Dataset Information

Autophagy-related Proteins as a Prognostic Factor of Patients With Colorectal Cancer.

ABSTRACT:

Objectives

Autophagy plays a dual role in tumorigenesis. In the initial stages, it promotes cell survival and suppresses carcinogenesis, whereas in cancer development, it induces cancer cell survival. In this study, we investigate the role of autophagy as a protective or tumor suppressor mechanism in colorectal cancer (CRC) cell lines and evaluate its role as a potential biomarker in human tumor samples.

Materials and methods

The data of 68 patients with CRC treated at our Department from January 1 to December 31, 2016 were analyzed. Immunohistochemistry evaluation of p62, LC3B, Beclin-1, and Rab-7 in formalin-fixed paraffin-embedded tissue samples was performed and their expression was correlated with clinicopathologic characteristics, mutation status, and therapeutic approach. The ? was used to test an association among categorical variables. Survival curves were estimated using the Kaplan-Meier method and differences were assessed using the log-rank test. Colo-205, HT29, SW-480, and Caco-2 cell lines were also used so as to test the autophagy markers with oxaliplatin, irinotecan, hydroxychloroquine, and 3-methyladenine.

Results

Overexpression of Beclin-1 is associated with poor survival (P=0.001) in patients with CRC treated with chemotherapy, irrespective of the stage and mutational status. Rab-7 is also correlated with progression-free survival (PFS) (P=0.088). Oxaliplatin (10 and 20???) and irinotecan (10 and 20???) inhibit autophagy in microsatellite stable (MSS) CRC cell lines. The inhibition of autophagy in MSS CRC cell lines after treatment with oxaliplatin and irinotecan is further identified through monodancylcadaverine staining. Moreover, inhibition of autophagy with molecules such as hydroxychloroquine (20???) and 3-methyladenine (5?mM) was identified by the accumulation of p62 and LC3B.

Conclusions

Beclin-1 is an independent prognostic factor of overall survival and PFS. Also, Rab-7 is identified as an independent prognostic factor of PFS. Besides, several chemotherapeutic drugs such as oxaliplatin and irinotecan inhibit autophagy in MSS CRC cell lines in a similar way like hydroxychloroquine and 3-methyladenine. Thus, in MSS patients who develop chemoresistance, a combination of other therapies that include an autophagy inhibitor could be more beneficial. Further clinical trials are needed to investigate these therapeutic strategies.

SUBMITTER: Koustas E

PROVIDER: S-EPMC6766360 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

Autophagy-related Proteins as a Prognostic Factor of Patients With Colorectal Cancer.

Koustas Evangelos E Sarantis Panagiotis P Theoharis Stamatios S Saetta Angelica A AA Chatziandreou Ilenia I Kyriakopoulou Georgia G Giannopoulou Ioanna I Michelli Maria M Schizas Dimitrios D Papavassiliou Athanasios G AG Karamouzis Michalis V MV

American journal of clinical oncology 20191001 10

<h4>Objectives</h4>Autophagy plays a dual role in tumorigenesis. In the initial stages, it promotes cell survival and suppresses carcinogenesis, whereas in cancer development, it induces cancer cell survival. In this study, we investigate the role of autophagy as a protective or tumor suppressor mechanism in colorectal cancer (CRC) cell lines and evaluate its role as a potential biomarker in human tumor samples.<h4>Materials and methods</h4>The data of 68 patients with CRC treated at our Departm ...[more]

PMID: 31517637

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Project description:PurposeAutophagy-related proteins (ATG) play a crucial role in autophagy. Recently, the functions of autophagy in cancer have been gathering attention. However, the prognostic value of ATGs in gastric cancer (GC) has not been explored.MethodsThe Kaplan-Meier plotter (KM plotter) online database was used to examine the value of ATGs gene expression levels in overall survival (OS) prediction in GC patients with different clinical stage, differentiation, gender, HER2 status, and therapeutic strategy. In vitro experiments applied VE-822, an effective GC treatment, to assess cell migration and proliferation in gastric mucosa epithelial cells, and real-time PCR was used to measure alterations of autophagy-related gene expression.ResultsHigh ATG3, ATG4C, ATG5, and ATG10 mRNA levels were associated with good OS, while increased ATG4B, ATG7, ATG12, ATG16L1, and TECPR1 mRNA levels related to unfavorable OS in patients with GC. ATG12 overexpression had different effects on OS due to high levels of heterogeneity. High ATG12 expression was correlated with good OS in female patients with GC and with bad OS for male patients. Additionally, the increased ATG12 expression was more likely to get a satisfactory OS in patients who underwent surgery alone but was associated with poor OS for patients treated with 5-FU adjuvant. In addition, elevated TECPR1 expression was related to favorable OS for patients with poorly differentiated type, while for patients with moderate differentiation, it was relevant to poor OS. The in vitro experiments showed that berzosertib could significantly inhibit the migration and proliferation of human gastric mucosa epithelial cells, and further real-time PCR assessment of ATG expressions partially coincided with the bioinformation analysis above.ConclusionThese results indicate that individual ATGs have unique prognostic significance interpreted using Kaplan-Meier plotter analysis and in vitro experiments, and this may help guide clinical therapeutic strategy and promote OS by individualizing therapy for GC patients.

Project description:Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA). Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed. Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs. Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.

Project description:IntroductionColorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor-node-metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment.MethodsCRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot.ResultsA prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased.ConclusionThe new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.

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Project description:BackgroundAutophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.MethodsFirst, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.ResultsA total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.ConclusionsBased on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

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Dataset Information

Autophagy-related Proteins as a Prognostic Factor of Patients With Colorectal Cancer.

Objectives

Materials and methods

Results

Conclusions

Publications

Autophagy-related Proteins as a Prognostic Factor of Patients With Colorectal Cancer.

Similar Datasets

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