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PubMed | Aerospace Center Hospital of Beijing University Beijing 100049, Shanghai JiaoTong University, Ut Md Anderson Cancer Center Houston, General Hospital of Jinan Military Region Jinan 250031 and 2 more.
Type: Journal Article | Journal: International journal of clinical and experimental medicine | Year: 2015

Osteopontin (OPN) is involved in promotion of cancer cells by regulating various facets of tumor progression such as cell proliferation, angiogenesis and metastasis. To understand the role of OPN in laryngeal squamous cell carcinoma (LSCC), we thus explored the biological function of OPN in LSCC after silencing OPN expression by RNA interference (RNAi).The OPN expression in tumor tissues of LSCC was determined immunohistochemically in both LSCC and adjacent normal tissues. Lentivirus vector with RNAi small hairpin gene sequence of OPN (named LV-shOPN) was transfected into Hep-2 cells and transplanted into BALB/c-nu mice. After siRNA transfection, the viability of Hep-2 cells was examined by MTS, OPN expression was detected by Western blotting, and tumor angiogenesis was assessed by microvessel densities (MVD).The difference of positive rate of OPN in 72 cases LSCC (54 cases, 75.0%) and adjacent normal tissues (15 cases, 20.8%) was statistically significant (P<0.001) and the OPN expression was also significantly correlated with tumor stage, grade and the presence of lymph node. Hep-2 cells infected with LV-shOPN significantly decreased OPN expression, in comparison to cells with LV-shNon transfection (as the control) (P<0.05). The constructed LV-shOPN effectively inhibited the viability of Hep-2 cell and growth of xenograft tumors in nude mice (all P<0.050). The expression of OPN and MVD was significantly decreased in xenograft tumors (all P<0.05).RNAi silencing of OPN expression can significantly inhibit tumor growth and angiogenesis of Hep-2 cells, and OPN may be considered as one of gene targeting therapy for LSCC.


PubMed | Hebei General Hospital Shijiazhuang, General Hospital of Jinan Military Command Jinan 250031, Shanghai JiaoTong University and Ut Md Anderson Cancer Center Houston
Type: Journal Article | Journal: International journal of clinical and experimental medicine | Year: 2015

Osteopontin (OPN) is overexpressed in many human tumors and involved in promotion of cancer cells by regulating various facets of tumor progression such as cell proliferation, invasion and metastasis. To understand roles of OPN in tumor progression of laryngeal squamous cell carcinoma (LSCC) or develop molecular marker for prognosis and treatment of LSCC, we thus explore biological function of OPN and correlation with p53 in LSCC.The expression of OPN and p53 in tumor tissues of LSCC was determined immunohistochemically in both LSCC and adjacent normal tissues. Lentivirus vector with RNAi small hairpin gene sequence of OPN (named LV-shOPN) was transfected into Hep-2 cells. OPN expression was detected by Western blotting assay and the viability and invasive ability of Hep-2 cells were examined by MTS and transwell assay.We found that OPN and p53 protein expressions were significantly higher in LSCC tumor tissues than adjacent normal tissues (76.2% vs. 23.8% for OPN and 63.8% vs. 15.2% for p53, all P < 0.001). OPN expression was also significantly correlated with p53 expression, tumor stage, grade and the presence of lymph node. The constructed LV-shOPN effectively inhibited the OPN expression, viability and invasive ability of Hep-2 cells (all P < 0.050).Taken together, OPN is overexpressed in LSCC. OPN expression is correlated with p53 expression, tumor progression and lymph node metastasis. Additionally, RNAi silencing of OPN expression can significantly inhibit tumor viability and invasion ability of Hep-2 cells. Thus, OPN may be considered as a marker and potential gene targeting therapy in LSCC.


Lee J.,Ut Md Anderson Cancer Center Houston | Ji Y.,Ut Md Anderson Cancer Center Houston | Liang S.,Ut Md Anderson Cancer Center Houston | Cai G.,Ut Md Anderson Cancer Center Houston | Muller P.,University of Texas at Austin
Cancer Informatics | Year: 2011

Motivation: RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements. Results: We present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study. © the author(s), publisher and licensee Libertas Academica Ltd.


Leskov I.,Massachusetts Institute of Technology | Pallasch C.P.,Massachusetts Institute of Technology | Pallasch C.P.,University of Cologne | Drake A.,Massachusetts Institute of Technology | And 10 more authors.
Oncogene | Year: 2013

Although numerous mouse models of B-cell malignancy have been developed via the enforced expression of defined oncogenic lesions, the feasibility of generating lineage-defined human B-cell malignancies using mice reconstituted with modified human hematopoietic stem cells (HSCs) remains unclear. In fact, whether human cells can be transformed as readily as murine cells by simple oncogene combinations is a subject of considerable debate. Here, we describe the development of humanized mouse model of MYC/BCL2-driven 'double-hit' lymphoma. By engrafting human HSCs transduced with the oncogene combination into immunodeficient mice, we generate a fatal B malignancy with complete penetrance. This humanized-MYC/BCL2-model (hMB) accurately recapitulates the histopathological and clinical aspects of steroid-, chemotherapy- and rituximab-resistant human 'double-hit' lymphomas that involve the MYC and BCL2 loci. Notably, this model can serve as a platform for the evaluation of antibody-based therapeutics. As a proof of principle, we used this model to show that the anti-CD52 antibody alemtuzumab effectively eliminates lymphoma cells from the spleen, liver and peripheral blood, but not from the brain. The hMB humanized mouse model underscores the synergy of MYC and BCL2 in 'double-hit' lymphomas in human patients. Additionally, our findings highlight the utility of humanized mouse models in interrogating therapeutic approaches, particularly human-specific monoclonal antibodies. © 2013 Macmillan Publishers Limited All rights reserved.


PubMed | Ut Md Anderson Cancer Center Houston
Type: | Journal: Cancer informatics | Year: 2011

RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements.We present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study.A public domain R package is available from http://odin.mdacc.tmc.edu/~ylji/BMDE/.

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