Development of diagnostic model of lung cancer based on multiple tumor markers and data mining

Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study

Aims

Medication‐taking behaviours of breast cancer survivors undergoing adjuvant hormone therapy have received considerable attention. This study aimed to determine factors affecting medication‐taking behaviours in people with breast cancer using data mining.

Design

A longitudinal observational retrospective cohort study with a hospital‐based survey.

Methods

A total of 385 subjects were surveyed, analysing existing data from January 2010 to December 2017 in Taiwan. Three data mining approaches—multiple logistic regression, decision tree and artificial neural network—were used to build the prediction models and rank the importance of influencing factors. Accuracy, specificity and sensitivity were used as assessment indicators for the prediction models.

Results

Multiple logistic regression was the most effective approach, achieving an accuracy of 96.37%, specificity of 96.75% and sensitivity of 96.12%. The duration of adjuvant hormone therapy discontinuation, duration of adjuvant hormone therapy use and age at diagnosis by data mining were the three most critical factors influencing the medication‐taking behaviours of people with breast cancer.

Comparative analysis of three data mining techniques in diagnosis of lung cancer

There is a lot of abnormal information in the development of lung cancer, and how to extract useful knowledge is urgent from massive information. Data mining technology has become a popular tool for medical classification and prediction. However, each technology has its advantage and disadvantage, and several data mining methods will be applied to conduct the in-depth analysis step by step. And the prediction results of different models are compared. A total of 180 lung cancer patients and 243 lung benign individuals were collected from the First Affiliated Hospital of Zhengzhou University from October 2014 to March 2016, and the prediction models based on epidemiological data, clinical features and tumor markers were developed by artificial neural network (ANN), decision tree C5.0 and support vector machine (SVM). The results showed that there were significant differences between the lung cancer group and the lung benign group in terms of seven tumor markers and 10 epidemiological and clinical indicators. The accuracy rates of ANN, C5.0 and SVM were 76.47, 89.92 and 85.71%, respectively. The results of receiver operating characteristic curve (ROC) curve revealed the area under the ROC curve (AUC) of ANN was 0.811 (0.770-0.847), the AUC of C5.0 was 0.897 (0.864-0.924) and the AUC of SVM was 0.878 (0.843-0.908). It was shown that the decision tree C5.0 model has the least error rate and highest accuracy, and it could be used to diagnose lung cancer.

RASSF1A: A promising target for the diagnosis and treatment of cancer

The Ras association domain family 1 isoform A (RASSF1A), a tumor suppressor, regulates several tumor-related signaling pathways and interferes with diverse cellular processes. RASSF1A is frequently demonstrated to be inactivated by hypermethylation in numerous types of solid cancers. It is also associated with lymph node metastasis, vascular invasion, and chemo-resistance. Therefore, reactivation of RASSF1A may be a viable strategy to block tumor progress and reverse drug resistance. In this review, we have summarized the clinical value of RASSF1A for screening, staging, and therapeutic management of human malignancies. We also highlighted the potential mechanism of RASSF1A in chemo-resistance, which may help identify novel drugs in the future.

Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established based on plasma miRNAs biomarkers, clinical symptoms and epidemiology material.

Methods: The expressions of plasma miRNA were examined with SYBR Green-based quantitative real-time PCR.

Results: We identified that the expressions of 10 plasma miRNAs (miR-21, miR-20a, miR-210, miR-145, miR-126, miR-223, miR-197, miR-30a, miR-30d, miR-25), smoking status, fever, cough, chest pain or tightness, bloody phlegm, haemoptysis, were significantly different between lung cancer and control groups (P<0.05). The accuracies of the combined SVM, miRNAs SVM, symptom SVM, combined Fisher, miRNAs Fisher and symptom Fisher were 96.34%, 80.49%, 84.15%, 84.15%, 75.61%, and 80.49%, respectively; AUC of these six model were 0.976, 0.841, 0.838, 0.865, 0.750, and 0.801, respectively. The accuracy and AUC of combined SVM were higher than the other 5 models (P<0.05).

Conclusions: Our findings indicate that SVM model based on plasma miRNAs biomarkers may serve as a novel, accurate, noninvasive method for auxiliary diagnosis of lung cancer.

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MS-based urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profiling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n = 98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high significance in BC relapse prognosis [HR = 5.76, p-value = 0.0001, c-index = 0.64]. Urinary peptide profiles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC.