Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently overexpressed and LEP,
Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, “multiplexed post-experiment monoisotopic mass refinement” (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/ MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.
Bioactive peptides are present in most soy products and eggs and have essential protective functions. Infection is a core feature of innate immunity that affects blood pressure and the glucose level, and ageing can be delayed by killing senescent cells. Food also encrypts bioactive peptides and protein sequences produced through proteolysis or food processing. Unique food protein fragments can improve human health and avoid metabolic diseases, inflammation, hypertension, obesity, and diabetes mellitus. This review focuses on drug targets and fundamental mechanisms of bioactive peptides on metabolic syndromes, namely obesity and type 2 diabetes, to provide new ideas and knowledge on the ability of bioactive peptide to control metabolic syndromes.
Smoking is the leading cause of lung cancer development and several genes have been identified as potential biomarker for lungs
cancer. Contributing to the present scientific knowledge of biomarkers for lung cancer two different data sets, i.e. GDS3257 and
GDS3054 were downloaded from NCBI׳s GEO database and normalized by RMA and GRMA packages (Bioconductor).
Diffrentially expressed genes were extracted by using and were R (3.1.2); DAVID online tool was used for gene annotation and
GENE MANIA tool was used for construction of gene regulatory network. Nine smoking independent gene were found whereas
average expressions of those genes were almost similar in both the datasets. Five genes among them were found to be associated
with cancer subtypes. Thirty smoking specific genes were identified; among those genes eight were associated with cancer sub
types. GPR110, IL1RN and HSP90AA1 were found directly associated with lung cancer. SEMA6A differentially expresses in only
non-smoking lung cancer samples. FLG is differentially expressed smoking specific gene and is related to onset of various cancer
subtypes. Functional annotation and network analysis revealed that FLG participates in various epidermal tissue developmental
processes and is co-expressed with other genes. Lung tissues are epidermal tissues and thus it suggests that alteration in FLG may
cause lung cancer. We conclude that smoking alters expression of several genes and associated biological pathways during
development of lung cancers.
Interactions between pairs of membrane-bound receptors can enhance tumour development with implications for targeted therapies for cancer. Here we demonstrate clear heterotypic interaction between CD74 and CD44, which might act in synergy and hence contribute to breast cancer progression. CD74, a type II transmembrane glycoprotein, is a chaperone for MHC class II biosynthesis and a receptor for the MIF. CD44 is the receptor for hyaluronic acid and is a Type I transmembrane protein. Interactions between CD74, MIF and the intra-cytoplasmic domain of CD44 result in activation of ERK1/2 pathway, leading to increased cell proliferation and decreased apoptosis. The level of CD44 in the breast tumor cell lines CAMA-1, MDA-MB-231, MDA-MB-435 and the immortalized normal luminal cell line 226LDM was higher than that of CD74. It was also observed that CD74 and CD44 exhibit significant variation in expression levels across the cells. CD74 and CD44 were observed to accumulate in cytoplasmic compartments, suggesting they associate with each other to facilitate tumour growth and metastasis. Use of a novel and validated colocalisation and image processing approach, coupled with co-immunoprecipitation, confirmed that CD74 and CD44 physically interact, suggesting a possible role in breast tumour growth. This is the first time that CD74 and CD44 colocalization has been quantified in breast cancer cells using a non-invasive and validated bioimaging procedure. Measuring the co-expression levels of CD74 and CD44 could potentially be used as a ‘biomarker signature’ to monitor different stages of breast cancer.
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