SPC25 Functions as a Prognostic-Related Biomarker, and Its High Expression Correlates with Tumor Immune Infiltration and UCEC Progression.

BACKGROUND: Most tumor tissues expressed spindle pole body component 25 (SPC25), one of the four subunits of the NDC80 complex, at greater levels compared to surrounding normal tissues. According to earlier researches, this subunit strongly encouraged tumor cell proliferation and tumor growth, which resulted in worse prognoses in patients with hepatocellular, breast, lung, and prostate cancer. Precisely because SPC25's role in uterine corpus endometrial carcinoma (UCEC) is understudied, we chose to concentrate on UCEC for gaining a more scientific and thorough understanding of SPC25. METHODS: Along with examining SPC25's differential expression, prognostic significance, and biological function in UCEC, our research sought to clarify the underlying mechanism by which SPC25 influences the course of UCEC and patient prognosis from the viewpoints of methylation and immune infiltration. RESULTS: We observed differential expression of SPC25 gene in different clinicopathological features of UCEC and identified SPC25 as a hazard factor for poorer overall survival (OS), disease-specific survival (DSS), and progress free interval (PFI) in UCEC, particularly in its multiple clinical subtypes. In addition, we also discovered that SPC25 and its co-expressed genes mostly engaged in biological processes and signal transduction routes linked to cell cycle and cell division in UCEC. After investigating SPC25's methylation status, we discovered that patients with UCEC had elevated SPC25 expression and a poor prognosis due to hypomethylation of CpG sites in the SPC25 gene sequence. Finally, we investigated SPC25's potential role in immunotherapy and discovered that SPC25 might alter the major immune cell infiltration levels in the tumor microenvironment (TME) by regulating the expression of immunoregulatory molecules and chemokines, which would be beneficial for SPC25 to control the progression of UCEC. CONCLUSIONS: In conclusion, SPC25 was a useful predictive biomarker as well as a possible therapeutic target for UCEC.

Optimal HP configurations of proteins by combining local search with elastic net algorithm.

The prediction of protein conformation from its amino-acid sequence is one of the most prominent problems in computational biology. But it is NP-hard. Here, we focus on an abstraction widely studied of this problem, the two-dimensional hydrophobic-polar protein folding problem (2D HP PFP). Mathematical optimal model of free energy of protein is established. Native conformations are often sought using stochastic sampling methods, but which are slow. The elastic net (EN) algorithm is one of fast deterministic methods as travelling salesman problem (TSP) strategies. However, it cannot be applied directly to protein folding problem, because of fundamental differences in the two types of problems. In this paper, how the 2D HP protein folding problem can be framed in terms of TSP is shown. Combination of the modified elastic net algorithm and novel local search method is adopted to solve this problem. To our knowledge, this is the first application of EN algorithm to 2D HP model. The results indicate that our approach can find more optimal conformations and is simple to implement, computationally efficient and fast.

The simulation of the three-dimensional lattice hydrophobic-polar protein folding.

One of the most prominent problems in computational biology is to predict the natural conformation of a protein from its amino acid sequence. This paper focuses on the three-dimensional hydrophobic-polar (HP) lattice model of this problem. The modified elastic net (EN) algorithm is applied to solve this nonlinear programming hard problem. The lattice partition strategy and two local search methods (LS(1) and LS(2)) are proposed to improve the performance of the modified EN algorithm. The computation and analysis of 12 HP standard benchmark instances are also involved in this paper. The results indicate that the hybrid of modified EN algorithm, lattice partition strategy, and local search methods has a greater tendency to form a globular state than genetic algorithm does. The results of noncompact model are more natural in comparison with that of compact model.

Exploration of two-dimensional hydrophobic-polar lattice model by combining local search with elastic net algorithm.

Determining the functional conformation of a protein from its amino acid sequence remains a central problem in computational biology. In this paper, we establish the mathematical optimal model of protein folding problem (PFP) on two-dimensional space based on the minimal energy principle. A novel hybrid of elastic net algorithm and local search method (ENLS) is applied successfully to simulations of protein folding on two-dimensional hydrophobic-polar (HP) lattice model. Eight HP benchmark instances with up to 64 amino acids are tested to verify the effectiveness of proposed approach and model. In several cases, the ENLS method finds new lower energy states. The numerical results show that it is drastically superior to other methods in finding the ground state of a protein.