3,071), sex, marital status, race/ethnicity, primary site, histologic subtypes, grade, T stage, and N stage were identified as independent prognostic variables for OS. The 1-, 3-, and 5-year area under the curve (AUC) values of the nomogram were robust in both the training (0.751, 0.739, and 0.723) and validation cohorts (0.748, 0.733, and 0.720). ROC, calibration plots, and DCA indicated good predictive performance of the nomogram in both the training and validation sets. Furthermore, patients were categorized into low-, middle-, and high-risk groups based on the nomogram risk score. Kaplan-Meier curve showed significant survival differences between the three groups.

Conclusion

We developed a prognostic nomogram and risk stratification system for stage III/IV EO-CRC, which may facilitate clinical decision-making and individual prognosis prediction.

Development and validation of a nomogram for predicting the prognosis in children with spinal cord injuries

AIMS

This research aims to construct and verify an accurate nomogram for forecasting the 3-, 5-, and 7-year outcomes in pediatric patients afflicted with spinal cord injury (SCI).

METHODS

Pediatric patients with SCI from multiple hospitals in China, diagnosed between Jan 2005 and Jan 2020, were incorporated into this research. Half of these patients were arbitrarily chosen for training sets, and the other half were designated for external validation sets. The Cox hazard model was employed to pinpoint potential prognosis determinants related to the American Spinal Injury Association (ASIA) and Functional Independence Assessment (FIM) index. These determinants were then employed to formulate the prognostic nomogram. Subsequently, the bootstrap technique was applied to validate the derived model internally.

RESULTS

In total, 224 children with SCI were considered for the final evaluation, having a median monitoring duration of 68.0 months. The predictive nomogram showcased superior differentiation capabilities, yielding a refined C-index of 0.924 (95% CI: 0.883-0.965) for the training cohort and a C-index of 0.863 (95% CI: 0.735-0.933) for the external verification group. Additionally, when applying the aforementioned model to prognostic predictions as classified by the FIM, it demonstrated a high predictive value with a C-index of 0.908 (95% CI: 0.863-0.953). Moreover, the calibration diagrams indicated a consistent match between the projected and genuine ASIA outcomes across both sets.

CONCLUSION

The crafted and verified prognostic nomogram emerges as a dependable instrument to foresee the 3-, 5-, and 7-year ASIA and FIM outcomes for children suffering from SCI.

Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic disease management in the community

PURPOSE

​Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community.

PATIENTS AND METHODS

The nomogram prediction model was developed in a cohort of 7200 hypertensive patients at a community health service center in Hongshan District, Wuhan City. The data were collected from January 2022 to December 2022 and randomly divided into modeling and validation groups at a 7:3 ratio. The Lasso regression model was used for data dimensionality reduction, feature selection, and clinical test feature construction. Multivariate logistic regression analysis was used to build the prediction model.

RESULTS

The application of the nomogram in the verification group showed good discrimination, with an AUC of 0.9205 (95% CI: 0.8471-0.9527) and a good calibration effect. Decision curve analysis demonstrated that the predictive model was clinically useful.

CONCLUSION

This study presents a nomogram prediction model that incorporates age, waist-height ratio and elevated density lipoprotein cholesterol (HDL-CHOLESTEROL), which can be used to predict the risk of codeveloping diabetes in hypertensive patients.

Causes of death among early-onset colorectal cancer population in the United States: a large population-based study

Background

Early-onset colorectal cancer (EOCRC) has an alarmingly increasing trend and arouses increasing attention. Causes of death in EOCRC population remain unclear.

Methods

Data of EOCRC patients (1975-2018) were extracted from the Surveillance, Epidemiology, and End Results database. Distribution of death was calculated, and death risk of each cause was compared with the general population by calculating standard mortality ratios (SMRs) at different follow-up time. Univariate and multivariate Cox regression models were utilized to identify independent prognostic factors for overall survival (OS).

