Gene expression profiling of clinical stages II and III breast cancer

TSHZ2 is an EGF-regulated tumor suppressor that binds to the cytokinesis regulator PRC1 and inhibits metastasis

Unlike early transcriptional responses to mitogens, later events are less well-characterized. Here, we identified delayed down-regulated genes (DDGs) in mammary cells after prolonged treatment with epidermal growth factor (EGF). The expression of these DDGs was low in mammary tumors and correlated with prognosis. The proteins encoded by several DDGs directly bind to and inactivate oncoproteins and might therefore act as tumor suppressors. The transcription factor teashirt zinc finger homeobox 2 (TSHZ2) is encoded by a DDG, and we found that overexpression of TSHZ2 inhibited tumor growth and metastasis and accelerated mammary gland development in mice. Although the gene TSHZ2 localizes to a locus (20q13.2) that is frequently amplified in breast cancer, we found that hypermethylation of its promoter correlated with down-regulation of TSHZ2 expression in patients. Yeast two-hybrid screens and protein-fragment complementation assays in mammalian cells indicated that TSHZ2 nucleated a multiprotein complex containing PRC1/Ase1, cyclin B1, and additional proteins that regulate cytokinesis. TSHZ2 increased the inhibitory phosphorylation of PRC1, a key driver of mitosis, mediated by cyclin-dependent kinases. Furthermore, similar to the tumor suppressive transcription factor p53, TSHZ2 inhibited transcription from the PRC1 promoter. By recognizing DDGs as a distinct group in the transcriptional response to EGF, our findings uncover a group of tumor suppressors and reveal a role for TSHZ2 in cell cycle regulation.

Determination of expression profile of p53 gene in different grades of breast cancer tissues by real time PCR

Background

Pakistan has a high incidence of breast cancer in Asia, where annually 16,232 deaths are reported. There are many exogenous and endogenous risk factors that affect the tumor suppressor genes and oncogenes. The p53 gene is a tumor suppressor gene and it has a role to protect the whole genome from external and internal stresses, which causes damages to the genome.

Objective

The aim of the current study was to investigate the p53 gene expression by using the real-time PCR technique in different grades of breast cancer as compared to the normal tissue.

Methods

Fresh Modified Radical Mastectomy (MRM) samples (grade1-grade3) were collected from different hospitals of the Lahore. The project was approved by an ethical review committee of Jinnah Hospital, Lahore. And before sampling an informed consent was obtained from patients and clinicians. RNA from fresh biopsies was extracted by Qiagen extraction kit and cDNA was formed. Real time PCR performed by using SYBR green master mix (ABI) and the data was evaluated by using Livak method. Statistical analysis was done by using Microsoft Excel.

Results

There was an abnormal gene expression of p53 in all grades of the breast tumors. Non-significant (p>0.05) difference of down and up regulation of p53 in different grades of breast tumor was found. However, as a whole up-regulation was more than down-regulation with significant difference (p<0.0011).

Conclusion

The abnormal expression of p53 shows that there are some genetic and epigenetic factors which are the primal cause of an abnormal gene expression. It is recommended that perform next generation sequencing (NGS) of the gene to find out the mutations causing the abnormal behavior of p53 gene.

A novel signature based on microvascular invasion predicts the recurrence of HCC

Background and objectives

In hepatocellular carcinoma (HCC) patients, microvascular invasion (MVI) is associated with worse outcomes regardless of treatment. No single reliable preoperative factor exists to predict MVI. The aim of the work described here was to develop a new MVI− based mRNA biomarker to differentiate between high and low risk patients.

Methods

Using The Cancer Genome Atlas (TCGA) database, we collected data from 315 HCC patients, including mRNA expression and complete clinical data. We generated a seven-mRNA signature to predict patient outcomes. The mRNA signature was validated using the GSE36376 cohort. Finally, we tested the formula in our own 53 HCC patients using qPCR for the seven mRNAs and analyzing the computed tomography (CT) features.

Results

This seven‐mRNA signature significantly correlated with length of recurrence-free survival (RFS) and overall survival (OS) for both the training and validation groups. RFS and OS were briefer in high risk versus low risk patients. A Kaplan–Meier analysis also indicated that survival time was significantly shortened in the high risk group versus the low risk group. Time-dependent receiver operating characteristic analysis demonstrated good predictive performance for the seven-mRNA signature. The mRNA signature also acts as an independent factor according to a Multivariate analysis. Our results are consistent with the seven-mRNA formula risk score.

