Risk and survival of chronic myeloid leukemia after breast cancer: A population-based study

Cancer Prevention for Survivors: Incidence of Second Primary Cancers and Sex Differences-A Population-Based Study from an Italian Cancer Registry

BACKGROUND

The number of cancer survivors continues to increase, thanks to advances in cancer diagnosis and treatment. Unfortunately, the incidence of a second primary cancer (SPC) is also increasing, but limited studies reporting incidence data are available regarding multiple cancers. This study presents our observations on multiple primary malignant cancers, the associations between sites, and the inherent sex differences.

PATIENTS AND METHODS

We report the data, disaggregated by sex, concerning the SPCs that were recorded in the "Registro Tumori Integrato" (RTI) a population-based cancer registry in Sicily, Italy, as observed in the period from 2003 to 2017, in a total population of approximately 2,300,000. SPCs were divided into synchronous and metachronous cancers. The International Classification of Diseases for Oncology, third edition (ICD-O-3), was used for topographical and morphological classifications. Multiple primary cancers with multi-organ primitiveness were selected from the database of the RTI by extracting patients with more than one diagnosis. SPCs had different histology or morphology from the particular cancer that was considered to be the index cancer case. Multicenter or multifocal cancers, or metastases, were excluded. The percentages of cancer by sex and topography, the average age of incidence, and a breakdown by age were computed.

RESULTS

Differences were observed between sexes in terms of incidence and site for SPCs. The most frequent SPC was skin cancer (20% of the SPCs observed). The associations among sites of multiple cancers are reported.

CONCLUSION

There are many gaps in our knowledge of sex differences in cancer. The study of multiple primary cancers could bring more likely opportunities for evaluation of the cancer burden and trends that can be used to identify new research areas by population health programs, as well as for clinical researchers.

Transcriptomics Analysis of the Tumor-Inhibitory Pathways of 6-Thioguanine in MCF-7 Cells via Silencing DNMT1 Activity

Background

6-thioguanine (6-TG), as a conventional “ancient” drug for the treatment of acute leukemia, has been proved to have extensive anti-tumor roles. This study was created to investigate the hidden function of 6-TG on the MCF-7 breast cancer cell line (ER+, PR+) and its mechanisms.

Methods

MCF-7 cells were treated with 6-TG, and the IC50 value was measured by a cell counting kit-8 assay. Differentially expressed genes (DEGs) were confirmed by RNA-seq analysis. Apoptosis and cell cycle consequences were determined by flow cytometry and Western blot analyses.

Results

The results showed that colony formation decreased markedly and the percentage of cell apoptosis increased after 6-TG treatment. DNMT1 mRNA and protein expression decreased, and FAS expression increased. Moreover, 6-TG also induced MCF-7 cells to undergo G2/M phase cell cycle arrest and upregulated CDKN1A (p21).

Conclusion

Overall, our results suggest that 6-TG may induce FAS-mediated exogenous apoptosis and p21-dependent G2/M arrest by inhibiting the activity of DNMT1 in MCF-7 breast cancer cells.

Polypharmacy in Type 2 Diabetes Mellitus: Insights from an Internal Medicine Department

BACKGROUND AND OBJECTIVES

Polypharmacy heavily impacts the quality of life of patients worldwide. It is a necessary evil in many disorders, and especially in type 2 diabetes mellitus, as patients require treatment both for this condition and its related or unrelated comorbidities. Thus, we aimed to evaluate the use of polypharmacy in type 2 diabetes mellitus vs. non-diabetes patients.

MATERIALS AND METHODS

A cross-sectional retrospective observational study was conducted. We collected the medical records of patients hospitalized in the Internal Medicine Clinic of the Clinical Emergency Hospital of Bucharest, Romania, for a period of two months (01/01/2018-28/02/2018). Patients diagnosed with type 2 diabetes mellitus were included in the study group, whereas patients who were not diabetic were used as controls.

RESULTS

The study group consisted of 63 patients with type 2 diabetes mellitus (mean age 69.19 ± 9.67 years, range 46-89 years; 52.38% males). The control group included 63 non-diabetes patients (mean age 67.05 ± 14.40 years, range 42-93 years, 39.68% males). Diabetic patients had more comorbidities (10.35 ± 3.09 vs. 7.48 ± 3.59, p = 0.0001) and received more drugs (7.81 ± 2.23 vs. 5.33 ± 2.63, p = 0.0001) vs. non-diabetic counterparts. The mean number of drug-drug and food-drug interactions was higher in type 2 diabetes mellitus patients vs. controls: 8.86 ± 5.76 vs. 4.98 ± 5.04, p = 0.0003 (minor: 1.22 ± 1.42 vs. 1.27 ± 1.89; moderate: 7.08 ± 4.08 vs. 3.54 ± 3.77; major: 0.56 ± 0.74 vs. 0.37 ± 0.77) and 2.63 ± 1.08 vs. 2.19 ± 1.42 (p = 0.0457), respectively.

CONCLUSIONS

Polypharmacy should be an area of serious concern also in type 2 diabetes mellitus, especially in the elderly. In our study, type 2 diabetes mellitus patients received more drugs than their non-diabetes counterparts and were exposed to more drug-drug and food-drug interactions.

Ensemble Feature Learning to Identify Risk Factors for Predicting Secondary Cancer

Background: In recent years, the development and diagnosis of secondary cancer have become the primary concern of cancer survivors. A number of studies have been developing strategies to extract knowledge from the clinical data, aiming to identify important risk factors that can be used to prevent the recurrence of diseases. However, these studies do not focus on secondary cancer. Secondary cancer is lack of the strategies for clinical treatment as well as risk factor identification to prevent the occurrence. Methods: We propose an effective ensemble feature learning method to identify the risk factors for predicting secondary cancer by considering class imbalance and patient heterogeneity. We first divide the patients into some heterogeneous groups based on spectral clustering. In each group, we apply the oversampling method to balance the number of samples in each class and use them as training data for ensemble feature learning. The purpose of ensemble feature learning is to identify the risk factors and construct a diagnosis model for each group. The importance of risk factors is measured based on the properties of patients in each group separately. We predict secondary cancer by assigning the patient to a corresponding group and based on the diagnosis model in this corresponding group. Results: Analysis of the results shows that the decision tree obtains the best results for predicting secondary cancer in the three classifiers. The best results of the decision tree are 0.72 in terms of AUC when dividing the patients into 15 groups, 0.38 in terms of F₁ score when dividing the patients into 20 groups. In terms of AUC, decision tree achieves 67.4% improvement compared to using all 20 predictor variables and 28.6% improvement compared to no group division. In terms of F₁ score, decision tree achieves 216.7% improvement compared to using all 20 predictor variables and 80.9% improvement compared to no group division. Different groups provide different ranking results for the predictor variables. Conclusion: The accuracies of predicting secondary cancer using k-nearest neighbor, decision tree, support vector machine indeed increased after using the selected important risk factors as predictors. Group division on patients to predict secondary cancer on the separated models can further improve the prediction accuracies. The information discovered in the experiments can provide important references to the personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with secondary cancer in all phases of the recurrent trajectory.