Systematic profiling of alternative splicing events and splicing factors in left- and right-sided colon cancer

Huang, Xiaoliang; Liu, Jing; Mo, Xiaokui; H, Liu; Chen, Wei; Huang, Ling; Chen, J; Tian, Chao; Meng, Yongsheng; Wu, Guojun; Xie, Wen; Fj, Peter; Z, Liu; Tang, Weizhong

doi:10.18632/aging.102319

Cited by 23 publications

(25 citation statements)

References 55 publications

(61 reference statements)

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“…The OS of patients with stage II-III CRC is affected by many factors besides tumor stage, such as tumor location, histological type, age, sex, microsatellite status, and RAS/RAF mutation, and no single factor can accurately predict survival in CRC. 8,9 In addition, sophisticated and expensive laboratory techniques have limits to their application. Nomograms have been widely used in clinical oncology as reliable tools for estimating numerical probabilities for individual patients by incorporating and illustrating important prognostic factors.…”

Section: Introductionmentioning

confidence: 99%

“…The prognosis of patients with CRC can vary greatly, even within the same TNM stage. The OS of patients with stage II‐III CRC is affected by many factors besides tumor stage, such as tumor location, histological type, age, sex, microsatellite status, and RAS/RAF mutation, and no single factor can accurately predict survival in CRC . In addition, sophisticated and expensive laboratory techniques have limits to their application.…”

Section: Introductionmentioning

confidence: 99%

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Nomogram for predicting overall survival in stage II‐III colorectal cancer

et al. 2020

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Purpose The overall survival (OS) of patients diagnosed with stage II‐III colorectal cancer (CRC) can vary greatly, even between patients with the same tumor stage. We aimed to design a nomogram to predict OS in resected, stage II‐III CRC and stratify patients with CRC into different risk groups. Patients and Methods Based on data from 873 patients with CRC, we used univariate Cox regression analysis to select the significant prognostic features, which were subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. Cross‐validation was used to confirm suitable tuning parameters (λ) for LASSO logistic regression. Then, the nomogram was used to estimate 3‐ and 5‐year OS based on the multivariable Cox regression model. The survival curves of the two groups were produced using the Kaplan‐Meier method. Risk group stratification was performed to assess the predictive capacity of the nomogram. Results Preoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy were identified as independent prognostic factors by LASSO regression and integrated for the construction of the nomogram. The nomogram provided good discrimination, with C‐indices of 0.67 and 0.69 for the training and validation sets, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 3‐ and 5‐year OS. Moreover, a significant difference in OS was shown between patients stratified into different risk groups (P < .001). Conclusion We constructed and validated an original predictive nomogram for OS in patients with CRC after surgery, facilitating physicians to appraise the individual survival of postoperative patients accurately and identify high‐risk patients who need more aggressive treatment and follow‐up strategies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nomogram for predicting overall survival in stage II‐III colorectal cancer

et al. 2020

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“…AS, as a post-transcriptional regulatory process that modifies more than 90% of human genes, plays a significant role in enhancing the diversity of transcription and protein (9,12,13). In recent years, the dysregulation of AS has been found to be associated with the occurrence and development of a variety of cancers including NSCLC (26)(27)(28)(29)(30)(31)(32). For instance, Li et al profiled the genome-wide AS events of NSCLC from TCGA and constructed prognostic predictors for LUAD, LUSC and merged NSCLC patients.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, cancer-specific AS events have been identified by comparing cancer tissues with normal controls in several studies. And associations between AS signatures and overall survival (OS) time of patients have been systematically evaluated in a variety of cancers, such as colorectal cancer, ovarian cancer, breast cancer, kidney renal clear cell carcinoma, esophageal carcinoma as well as NSCLC (26)(27)(28)(29)(30)(31)(32). However, although systematic analysis and prognostic prediction models of AS events for NSCLC have been conducted in some studies, no comprehensive study specifically for LUSC has been performed.…”

Section: Introductionmentioning

confidence: 99%

Identification of Survival-Associated Alternative Splicing Signatures in Lung Squamous Cell Carcinoma

Liu

Jia

et al. 2020

Front. Oncol.

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Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients. Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape. Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events. Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.

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“…Flodrops et al [ 31 ] found that TIMP1 intron 3 retention could affect the progression of colon cancer. Huang et al [ 32 ] constructed an AS signature based on differentially expressed AS events between left- and right-sided colon cancer. In the present study, we detected eleven AS events for the construction of the model.…”

Section: Discussionmentioning

confidence: 99%

Identification of an alternative splicing signature as an independent factor in colon cancer

2020

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Background Colon cancer is a common malignant tumor with a poor prognosis. Abnormal alternative splicing (AS) events played a part in the occurrence and metastasis of the tumor. We aimed to develop a survival-associated AS signature in colon cancer. Methods The Percent Spliced In values of AS events were available in The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox analysis was carried out to detect the prognosis-related AS events. We created a predictive model on account of the survival-associated AS events, which was further validated with a training-testing group design. Kaplan-Meier analysis was applied to assess patient survival. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of this model. Meanwhile, the clinical relevance of the signature and its regulatory relationship with splicing factors (SFs) were also evaluated. Results In total, 2132 survival-related AS events were identified from colon cancer samples. We developed an eleven-AS signature, in which the 5-year AUC value was 0.911. Meanwhile, the AUC values at five years were 0.782 and 0.855 in the testing and entire cohort, respectively. Multivariate Cox regression displayed that the T category and the risk score of the signature were independent risk factors of colon cancer survival. Also, we constructed an SFs-AS network based on 11 SFs and 48 AS events. Conclusions We identified an eleven-AS signature of colon cancer. This signature could be treated as an independent prognostic factor.

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Systematic profiling of alternative splicing events and splicing factors in left- and right-sided colon cancer

Cited by 23 publications

References 55 publications

Nomogram for predicting overall survival in stage II‐III colorectal cancer

Nomogram for predicting overall survival in stage II‐III colorectal cancer

Identification of Survival-Associated Alternative Splicing Signatures in Lung Squamous Cell Carcinoma

Identification of an alternative splicing signature as an independent factor in colon cancer

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