Mass spectrometric identification of host-specific protein spots from serum protein profiles using MASCOT search engine and the NCBI database.

<p>Mass spectrometric identification of host-specific protein spots from serum protein profiles using MASCOT search engine and the NCBI database.</p

Interaction networks of identified host specific proteins using STRING v9.1.

<p>STRING database is a curated knowledge database of known and predicted protein-protein interactions. Most of the identified host-specific proteins have an established link with each other in the interaction network.</p

The relative expression of host specific proteins among the sera of patients.

<p>Fold change measures the degree of change in the protein of the OSCC patients (n = 25) when compared to normal controls (n<i> = </i>25). This is measured by dividing the average spot intensity in the patients by the average spot intensity in the controls. (up) represents up-regulated expression whilst (down) represents down-regulated expression of protein spot.</p><p>The relative expression of host specific proteins among the sera of patients.</p

Functional annotation analysis of identified host-specific proteins using DAVID v6.7.

+<p>The classification stringency was set to high.</p><p>Functional annotation analysis of identified host-specific proteins using DAVID v6.7.</p

Representative 2-DE serum protein profiles of normal controls and OSCC patients.

<p>Unfractionated serum samples of (a) normal controls and (b) OSCC patients were subjected to 2-DE and silver staining. The labeled spot clusters are proteins which are consistently identified in profiles of normal controls and OSCC patients. α2-HS-glycoprotein (AHS) and α1-antitrysin (AAT) are high abundance proteins that typically appeared in protein profiles.</p

<b>Table 2.</b> The correlation between TGF-β (A) and IL-10 (B) levels with patient clinico-pathological factors.

<p><b>Table 2.</b> The correlation between TGF-β (A) and IL-10 (B) levels with patient clinico-pathological factors.</p

Disease-specific Kaplan-Meier survival estimates based on the levels of CD4⁺CD25^hiCD127^low regulatory T cells.

<p>Disease-specific Kaplan-Meier survival estimates based on the levels of CD4⁺CD25^hiCD127^low regulatory T cells.</p

Co-Expression of TWIST1 and ZEB2 in Oral Squamous Cell Carcinoma Is Associated with Poor Survival

<div><p>Oral squamous cell carcinoma (OSCC) is an aggressive disease accounting for more than 260,000 cancer cases diagnosed and 128,000 deaths worldwide. A large majority of cancer deaths result from cancers that have metastasized beyond the primary tumor. The relationship between genetic changes and clinical outcome can reflect the biological events that promote cancer’s aggressive behavior, and these can serve as molecular markers for improved patient management and survival. To this end, epithelial-mesenchymal transition (EMT) is a major process that promotes tumor invasion and metastasis, making EMT-related proteins attractive diagnostic biomarkers and therapeutic targets. In this study, we used immunohistochemistry to study the expression of a panel of transcription factors (TWIST1, SNAI1/2, ZEB1 and ZEB2) and other genes intimately related to EMT (CDH1 and LAMC2) at the invasive tumor front of OSCC tissues. The association between the expression of these proteins and clinico-pathological parameters were examined with Pearson Chi-square and correlation with survival was analyzed using Kaplan Meier analysis. Our results demonstrate that there was a significant differential expression of CDH1, LAMC2, SNAI1/2 and TWIST1 between OSCC and normal oral mucosa (NOM). Specifically, CDH1 loss was significantly associated with Broder’s grading, while diffused LAMC2 was similarly associated with non-cohesive pattern of invasion. Notably, co-expression of TWIST1 and ZEB2 in OSCC was significantly associated with poorer overall survival, particularly in patients without detectable lymph node metastasis. This study demonstrates that EMT-related proteins are differentially expressed in OSCC and that the co-expression of TWIST1 and ZEB2 could be of clinical value in identifying patients with poor survival for appropriate patient management.</p></div

(A) Cox regression analysis demonstrates that co-expression of TWIST1 and ZEB2 is an independent prognostic factor for poor overall survival.

<p>Abbreviations: CI, confidence intervals</p><p>*<i>p</i> < 0.05.</p><p>(B) Cox regression analysis among patients with negative cervical node demonstrates that co-expression of TWIST1 and ZEB2 is an independent prognostic factor for poor overall survival.</p