Dataset Information

Quantitative Proteomic Profiling the Molecular Signatures of Annexin A5 in Lung Squamous Carcinoma Cells.

ABSTRACT: Lung cancer remains the leading cancer killer around the world. It's crucial to identify newer mechanism-based targets to effectively manage lung cancer. Annexin A5 (ANXA5) is a protein kinase C inhibitory protein and calcium dependent phospholipid-binding protein, which may act as an endogenous regulator of various pathophysiological processes. However, its molecular mechanism in lung cancer remains poorly understood. This study was designed to determine the mechanism of ANXA5 in lung cancer with a hope to obtain useful information to provide a new therapeutic target. We used a stable isotope dimethyl labeling based quantitative proteomic method to identify differentially expressed proteins in NSCLC cell lines after ANXA5 transfection. Out of 314 proteins, we identified 26 and 44 proteins that were down- and up-regulated upon ANXA5 modulation, respectively. The IPA analysis revealed that glycolysis and gluconeogenesis were the predominant pathways modulated by ANXA5. Multiple central nodes, namely HSPA5, FN1, PDIA6, ENO1, ALDOA, JUP and KRT6A appeared to occupy regulatory nodes in the protein-protein networks upon ANXA5 modulation. Taken together, ANXA5 appears to have pleotropic effects, as it modulates multiple key signaling pathways, supporting the potential usefulness of ANXA5 as a potential target in lung cancer. This study might provide a new insight into the mechanism of ANXA5 in lung cancer.

SUBMITTER: Sun B

PROVIDER: S-EPMC5042544 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Quantitative Proteomic Profiling the Molecular Signatures of Annexin A5 in Lung Squamous Carcinoma Cells.

Sun Bing B Bai Yuxin Y Zhang Liyuan L Gong Linlin L Qi Xiaoyu X Li Huizhen H Wang Faming F Chi Xinming X Jiang Yulin Y Shao Shujuan S

PloS one 20160929 9

Lung cancer remains the leading cancer killer around the world. It's crucial to identify newer mechanism-based targets to effectively manage lung cancer. Annexin A5 (ANXA5) is a protein kinase C inhibitory protein and calcium dependent phospholipid-binding protein, which may act as an endogenous regulator of various pathophysiological processes. However, its molecular mechanism in lung cancer remains poorly understood. This study was designed to determine the mechanism of ANXA5 in lung cancer wi ...[more]

PMID: 27684953

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Project description:BackgroundTumor mutational burden (TMB) has been identified as one of the predictors for the response to anti-programmed cell death-1 (anti-PD-1) antibody therapy and reported to correlate with smoking history in lung adenocarcinoma. However, in squamous cell carcinoma of the lung, the association between TMB and clinicopathological background factors, such as smoking history, has not been reported, including in our previous study. The mutational signature is a tool to identify the mutagens that are contributing to the mutational spectrum of a tumor by investigating the pattern of DNA changes. Here, we analyzed the mutational signature in lung squamous cell carcinoma to identify mutagens affecting the TMB.MethodsSeven representative mutational signatures including signature 7 (SI7) [ultraviolet (UV)-related], SI4 (smoking), SI6/15 [mismatch repair (MMR)], SI2/13 [apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC)], and SI5 (clock-like) were analyzed in Japanese patients with lung squamous cell carcinoma (n=67) using data generated by next-generation sequencing consisting of a 415-gene panel. The relationships between signatures and clinico-pathological data including TMB and programmed death-ligand 1 (PD-L1) expression were analyzed.ResultsAlthough the reconstructed mutational counts were small with targeted sequencing (median: 30.1, range: 13.3-98.7), the distributions of signatures were comparable among samples, with 56 cases containing more than four signatures. The smoking-related SI4 was found in 45 cases and was significantly related with pack-year index (PYI) (P=0.026). The reconstructed mutation counts were highly correlated with SI4 (r=0.51, P<0.0001), whereas the correlation was weak with SI6/15 (MMR-related) and SI2/13 (APOBEC-related). There was no mutational signature related with PD-L1 expression. Some patients exhibited unique signatures; the patient with the highest mutational counts had a MMR signature, and another patient with a prominent UV signature had occupational exposure to UV, as he was employed as a neon sign engineer.ConclusionsMutational signatures can predict the cause of lung squamous cell carcinoma. Tobacco smoking is the mutagen most related with TMB.

Project description:Annexin A5 (ANXA5), which is known as a protein with anticoagulative function, may play a role in triglyceride biosynthesis. Triglycerides are involved in lipid and energy metabolism, which are important in the elucidation of obesity. To investigate the association between single-nucleotide polymorphisms (SNPs) of ANXA5 and obesity in a Korean population, 372 participants (213 overweight/obese individuals and 159 control subjects) were enrolled from the Kyung Hee University Medical Center and Keimyung University Dongsan Medical Center. The genotypes of five SNPs (rs12510548, rs4240260, rs3756281, rs13136094 and rs6534313) were evaluated in ANXA5 using the multiple logistic regression analysis with the codominant 1, codominant 2, dominant, recessive and log-additive models. The genotype and allele frequencies of the five investigated SNPs exhibited significant differences between the control and the overweight/obese groups: rs12510548 (P=0.004 in the codominant 2 model, P=0.0019 in the recessive model, P=0.027 in the log-additive model and P=0.026 in allele frequencies); rs4240260 (P=0.002 and Fisher's exact P=0.0006 in the codominant 2 model, P=0.0007 and Fisher's exact P=0.0007 in the recessive model, P=0.020 and Fisher's exact P=0.0019 in the log-additive model and P=0.020 in allele frequencies); rs3756281 (P=0.016 in the codominant 2 model and P=0.0094 in the recessive model); rs13136094 (P=0.0030 and Fisher's exact P=0.0011 in the codominant 2 model, P=0.0012 and Fisher's exact P=0.0013 in the recessive model, P=0.034 and Fisher's exact P=0.0035 in the log-additive model and P=0.024 in allele frequencies); and rs6534313 (P=0.0010 and Fisher's exact P=0.0003 in the codominant 2 model, P=0.0003 and Fisher's exact P=0.0003 in the recessive model, P=0.0075 and Fisher's exact P=0.0010 in the log-additive model and P=0.005 in allele frequencies). Two haplotypes were weakly associated with obesity (GGATG, P=0.037 and CAGCC, P=0.020). Results of the present study suggested that ANXA5 may be associated with the development of obesity in a Korean population.

Project description:BackgroundEarly-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.Methods and findingsTumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.ConclusionsOur findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

Dataset Information

Quantitative Proteomic Profiling the Molecular Signatures of Annexin A5 in Lung Squamous Carcinoma Cells.

Publications

Quantitative Proteomic Profiling the Molecular Signatures of Annexin A5 in Lung Squamous Carcinoma Cells.

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