Predicting cancer prognosis using functional genomics data sets

MicroRNA-623 inhibits tumor progression and is a predictor of poor prognosis of breast cancer

Dysregulated microRNAs (miRNAs) serve vital roles in the progression and prognosis of breast cancer. miR-623 has been reported to influence the progression of numerous other cancers, such as lung adenocarcinoma and hepatocellular carcinoma, however, its role in breast cancer remains unclear. In the present study, the mRNA expression of miR-623 was studied in 121 pairs of breast cancer and adjacent normal tissues and cultured cell lines by reverse-transcription quantitative PCR. The association between miR-623 expression and clinical characteristics or the overall survival rate of patients was investigated by the χ² test or Cox regression analysis, respectively. The role of miR-623 in cell proliferation, migration and invasion of breast cancer cells was evaluated by cell transfection to regulate miR-623 expression and the CCK8 and Transwell assays, respectively. miR-623 was downregulated in breast cancer tissues and cell lines compared with normal tissues and breast epithelial cell lines. The χ² test demonstrated that the downregulation of miR-623 was associated with the tumor node metastasis (TNM) stage of patients with breast cancer. miR-623 and TNM stage were considered as two independent prognostic factors for breast cancer. Additionally, cell proliferation, migration, and invasion of breast cancer cells were promoted by the downregulation of miR-623, while upregulation of miR-623 led to inhibition of the aforementioned processes. Downregulation of miR-623 in breast cancer is associated with the development of breast cancer and indicates a poor prognosis of patients. The downregulation of miR-623 promotes cell proliferation, migration and invasion of breast cancer. The findings of the present study indicate that miR-623 functions as a prognosis biomarker and a tumor suppressor in breast cancer, which provides a potential therapeutic target for patients with breast cancer.

The clinicopathological significance and prognostic value of EMMPRIN overexpression in cancers: evidence from 39 cohort studies

Extracellular matrix metalloproteinase inducer (EMMPRIN) has been reported to be associated with tumor formation and invasion in many studies. However, the clinicopathological significance and prognosis of EMMPRIN in cancer patients remains inconclusive. Therefore, we conducted a meta-analysis to assess the predictive potential of EMMPRIN in various cancers. By searching Pubmed, Cochrane library database and web of science comprehensively, 39studies with 5739 cases were included in our meta-analysis. The results indicated that EMMPRIN overexpression was significantly associated with poor outcome of cancers (HR=2.46, 95% CI: 2.21-2.75, P<0.0001). In addition, a significant relation was found between EMMPRIN overexpression and clinicopathological features, such as tumor stage (T3+T4/ T1+T2, OR=1.87, 95% CI:1.64-2.12, P<0.0001), tumor differentiation (poor/ well+ moderate, OR=1.09, 95% CI:1.60-2.23, P<0.0001), clinical stage (III+IV /I +II, OR=1.96, 95% CI:1.69-2.27, P<0.0001) and nodal metastasis (positive/negative, OR=2.37, 95% CI:1.93-2.90, P<0.0001). However, the expression of EMMRIN was not significantly associated with tumor stage in cervical cancer (OR=1.35, 95%CI: 0.73-2.48, P=0.33). In conclusion, EMMPRIN overxepression is significantly associated with clinicopathological characteristics and prognosis of cancers. Thus, EMMPRIN may be regarded as a promising bio-marker in predicting the clinical outcome of patients in cancers and could be used as the therapeutic target during clinical practices.

Health Informatics via Machine Learning for the Clinical Management of Patients

OBJECTIVES

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice.

METHODS

We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case.

RESULTS

Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups.

CONCLUSIONS

Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Improving the Prediction of Prostate Cancer Overall Survival by Supplementing Readily Available Clinical Data with Gene Expression Levels of IGFBP3 and F3 in Formalin-Fixed Paraffin Embedded Core Needle Biopsy Material

Background

A previously reported expression signature of three genes (IGFBP3, F3 and VGLL3) was shown to have potential prognostic value in estimating overall and cancer-specific survivals at diagnosis of prostate cancer in a pilot cohort study using freshly frozen Fine Needle Aspiration (FNA) samples.

Methods

We carried out a new cohort study with 241 prostate cancer patients diagnosed from 2004–2007 with a follow-up exceeding 6 years in order to verify the prognostic value of gene expression signature in formalin fixed paraffin embedded (FFPE) prostate core needle biopsy tissue samples. The cohort consisted of four patient groups with different survival times and death causes. A four multiplex one-step RT-qPCR test kit, designed and optimized for measuring the expression signature in FFPE core needle biopsy samples, was used. In archive FFPE biopsy samples the expression differences of two genes (IGFBP3 and F3) were measured. The survival time predictions using the current clinical parameters only, such as age at diagnosis, Gleason score, PSA value and tumor stage, and clinical parameters supplemented with the expression levels of IGFBP3 and F3, were compared.

Results

When combined with currently used clinical parameters, the gene expression levels of IGFBP3 and F3 are improving the prediction of survival time as compared to using clinical parameters alone.

Conclusion

The assessment of IGFBP3 and F3 gene expression levels in FFPE prostate cancer tissue would provide an improved survival prediction for prostate cancer patients at the time of diagnosis.

Prognostic value of melanoma cell adhesion molecule expression in cancers: a meta-analysis

Melanoma cell adhesion molecule (MACM) has been reported in many studies as a novel bio-marker for its prognosis value in cancers. But the prognosis significance of MACM expression in cancer remains inconclusive. Therefore, we conducted a system review and meta-analysis to assess its prognosis value in cancers. A systematic search through Pubmed, EMBASE and Cochran Library database was conducted. Hazard Ratios (HRs) and 95% confidence intervals (CIs) were used to evaluate the prognosis value of MACM expression. Eleven studies with 2657 cases were included after sorting out 462 articles for this meta-analysis. The results of the fixed-model depending on the heterogeneity in studies demonstrated that MACM expression was significantly associated with overall survival (OS) in cancer (HR=2.84, 95% CI: 1.10-7.31, P<0.00001). Furthermore, subgroup analysis indicated that high expressed MACM predicted a poor OS in both Asian (HR=2.52, 95% CI: 1.80-3.52, P<0.00001) and Caucasian (HR=2.40, 95% CI: 2.01-2.88, P<0.00001). In conclusion, high expression of MACM was significantly associated with a poor prognostic outcome in cancer. MACM can be regarded as a novel bio-marker in different types of cancers and can be used to evaluate the prognosis of therapeutic effect during clinical practices.