Results

The study included 36,013 patients, among whom 9,998 (27.7%) patients died of colorectal cancer (CRC) and 6,305 (17.5%) patients died of non-CRC causes. CRC death accounted for a high proportion of 74.8%-90.7% death cases within 10 years, while non-CRC death (especially cardiocerebrovascular disease death) was the major cause of death after 10 years. Non-cancer death had the highest SMR in EOCRC population within the first year after cancer diagnosis. Kidney disease [SMR = 2.10; 95% confidence interval (CI), 1.65-2.64] and infection (SMR = 1.92; 95% CI, 1.48-2.46) were two high-risk causes of death. Age at diagnosis, race, sex, year of diagnosis, grade, SEER stage, and surgery were independent prognostic factors for OS.

Conclusion

Most of EOCRC patients died of CRC within 10-year follow-up, while most of patients died of non-CRC causes after 10 years. Within the first year after cancer diagnosis, patients had high non-CRC death risk compared to the general population. Our findings help to guide risk monitoring and management for US EOCRC patients.

[Establishment and validation of a predictive nomogram for liver fibrosis in patients with Wilson disease and abnormal lipid metabolism]

OBJECTIVE

To establish and validate predictive nomogram for liver fibrosis in patients with Wilson disease (WD) showing abnormal lipid metabolism.

METHODS

We retrospectively collected the clinical data of 500 patients with WD showing abnormalities in lipid metabolism, who were treated in the Department of Encephalopathy of the First Affiliated Hospital of Anhui University of Chinese Medicine from December, 2018 to December, 2021 and divided into modeling group and validation group. The independent risk factors of liver fibrosis in these patients were screened using LASSO regression and multivariate logistic regression analysis for establishment of the predictive nomogram. The area under the curve (AUC), calibration curve and decision curve of the receiver-operating characteristic curve (ROC) were used for internal and external verification of the nomogram in the modeling and validation group and evaluating the differentiation, calibration and clinical practicability of the model.

RESULTS

Triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (Apo-B) were independent risk factors for the development of liver fibrosis in patients with WD and abnormal lipid metabolism (P < 0.05). The predictive nomogram showed good discrimination, calibration and clinical utility in both the modeling and validation groups.

CONCLUSION

The established predictive nomogram in this study has a high accuracy for early identification and risk prediction of liver fibrosis in patients with WD having abnormal lipid metabolism.

Institutionally validated nomogram predicting prognosis for older patients with nonmetastatic nasopharyngeal carcinoma

Aim: Older adult patients with nonmetastatic nasopharyngeal carcinoma (NPC) have poor outcomes relative to younger patients. The authors' group established a nomogram to predict the overall survival of older adults with NPC and inform patient management. Methods: Cases with NPC (n = 782) were enrolled in this study; clinical data in the Surveillance, Epidemiology, and End Results database from 2010 to 2015 served as the training cohort (n = 657), and patients from Jiangxi Cancer Hospital (n = 125) served as the external validation cohort. Results: Training and external validation cohort C-index, receiver operator characteristics curves and calibration curves showed that our nomogram has great predictive ability. Conclusions: Compared with tumor-node-metastasis staging, this nomogram can help clinicians better predict the prognosis of older adults with nonmetastatic NPC.

NDC1 is a Prognostic Biomarker and Associated with Immune Infiltrates in Colon Cancer

Background

Colon cancer is one of the most lethal cancers in the world. NDC1 is a crucial membrane-integral nucleoporin of nuclear pore complexes. The clinical significance of NDC1 in colon cancer has not been demonstrated to date. Therefore, we determined to evaluate the association between NDC1 and colon cancer using the open-access database.

Methods

The TCGA data of colon cancer were extracted to determine the relationship between NDC1 and the clinical characterization. We assessed the predictive role of NDC1 expression in the survival of patients with colon cancer. Univariate and multivariate Cox proportional hazard models were applied to analyze the association between the clinical factors and prognosis. The TIMER database was used to describe the association between immune cell infiltration and specific gene expression in the colon cancer context. Gene set enrichment analysis (GSEA) was performed based on the TCGA dataset.

Results

A total of 445 colon cancer patients with complete clinical information were included. NDC1 expression was significantly up-regulated in colon cancer tissues compared to adjacent normal tissues. Univariate and multivariate Cox regression analyses showed that NDC1 was an independent prognostic factor. Patients with a higher level of NDC1 expression tend to survive longer compared to those with a lower level of NDC1 expression. The level of the NDC1 expression is significantly associated with TNM stages. Furthermore, we constructed a nomogram to predict the prognosis by using NDC1 as a factor. The expression of NDC1 was significantly associated with infiltration of B cell, CD8+T cells, macrophages, neutrophils, and dendritic cells in colon cancer lesions. Additionally, NDC1 was predominantly enriched in KRAS-related signaling pathways by GSEA.

Conclusion

NDC1 can serve as a prognostic biomarker, which is negatively correlated with aggressiveness and positively associated with immune infiltrates of colon cancer.

Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study

BACKGROUND

Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients.

MATERIALS AND METHODS

A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran's three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve.

RESULTS

The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650-0.734), as well as 0.627 (0.670, 0.686) in the test data.

CONCLUSION

We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.

Development and external validation of a nomogram predicting overall survival after curative resection of colon cancer

OBJECTIVE

To develop and externally validate a prognostic nomogram to predict overall survival (OS) in patients with resectable colon cancer.

METHODS

Data for 50,996 patients diagnosed with non-metastatic colon cancer were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were assigned randomly to the training set (n = 34,168) or validation set (n = 16,828). Independent prognostic factors were identified by multivariate Cox proportional hazards regression analysis and used to construct the nomogram. Harrell's C-index and calibration plots were calculated using the SEER validation set. Additional external validation was performed using a Chinese dataset (n = 342).

RESULTS

Harrell's C-index of the nomogram for OS in the SEER validation set was 0.71, which was superior to that using the 7th edition of the American Joint Committee on Cancer TNM staging (0.59). Calibration plots showed consistency between actual observations and predicted 1-, 3-, and 5-year survival. Harrell's C-index (0.72) and calibration plot showed excellent predictive accuracy in the external validation set.

CONCLUSIONS

We developed a nomogram to predict OS after curative resection for colon cancer. Validation using the SEER and external datasets revealed good discrimination and calibration. This nomogram may help predict individual survival in patients with colon cancer.

Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.

Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.

Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.

Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

Prognostic nomogram to predict the overall survival of patients with early-onset colorectal cancer: a population-based analysis

PURPOSE

The present study aimed to identify independent clinicopathological and socio-economic prognostic factors associated with overall survival of early-onset colorectal cancer (EO-CRC) patients and then establish and validate a prognostic nomogram for patients with EO-CRC.

METHODS

Eligible patients with EO-CRC diagnosed from 2010 to 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training cohort and a testing cohort. Independent prognostic factors were obtained using univariate and multivariate Cox analyses and were used to establish a nomogram for predicting 3- and 5-year overall survival (OS). The discriminative ability and calibration of the nomogram were assessed using C-index values, AUC values, and calibration plots.

RESULTS

In total, 5585 patients with EO-CRC were involved in the study. Based on the univariate and multivariate analyses, 15 independent prognostic factors were assembled into the nomogram to predict 3- and 5-year OS. The nomogram showed favorable discriminatory ability as indicated by the C-index (0.840, 95% CI 0.827-0.850), and the 3- and 5-year AUC values (0.868 and 0.84869 respectively). Calibration plots indicated optimal agreement between the nomogram-predicted survival and the actual observed survival. The results remained reproducible in the testing cohort. The C-index of the nomogram was higher than that of the TNM staging system (0.840 vs 0.804, P < 0.001).

CONCLUSION

A novel prognostic nomogram for EO-CRC patients based on independent clinicopathological and socio-economic factors was developed, which was superior to the TNM staging system. The nomogram could facilitate postoperative individual prognosis prediction and clinical decision-making.

