Stem cells are defined as cells able to both extensively self-renew and differentiate into progenitors. Research data show that more resistant stem cells than common cancer cells exist in cancer patients.To identify unrecognized differences between cancer stem cells and cancer cells might be able to develope effective classification,diagnose and treat ment for cancer.

Stem Cells,Cancer and Cancer Stem Cells

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Autophagy is a mechanism by which cellular material is delivered to lysosomes for degradation, leading to the basal turnover of cell components and providing energy and macromolecular precursors. Autophagy has opposing, context-dependent roles in cancer, and interventions to both stimulate and inhibit autophagy have been proposed as cancer therapies. This has led to the therapeutic targeting of autophagy in cancer to be sometimes viewed as controversial. In this Review, we suggest a way forwards for the effective targeting of autophagy by understanding the context-dependent roles of autophagy and by capitalizing on modern approaches to clinical trial design.

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Targeting autophagy in cancer

Recently, more than 1000 large intergenic noncoding RNAs (lincRNAs) have been reported. These RNAs are evolutionarily conserved in mammalian genomes and thus presumably function in diverse biological processes. Here, we report the identification of lincRNAs that are regulated by p53. One of these lincRNAs (lincRNA-p21) serves as a repressor in p53-dependent transcriptional responses. Inhibition of lincRNA-p21 affects the expression of hundreds of gene targets enriched for genes normally repressed by p53. The observed transcriptional repression by lincRNA-p21 is mediated through the physical association with hnRNP-K. This interaction is required for proper genomic localization of hnRNP-K at repressed genes and regulation of p53 mediates apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulate their localization to sets of previously active genes.

A Large Intergenic Noncoding RNA Induced by p53 Mediates Global Gene Repression in the p53 Response

http://europepmc.org/articles/PMC4504744?pdf=render

An overview of microRNAs

Pelvic lymph node metastases are regarded as the most important risk factor and a predictor of poor prognosis for patients with cervical cancer. Exploration of metastasis-related molecules is helpful toward improving the prognosis in cervical cancer. To identify the role of miR-375 in metastasis and progression of cervical cancer, we examined the expression of miR-375 in 170 cervical cancer tissues and 68 normal cervical tissues, using stem-loop quantitative PCR, and found that the expression of miR-375 in cervical cancer tissues was significantly decreased by 4.45-fold, compared with 68 normal tissues. A significant correlation existed between miR-375 expression and clinicopathologic parameters, including lymph node metastasis of cervical cancer. Overexpressed miR-375 suppressed cell proliferation, blocked G1-to-S cell-cycle transition, and inhibited cell migration and invasion in human cervical SiHa and CaSki cells. SP1, a potential target gene of miR-375, was inversely correlated with miR-375 expression in cervical cancer tissues. Moreover, SP1 was negatively regulated by miR-375, and knockdown of SP1 by siRNA inhibited cell malignant behaviors. Thus, our findings suggest that down-regulated miR-375 promotes cell malignant behaviors via the target gene SP1 and may consequently contribute to the progression of cervical cancer.

miR-375 Is Down-Regulated in Squamous Cervical Cancer and Inhibits Cell Migration and Invasion via Targeting Transcription Factor SP1

miRNAs have the potential to act on diverse downstream genes, and miRNA signatures of HPV-infected tissues may provide insight into HPV-related carcinogenesis. We set out to profile miRNA expression in HPV-infected samples and relate this to histological and grade-specific alterations in the spectrum of cervical carcinogenesis in vivo. A total of 31 miRNAs showed significant and continuous expression along with the progression from normal cervical tissue to cancer, and six of them were validated in 133 samples. By bioinformatics analyses, we established a putative HPV-associated miRNA-mRNA regulatory network, showing that miR-29 is the most highly enriched. We also found that YY1 and CDK6 were both positively correlated with E6/E7 RNA expression and targeted by tumour-suppressive miR-29. Evidence of miR-29 involvement in HPV infection was further verified in patient samples and by various experimental approaches. Taken together, our results suggest that HPVs have oncogenic properties at least in part by reshaping the milieu of cellular miRNAs. miR-29 restrains cell cycle progression and induces apoptosis via YY1 and CDK6 promoting malignant transformation induced by HPV, although the abnormality of miR-29 in HPV-infected cells might be regulated in an indirect way.

Progressive miRNA expression profiles in cervical carcinogenesis and identification of HPV-related target genes for miR-29†

Paclitaxel is recommended as a first-line chemotherapeutic agent against ovarian cancer, but drug resistance becomes a major limitation of its success clinically. The key molecule or mechanism associated with paclitaxel resistance in ovarian cancer still remains unclear. Here, we showed that TXNDC17 screened from 356 differentially expressed proteins by LC-MS/MS label-free quantitative proteomics was more highly expressed in paclitaxel-resistant ovarian cancer cells and tissues, and the high expression of TXNDC17 was associated with poorer prognostic factors and exhibited shortened survival in 157 ovarian cancer patients. Moreover, paclitaxel exposure induced upregulation of TXNDC17 and BECN1 expression, increase of autophagosome formation, and autophagic flux that conferred cytoprotection for ovarian cancer cells from paclitaxel. TXNDC17 inhibition by siRNA or enforced overexpression by a pcDNA3.1(+)-TXNDC17 plasmid correspondingly decreased or increased the autophagy response and paclitaxel resistance. Additionally, the downregulation of BECN1 by siRNA attenuated the activation of autophagy and cytoprotection from paclitaxel induced by TXNDC17 overexpression in ovarian cancer cells. Thus, our findings suggest that TXNDC17, through participation of BECN1, induces autophagy and consequently results in paclitaxel resistance in ovarian cancer. TXNDC17 may be a potential predictor or target in ovarian cancer therapeutics.

https://www.tandfonline.com/doi/pdf/10.1080/15548627.2014.998931

TXNDC17 promotes paclitaxel resistance via inducing autophagy in ovarian cancer.

VEGF, VEGFRs expressions and activated STATs in ovarian epithelial carcinoma.

Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. Methods The data were collected to establish three deep-learning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. Results The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of high-grade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. Implications The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.

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Weiguo Lu

Papers

miR-375 Is Down-Regulated in Squamous Cervical Cancer and Inhibits Cell Migration and Invasion via Targeting Transcription Factor SP1

Progressive miRNA expression profiles in cervical carcinogenesis and identification of HPV-related target genes for miR-29†

TXNDC17 promotes paclitaxel resistance via inducing autophagy in ovarian cancer.

VEGF, VEGFRs expressions and activated STATs in ovarian epithelial carcinoma.

The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images