An analysis of the effect of agriculture subsidies on technical efficiency: Evidence from rapeseed production in China.

This study was conducted to examine the effects of agricultural subsidies on the technical efficiency of agricultural production technology and on factor input. It utilized a random frontier production function, instrumental variable method, and threshold regression model. The data used for this analysis consisted of 609 field yield measurements from the National Rapeseed Industry Technology System in 2020. The findings indicate that agricultural subsidies have a substantial impacts and it increases the technical efficiency of production process. Specifically, these subsidies encourage the use of land resources while inhibiting the use of chemical fertilizers. However, this does not have a significant effect on the utilization of labor and capital resources. Furthermore, the impact of agricultural subsidies on production technology efficiency varies depending on the scale of the farming operation. The subsidies significantly enhance the production technology efficiency of farmers with a business scale of less than 0.67 ha, but do not significantly improve the production technology efficiency of farmers with a business scale exceeding 0.67 ha. To optimize the effectiveness of agricultural subsidy policy, three methods and recommendations are proposed: increasing the overall amount of subsidies, expanding and diversifying the types of subsidies, and refining the process of disbursing subsidies.

The power and the promise of CAR-mediated cell immunotherapy for clinical application in pancreatic cancer.

BACKGROUND: Pancreatic cancer, referred to as the "monarch of malignancies," is a neoplastic growth mostly arising from the epithelial cells of the pancreatic duct and acinar cells. This particular neoplasm has a highly unfavorable prognosis due to its marked malignancy, inconspicuous initial manifestation, challenging early detection, rapid advancement, and limited survival duration. Cellular immunotherapy is the ex vivo culture and expansion of immune effector cells, granting them the capacity to selectively target malignant cells using specialized techniques. Subsequently, these modified cells are reintroduced into the patient's organism with the purpose of eradicating tumor cells and providing therapeutic intervention for cancer. PRESENT SITUATION: Presently, the primary cellular therapeutic modalities employed in the treatment of pancreatic cancer encompass CAR T-cell therapy, TCR T-cell therapy, NK-cell therapy, and CAR NK-cell therapy. AIM OF REVIEW: This review provides a concise overview of the mechanisms and primary targets associated with various cell therapies. Additionally, we will explore the prospective outlook of cell therapy in the context of treating pancreatic cancer.

KLK10/LIPH/PARD6B/SLC52A3 are promising molecular biomarkers for the prognosis of pancreatic cancer through a ceRNA network.

Pancreatic adenocarcinoma (PAAD) remains challenging to diagnose and treat clinically due to its difficult early diagnosis, low surgical resection rate, and high risk of postoperative recurrence and metastasis. SMAD4 is a classical mutated gene in pancreatic cancer and is lost in up to 60%-90 % of PAAD patients, and its mutation often predicts a poor prognosis and treatment resistance. In this study, based on the expression profile data in The Cancer Genome Atlas database, we identified a ceRNA network composed of 2 lncRNAs, 1 miRNA, and 4 mRNAs through differential expression analysis and survival prognosis analysis. Among them, high expression of KLK10/LIPH/PARD6B/SLC52A3 influenced the prognosis and overall survival of PAAD patients. We confirmed the high expression of these target genes in pancreatic tissue of pancreatic-specific SMAD4-deficient mice. In addition, immune infiltration analysis showed that the high expression of these target genes affects the tumor immune environment and contributes to the progression of PAAD. Abnormal overexpression of these target genes may be caused by hypermethylation. In conclusion, we found that KLK10/LIPH/PARD6B/SLC52A3 is a potential prognostic marker for PAAD based on a competing endogenous RNA-mediated mechanism and revealed the potential pathogenic mechanism by which deficient expression of SMAD4 promotes pancreatic cancer progression, which provides a new pathway and theoretical basis for targeted therapy or improved prognosis of pancreatic cancer. These data will help reveal potential therapeutic targets for pancreatic cancer and improve the prognosis of pancreatic cancer patients.

Effects of substrate improvement on winter nitrogen removal in riparian reed (Phragmites australis) wetlands: rhizospheric crosstalk between plants and microbes.

With continued anthropogenic inputs of nitrogen (N) into the environment, non-point source N pollutants produced in winter cannot be ignored. As the water-soil interface zones, riparian wetlands play important roles in intercepting and buffering N pollutants. However, winter has the antagonistic effect on the N removal. Substrate improvement has been suggested as a strategy to optimize wetland performance and there remain many uncertainties about the inner mechanism. This study explores the effects of substrate improvement on N removal in winter and rhizospheric crosstalk between reed (Phragmites australis) and microbes in subtropical riparian reed wetlands. The rates of wetland N removal in winter, root metabolite profiles, and rhizosphere soil microbial community compositions were determined following the addition of different substrates (gravel, gravel + biochar, ceramsite + biochar, and modified ceramsite + biochar) to natural riparian soil. The results showed that the addition of different substrates to initial soil enhanced N removal from the microcosms in winter. Gravel addition increased NH4+-N removal by 8.3% (P < 0.05). Gravel + biochar addition increased both TN and NH4+-N removals by 8.9% (P < 0.05). The root metabolite characteristics and microbial community compositions showed some variations under different substrate additions compared to the initial soil. The three treatments involving biochar addition decreased lipid metabolites and enhanced the contents and variety of carbon sources in rhizosphere soil, while modified ceramsite + biochar addition treatment had a greater impact on the microbial community structure. There was evidence for a complex crosstalk between plants and microbes in the rhizosphere, and some rhizosphere metabolites were seen to be significantly correlated with the bacterial composition of the rhizospheric microbial community. These results highlighted the importance of rhizospheric crosstalk in regulating winter N removal in riparian reed wetland, provided a scientific reference for the protection and restoration of riparian reed areas and the prevention and control of non-point source pollution.

Galectin-1-mediated high NCAPG expression correlates with poor prognosis in gastric cancer.

Galectin-1 (Gal1) and non-SMC condensin I complex, subunit G (NCAPG) are associated with metastasis in several malignant tumors. However, their precise roles in gastric cancer (GC) remain uncertain. This study explored the clinical significance and relationship of Gal1 and NCAPG in GC. Gal1 and NCAPG expressions were significantly up-regulated in GC compared to adjacent non-cancerous tissues by immunohistochemistry (IHC) and Western blotting. Besides, methods including stable transfection, quantitative real-time reverse transcription PCR, Western blotting, Matrigel invasion and wound-healing assays in vitro, were also conducted. IHC scores for Gal1 and NCAPG had a positive correlation in GC tissues. High Gal1 or NCAPG expression significantly correlated with poor prognosis in GC, and Gal1 combined with NCAPG had a synergetic effect on the prediction of GC prognosis. Gal1 overexpression in vitro enhanced NCAPG expression, cell migration, and invasion in SGC-7901 and HGC-27 cells. Simultaneous Gal1 overexpression and NCAPG knockdown in GC cells partly rescued the migrative and invasive abilities. Thus, Gal1 promoted GC invasion through increased NCAPG expression. The present study demonstrated the prognostic significance of the combination of Gal1 and NCAPG in GC for the first time.

A demonstration based on multi-omics transcriptome sequencing data revealed disulfidptosis heterogeneity within the tumor microenvironment of esophageal squamous cell carcinoma.