Prognostic effect of a novel long noncoding RNA signature and comparison with clinical staging systems for patients with hepatitis B virus-related hepatocellular carcinoma after hepatectomy

OBJECTIVES

We aimed to establish a novel prognostic long noncoding RNA (lncRNA) signature for hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) patients after hepatectomy and to validate its prognostic efficacy compared with other clinical staging systems.

METHODS

Expression data of 374 HCC samples were retrieved from The Cancer Genome Atlas (TCGA) database. Cox regression analyses were performed to develop the lncRNA model. The expression levels of lncRNAs were detected by qualitative real-time polymerase chain reaction (qRT-PCR) in HBV-HCC. Then the qRT-PCR-based signature and nomogram were constructed and compared with those of other clinical staging systems in a clinical cohort and qRT-PCR, RNA fluorescent in situ hybridization and comprehensive bioinformatics analyses were conducted.

RESULTS

The signature containing five lncRNAs was constructed through TCGA. This model showed the highest predictive efficacy in patients with HBV-HCC. Compared with normal liver tissues, all lncRNAs were highly expressed in HBV-HCC. A four-lncRNA signature containing LINC01116, DDX11-AS1, LUCAT1 and FIRRE was developed based on the qRT-PCR data in a clinical HBV-HCC patient cohort. A Kaplan-Meier analysis indicated that the low-risk group had significantly longer overall survival than the high-risk group. Additionally, the qRT-PCR-based four-lncRNA formula was an independent prognostic factor and had better predictive efficacy for survival (area under the receiver operating characteristic curve 0.875) compared with other clinical staging systems in HBV-HCC. The lncRNA-mRNA co-expression and enrichment analyses revealed the potential regulatory mechanisms of the lncRNA identified.

CONCLUSION

The four-lncRNA model may be an effective prognostic signature and provides potential prognostic biomarkers and therapeutic targets for HBV-HCC.

Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis

Background

This study aimed to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in elderly colorectal cancer (ECRC) patients (Age ≥ 70).

Methods

The clinical variables of patients confirmed as ECRC between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analysis were performed, followed by the construction of nomograms in OS and CSS.

Results

A total of 44,761 cases were finally included in this study. Both C-index and calibration plots indicated noticeable performance of newly established nomograms. Moreover, nomograms also showed higher outcomes of decision curve analysis (DCA) and the area under the curve (AUC) compared to American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage and SEER stage.

Conclusions

This study established nomograms of elderly colorectal cancer patients with distinct clinical values compared to AJCC TNM and SEER stages regarding both OS and CSS.

Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network

Colorectal cancer is one of the commoner digestive tract malignant tumor types, and its incidence and mortality rate are high. Accumulating evidence indicates that long‑chain non‑coding RNAs (lncRNAs) and protein‑coding RNAs interact with each other by competing with the same micro(mi)RNA response element (MREs) and serve an important role in the regulation of gene expression in a variety of tumor types. However, the regulatory mechanism and prognostic role of lncRNA‑mediated competing endogenous (ce)RNA networks in colon cancer have yet to be elucidated. The expression profiles of mRNAs, lncRNAs and miRNAs from 471 colon cancer and 41 paracancerous tissue samples were downloaded from The Cancer Genome Atlas database. A lncRNA‑miRNA‑mRNA ceRNA network in colon cancer was constructed and comprised 17 hub lncRNAs, 87 hub miRNA and 144 hub mRNAs. The topological properties of the network were analyzed, and the random walk algorithm was used to identify the nodes significantly associated with colon cancer. Survival analysis using the UALCAN database indicated that 2/17 lncRNAs identified [metastasis‑associated lung adenocarcinoma transcript (MALAT1) and maternally expressed gene 3 (MEG3)] and 5/144 mRNAs [FES upstream region (FURIN), nuclear factor of activated T‑cells 5 (NFAT5), RNA Binding Motif Protein 12B (RBM12B), Ras related GTP binding A (RRAGA) and WD repeat domain phosphoinositide‑interacting protein 2 (WIPI2)] were significantly associated with the overall survival of patients with colon cancer, and may therefore be used as potential prognostic biomarkers of colon cancer. According to extracted lncRNA‑miRNA‑mRNA interaction pairs, the GSE26334 dataset was used to confirm that the lncRNA MALAT1/miR‑129‑5p/NFAT5 axis may represent a novel regulatory mechanism concerning the progression of colon cancer. The clusterProfiler package was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in colon cancer. Finally, drugs that significantly interact with the core genes identified in colon cancer were predicted using a hypergeometric test. Of these, fostamatinib was identified to be a targeted drug for colon cancer therapy. The present findings provide a novel perspective for improved understanding of the lncRNA‑associated ceRNA network and may facilitate the development of novel targeted therapeutics in colon cancer.

