Author Correction: Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods.
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ABSTRACT:
SUBMITTER: Chen Q
PROVIDER: S-EPMC10959997 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Author Correction: Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods.
Chen Qingquan Q Hu Yiming Y Lin Wen W Huang Zhimin Z Li Jiaxin J Lu Haibin H Dai Rongrong R You Liuxia L
Scientific reports 20240322 1
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Project description:Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan-Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan-Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC.
| S-EPMC10912082 | biostudies-literature
Project description: Not available
| S-EPMC8012648 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. The preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modelling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to the generation of research hypotheses, predictions of targets for diagnosis and treatment, and relationships between data variables.
| S-EPMC11828881 | biostudies-literature
Project description:One of the most severe and devastating cancer is pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC) is one of the major pancreatic exocrine cancer with a poor prognosis and growing prevalence. It is the most deadly disease, with an overall five-year survival rate of 6% to 10%. According to various reports, it has been demonstrated that pancreatic cancer stem cells (PCSCs) are the main factor responsible for the tumor development, proliferation, resistance to anti-cancer drugs, and recurrence of tumors after surgery. PCSCs have encouraged new therapeutic methods to be explored that can specifically target cancer cells. Furthermore, stem cells, especially mesenchymal stem cells (MSCs), are known as influential anti-cancer agents as they function through anti-inflammatory, paracrine, cytokines, and chemokine's action. The properties of MSCs, such as migration to the site of infection and host immune cell activation by its secretome, seem to control the microenvironment of the pancreatic tumor. MSCs secretome exhibits similar therapeutic advantages as a conventional cell-based therapy. Moreover, the potential for drug delivery could be enhanced by engineered MSCs to increase drug bioactivity and absorption at the tumor site. In this review, we have discussed available therapeutic strategies, treatment hurdles, and the role of different factors such as PCSCs, cysteine, GPCR, PKM2, signaling pathways, immunotherapy, and NK-based therapy in pancreatic cancer.
| S-EPMC7916947 | biostudies-literature
Project description:A single tumor marker has a low diagnostic value in pancreatic cancer. Combinations of multiple biomarkers and unique analysis algorithms can be applied to overcome these limitations. This study sought to develop diagnostic algorithms using multiple biomarker panels and to validate their performance in the diagnosis of pancreatic ductal adenocarcinoma (PDAC). We used blood samples from 180 PDAC patients and 573 healthy controls. Candidate markers consisted of 11 markers that are commonly expressed in various cancers and which have previously demonstrated increased expression in pancreatic cancer. Samples were divided into training and validation sets. Five linear or non-linear classification methods were used to determine the optimal model. Differences were identified in 10 out of the 11 markers tested. We identified 2047 combinations, all of which were applied to 5 separate algorithms. The new biomarker combination consisted of 6 markers (ApoA1, CA125, CA19-9, CEA, ApoA2, and TTR). The area under the curve, specificity, and sensitivity were 0.992, 95%, and 96%, respectively, in the training set. Meanwhile, the measures were 0.993, 96%, and 93% in the validation set. This study demonstrated the utility of multiple biomarker combinations in the early detection of PDAC. A diagnostic panel of 6 biomarkers was developed and validated. These algorithms will assist in the early diagnosis of PDAC.
| S-EPMC7352313 | biostudies-literature
Project description:BACKGROUND We aimed to identify pivotal genes and pathways involved in pancreatic ductal adenocarcinoma (PDAC), and explore possible molecular markers for the early diagnosis of the disease. MATERIAL AND METHODS The array data of GSE74629, including 34 PDAC samples and 16 healthy samples, was downloaded from GEO (Gene Expression Omnibus) database. Then, the DEGs (differentially expressed genes) in PDAC samples were compared with healthy samples using limma (linear models for microarray). Gene functional interaction networks were analyzed with Cytoscape and ReactomeFIViz. PPI networks were constructed with Cytoscape software. In addition, PPI (protein-protein interaction) network clustering modules were analyzed with ClusterONE, and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses for modules were performed. RESULTS A total of 630 upregulated and 1,002 downregulated DEGs were identified in PDAC samples compared with healthy samples. Some ribosomal protein genes with higher average correlation in module 0 were enriched in the ribosome pathway. NUP107 (nucleoporin 107 kDa) and NUP160 (nucleoporin 160 kDa) were enriched in module 3. HNRNPU (heterogeneous nuclear ribonucleoprotein U) with higher average correlation in module 8 was enriched in the spliceosome pathway. The ribosome pathway and the spliceosome pathway were significantly enriched in cluster 1 and cluster 2, respectively. CONCLUSIONS Ribosomal protein genes Nup170, Nup160, and HNRNPU, and the ribosome pathway as well as the spliceosome pathway may play important roles in PDAC progression. In addition, ribosomal protein genes Nup170, Nup160, and HNRNPU may be used as possible molecular markers for the early diagnosis of the disease.
| S-EPMC5928849 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based system modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables.
2024-12-18 | PXD057798 | Pride
Project description:Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based system modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables.
2024-12-18 | PXD057795 | Pride
Project description:Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based system modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables.
2024-12-18 | PXD057888 | Pride
Project description:Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based system modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here may contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables. bridging the gap between preclinical research and clinical trials, thus enhancing the possibilities for discovering early diagnostic biomarkers and effective therapeutic targets.
2024-12-18 | PXD057928 | Pride