Pretreatment serum albumin-to-alkaline phosphatase ratio is an independent prognosticator of survival in patients with metastatic gastric cancer.

BACKGROUND: Previous studies have suggested that a low albumin-to-alkaline phosphatase ratio (AAPR) is associated with a lower survival rate in patients with various malignancies. However, the relationship between pretreatment AAPR and the prognosis of patients with gastric cancer (GC) remains unclear. AIM: To investigate the prognostic value of AAPR in distant metastatic GC. METHODS: A total of 191 patients with distant metastatic cancer from a single institute were enrolled in this study. Pretreatment clinical data, including serum albumin and alkaline phosphatase levels, were collected. A chi-square test or Fisher's exact test was applied to evaluate the correlations between AAPR and various clinical parameters in GC patients. The Kaplan-Meier method and Cox proportional hazards regression model were used to evaluate the prognostic efficacy of AAPR in metastatic GC patients. A two-sided P value lower than 0.05 was considered statistically significant. RESULTS: A receiver operating characteristic curve indicated that 0.48 was the optimal threshold value for AAPR. AAPR ≤ 0.48 was significantly associated with bone (P < 0.05) and liver metastasis (P < 0.05). Patients with high levels of AAPR had better survival in terms of overall survival (OS) and progression-free survival (PFS), regardless of the presence of liver/bone metastasis. Pretreatment AAPR was found to be a favorable predictor of OS and PFS based on a multivariate cox regression model. AAPR-M system, constructed based on AAPR and number of metastatic sites, showed superior predictive ability relative to the number of metastatic sites for predicting survival. CONCLUSION: Pretreatment AAPR may serve as an independent prognostic factor for predicting PFS and OS in patients with metastatic GC. Furthermore, AAPR may assist clinicians with individualizing treatment.

Impacts of the charged residues mutation S48E/N62H on the thermostability and unfolding behavior of cold shock protein: insights from molecular dynamics simulation with Go model.

The cold shock protein from the hyperthermophile Thermotoga maritima (Tm-Csp) exhibits significantly higher thermostability than its homologue from the thermophile Bacillus caldolyticus (Bc-Csp). Experimental studies have shown that the electrostatic interactions unique to Tm-Csp are responsible for improving its thermostability. In the present work, the favorable charged residues in Tm-Csp were grafted into Bc-Csp by a double point mutation of S48E/N62H, and the impacts of the mutation on the thermostability and unfolding/folding behavior of Bc-Csp were then investigated by using a modified Go model, in which the electrostatic interactions between charged residues were considered in the model. Our simulation results show that this Tm-Csp-like charged residue mutation can effectively improve the thermostability of Bc-Csp without changing its two-state folding mechanism. Besides that, we also studied the unfolding kinetics and unfolding/folding pathway of the wild-type Bc-Csp and its mutant. It is found that this charged residue mutation obviously enhanced the stability of the C-terminal region of Bc-Csp, which decreases the unfolding rate and changes the unfolding/folding pathway of the protein. Our studies indicate that the thermostability, unfolding kinetics and unfolding/folding pathway of Bc-Csp can be artificially changed by introducing Tm-Csp-like favorable electrostatic interactions into Bc-Csp.

Analysis of conformational motions and related key residue interactions responsible for a specific function of proteins with elastic network model.

Protein collective motions play a critical role in many biochemical processes. How to predict the functional motions and the related key residue interactions in proteins is important for our understanding in the mechanism of the biochemical processes. Normal mode analysis (NMA) of the elastic network model (ENM) is one of the effective approaches to investigate the structure-encoded motions in proteins. However, the motion modes revealed by the conventional NMA approach do not necessarily correspond to a specific function of protein. In the present work, a new analysis method was proposed to identify the motion modes responsible for a specific function of proteins and then predict the key residue interactions involved in the functional motions by using a perturbation approach. In our method, an internal coordinate that accounts for the specific function was introduced, and the Cartesian coordinate space was transformed into the internal/Cartesian space by using linear approximation, where the introduced internal coordinate serves as one of the axes of the coordinate space. NMA of ENM in this internal/Cartesian space was performed and the function-relevant motion modes were identified according to their contributions to the specific function of proteins. Then the key residue interactions important for the functional motions of the protein were predicted as the interactions whose perturbation largely influences the fluctuation along the internal coordinate. Using our proposed methods, the maltose transporter (MalFGK2) from E. Coli was studied. The functional motions and the key residue interactions that are related to the channel-gating function of this protein were successfully identified.

The Intrinsic Dynamics and Unfolding Process of an Antibody Fab Fragment Revealed by Elastic Network Model.

Antibodies have been increasingly used as pharmaceuticals in clinical treatment. Thermal stability and unfolding process are important properties that must be considered in antibody design. In this paper, the structure-encoded dynamical properties and the unfolding process of the Fab fragment of the phosphocholine-binding antibody McPC603 are investigated by use of the normal mode analysis of Gaussian network model (GNM). Firstly, the temperature factors for the residues of the protein were calculated with GNM and then compared with the experimental measurements. A good result was obtained, which provides the validity for the use of GNM to study the dynamical properties of the protein. Then, with this approach, the mean-square fluctuation (MSF) of the residues, as well as the MSF in the internal distance (MSFID) between all pairwise residues, was calculated to investigate the mobility and flexibility of the protein, respectively. It is found that the mobility and flexibility of the constant regions are higher than those of the variable regions, and the six complementarity-determining regions (CDRs) in the variable regions also exhibit relative large mobility and flexibility. The large amplitude motions of the CDRs are considered to be associated with the immune function of the antibody. In addition, the unfolding process of the protein was simulated by iterative use of the GNM. In our method, only the topology of protein native structure is taken into account, and the protein unfolding process is simulated through breaking the native contacts one by one according to the MSFID values between the residues. It is found that the flexible regions tend to unfold earlier. The sequence of the unfolding events obtained by our method is consistent with the hydrogen-deuterium exchange experimental results. Our studies imply that the unfolding behavior of the Fab fragment of antibody McPc603 is largely determined by the intrinsic dynamics of the protein.

Mass-tag technology responding to intracellular signals as a novel assay system for the diagnosis of tumor.

A novel mass spectrometry-based assay system for determining protein kinase activity employing mass-tagged substrate peptide probes was used for the diagnosis of tumors. Two peptide probes (H-type and D-type) were synthesized containing the same substrate peptide sequence for protein kinase C (PKC). The molecular weights of the two probes differ because of the incorporation of deuterium into the acetyl groups of the D-type probe. The lysates of the normal and tumor tissue were prepared and reacted with the H- and D-type peptide probes, respectively. The PKC activities of the normal and tumor tissues can be compared simply and directly by calculating the phosphorylated ratio to each peptide probe, obtained from the peak intensity of the mass spectrum after mixing of the two reaction solutions. The phosphorylation ratio for the reaction of the H-type peptide probe with the tumor tissue lysate (B16 melanoma) was more than three times higher than that of the D type peptide probe with the normal skin tissue lysate. These results show that the novel assay system for detecting protein kinase activity using mass-tag technology can be a simple and useful means to profile protein kinase activity for cell or tissue lysate samples, and can be applied to the diagnosis of tumors.