Development and validation of a nomogram to predict the overall survival of patients with neuroblastoma

Neuroblastoma is the most prevalent malignancy in infants characterized by heterogeneous prognosis. It is critical to stratify the risks for patients with neuroblastoma. To stratify the risks for neuroblastoma, clinical characteristics of neuroblastoma patients were retrieved from the Therapeutically Applicable Research to Generate Effective Treatment program. All patients were randomly sampled into the development and validation sets. Cox regression was used to construct a prediction nomogram. The discrimination and calibration capacity of the nomogram was assessed. Prognostic index (PI) was calculated and tested to evaluate the performance of the nomogram. This nomogram demonstrated reasonable discrimination and calibration capacity. The nomogram derived PI exhibited acceptable accuracy in predicting the prognosis for neuroblastoma patients. The overall survival rate was significantly different between the PI discriminated high and low-risk patient subgroups. In conclusion, besides traditional staging systems, some newly defined risk factors could be involved in risk stratification for patients with neuroblastoma. Our nomogram may aid the risk stratification for neuroblastoma patients.

Development of a Novel Six-miRNA-Based Model to Predict Overall Survival Among Colon Adenocarcinoma Patients

Introduction: Colon carcinoma is a common malignant tumor worldwide. Accurately predicting prognosis of colon adenocarcinoma (CA) patients may facilitate clinical individual decision-making. Many studies have reported that microRNAs (miRNAs) were associated with prognosis for patients with colon carcinoma. This study aimed to identify the prognosis-related miRNAs for predicting the overall survival (OS) of CA patients. Methods: Firstly, we analyzed the CA datasets from the Cancer Genome Atlas (TCGA), and looked for the prognosis-related miRNAs. Then, we developed a novel prediction model based on these miRNAs and the clinical characteristics. Time-dependent receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the discrimination and accuracy of the signature and model. Finally, cell function assays and bioinformatics analyses were performed to evaluate the role of these selected miRNAs in modulating biological process in CA. Results: Six prognosis-related miRNAs were included in the miRNA-based signature, and it could effectively distinguish low-risk patients and high-risk patients. Furthermore, we established a prognostic model incorporating the six-miRNA-based signature and clinical characteristics. Areas under curves (AUCs) indicated that the six-miRNA-based model has a better predictive ability than TNM stage (AUC: 0.805 vs. 0.694). The calibration plots suggested close agreement between model predictions and actual observations. GO analysis showed that the target genes of these miRNAs are mainly involved in enrichment in protein binding and regulation of transcript and cytosol. KEGG pathway enrichment analysis indicated that these genes were mainly enriched in PI3K-Akt signaling pathway. Finally, we found that the five miRNAs except miR-152 were upregulated in tumor tissues and CA cells. The functional experiments revealed that miR-1245a, miR-3682, miR-33b, and miR-5683 promoted the migratory abilities and proliferation of CA cell, whereas miR-152 showed opposite effects. However, miR-4444-2 did not influence the migratory ability and proliferation of CA cell. Conclusions: In conclusion, we developed a novel six-miRNA-based model to predict 5-year survival probabilities for CA patients. This model has the potential to facilitate individualized treatment decisions.

Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Background:

Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC).

Methods:

Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis.

Results:

After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction.

Conclusions:

Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.