Conclusion

Our research showed a novel seven-mRNA biomarker based on MVI predicting RFS and OS in HCC patients. This mRNA signature can stratify patients into subgroups based on their risk of recurrence to help guide individualized treatment and precision management in HCC.

KRT 15 as a prognostic biomarker is highly expressed in esophageal carcinoma

Aim: To investigate the expression and prognostic value of KRT 15 in esophageal carcinoma. Materials & methods: The expression levels of KRT 15 were measured in 128 cases of esophageal carcinoma and matched adjacent normal tissues by immunohistochemistry and Western blot assays. Results & conclusion: Western blot analysis shown the expression levels of KRT 15 in esophageal carcinoma were significantly higher compared with those in matched adjacent normal tissues (p < 0.001). immunohistochemistry result shown the high-expression rate of KRT 15 in esophageal carcinoma were 56.3%, which was significantly higher than those in normal tissues (35.9%; p = 0.002). KRT 15 high-expression correlated with T stage, lymph node metastasis, tumor node metastasis stage and prognosis (p < 0.05). These data indicate KRT 15 as a prognostic biomarker is highly expressed in esophageal carcinoma.

Expression of Heterochromatin Protein 1 in the Primary Tumor of Breast Cancer Patients in the Presence or Absence of Occult Metastatic Cells in the Bone Marrow

Gene silencing may occur in breast cancer samples from patients presenting with occult metastatic cells in the bone marrow and one mechanism regulating gene suppression is heterochromatin formation. We have studied whether members of the heterochromatin protein 1 family (HP1Hsα, HP1Hsβ and HP1Hsγ), which take part in chromatin packaging and gene expression regulation, were differentially expressed in tumors from patients with and without occult metastatic cells in their bone marrow. Tumor samples and bone marrow aspirates were obtained from 37 breast cancer patients. Median age was 63 years and 68% of the patients presented with clinical stage I/II disease. Presence of occult metastatic cells in bone marrow was detected through keratin-19 expression by nested RT-PCR in samples from 20 patients (54.1%). The presence of occult metastatic cells in bone marrow was not associated with node involvement, histological grade, estrogen receptor and ERBB2 immunoexpression. Relative gene expression of HP1Hsα, HP1Hsβ and HP1Hsγ was determined by real-time RT-PCR and did not vary according to the presence of occult metastatic cells in bone marrow. In addition, the combined expression of these three transcripts could not be used to classify samples according to the presence of bone marrow micrometastasis. Our work indicates that regulation of heterochromatin formation through HP1 family members may not be the sole mechanism implicated in the metastatic process to the bone marrow.

Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful.

METHODS

The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset.

RESULTS

The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy.

CONCLUSIONS

The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.

Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes

Background

Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait.

Results

Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway.

Conclusions

Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.

Expression profile of the S100 gene family members in oral squamous cell carcinomas

BACKGROUND

Several of the S100 gene members have been reported to be differentially expressed in many human pathological conditions, in particular, the malignancies. Identification and quantification of the differentially expressed S100 gene members in oral squamous cell carcinoma (OSCC) might facilitate their use as potential diagnostic and/or prognostic markers or targets for therapy.

METHODS

we examined the expression profile of 16 members of the S100 gene family at the mRNA level by semiquantitative reverse transcription-polymerase chain reaction (sRT-PCR) in 27 cases of OSCCs/their pair-wised normal controls obtained from Sudanese patients, and confirmed the sRT-PCR results by performing quantitative real time-polymerase chain reaction (qRT-PCR) for 6 of the 16 genes examined.

RESULTS

With sRT-PCR, 4 (25%; S100A4, S100A6, S100A8, S100A14) out of the 16 S100 gene members examined were found to be significantly down-regulated (P < 0.05) in the tumors compared to the normal controls. None of the S100 gene members examined were found to be significantly up-regulated in the tumors. qRT-PCR results confirmed the significant down-regulation of the S100A4, S100A6, and S100A14 genes in the tumors examined.

CONCLUSION

S100 gene family members might play an important role in the pathogenesis of the OSCCs examined. Findings of the present work warrant in-depth studies of the S100 gene family members, in particular, the S100A4, S100A6, S100A8, and S100A14 to further understand their possible role(s) in OSCC tumorigenesis.