BACKGROUND: It is of great concern to identify prognostic signatures for the prediction and prediction of esophageal squamous cell carcinoma (ESCC), which is the lethal pathological type of malignancy. METHOD: Bulk RNA sequencing and scRNA-seq data were retrieved from GSE53624, GSE53622, and GSE188900. Disulfidptosis-related differentially expressed genes (DEGs) were identified between disulfidptosis-high score and disulfidptosis-low score groups. Functional annotation of DEGs were analyzed by Gene Ontology (GO). Consistent clustering and co-expression modules were analyzed, and then constructed a risk score model via multivariate Cox regression analysis. Immune infiltration and immunotherapy response analyses were conducted based on risk score. qRT-PCR, colony formation assay, and flow cytometry analysis were conducted in KYSE-150 and TE-1 cell lines. RESULTS: Seven genes (CD96, CXCL13, IL2RG, LY96, TPK1, ACAP1, and SOX17) were selected as marker genes. CD96 and SOX17 are independent prognostic signatures for ESCC patients, with a significant correlation with infiltrated immune cells. ESCC patients had worse response to nivolumab in the high-risk group. Through cellular experiments, we found that CD96 expression was associated with apoptosis and cell cycle ESCC cells. CONCLUSION: In a word, the risk score based on disulfidptosis is associated with prognosis and the immune microenvironment, which may direct immunotherapy of ESCC. The key gene of risk score, namely CD96, plays a role in proliferation and apoptosis in ESCC. We offer an insight into the exploration of the genomic etiology of ESCC for its clinical management.

Galectin-1 promotes gastric cancer peritoneal metastasis through peritoneal fibrosis.

BACKGROUND: Peritoneal metastasis is one of the main causes of death in patients with gastric cancer (GC). Galectin-1 regulates various undesirable biological behaviors in GC and may be key in GC peritoneal metastasis. METHODS: In this study, we elucidated the regulatory role of galectin-1 in GC cell peritoneal metastasis. GC and peritoneal tissues underwent hematoxylin-eosin (HE), immunohistochemical (IHC), and Masson trichrome staining to analyze the difference in galectin-1 expression and peritoneal collagen deposition in different GC clinical stages. The regulatory role of galectin-1 in GC cell adhesion to mesenchymal cells and in collagen expression was determined using HMrSV5 human peritoneal mesothelial cells (HPMCs). Collagen and corresponding mRNA expression were detected with western blotting and reverse transcription PCR, respectively. The promoting effect of galectin-1 on GC peritoneal metastasis was verified in vivo. Collagen deposition and collagen I, collagen III, and fibronectin 1 (FN1) expression in the peritoneum of the animal models were detected by Masson trichrome and IHC staining. RESULTS: Galectin-1 and collagen deposition in the peritoneal tissues was correlated with GC clinical staging and were positively correlated. Galectin-1 enhanced the ability of GC cells to adhere to the HMrSV5 cells by promoting collagen I, collagen III, and FN1 expression. The in vivo experiments confirmed that galectin-1 promoted GC peritoneal metastasis by promoting peritoneal collagen deposition. CONCLUSION: Galectin-1-induced peritoneal fibrosis may create a favorable environment for GC cell peritoneal metastasis.

Thermally Laminated Lighting Textile for Wearable Displays with High Durability.

Textile-based light-emitting devices are attracting more and more attention because of their potential applications in smart clothing, human-computer interfaces, safety warnings, entertainment fashion, etc. However, simple and efficient manufacturing of luminescent devices on fabrics even clothing with excellent stretchability and washability remains challenging. Here, a solvent-free thermal lamination process combined with laser engraving has been proposed to fabricate electroluminescent (EL) devices on textiles. All the preprepared components, such as the bottom electrode, the EL layer, and the top transparent electrode, were thermally laminated on the surface of textiles employing thermoplastic polyurethane (TPU) as the binding matrix. The stretchability, luminance, and interface adhesion of the EL devices were systematically studied, showing excellent mechanical durability at high temperature, in humid environments, withstanding repeated machine washing, and resistant to various forms of physical damage. As a demonstration of potential application, textile-based EL devices were fabricated, which could display colored and pixelated patterns as well as dynamic images. The thermal lamination technology developed in this work can potentially enable people to DIY (do it yourself) fabricate light-emitting devices on clothing using daily tools, which could facilitate the widespread use of textile-based wearable displays.

ALDH1: A potential therapeutic target for cancer stem cells in solid tumors.

Solid tumors can be divided into benign solid tumors and solid malignant tumors in the academic community, among which malignant solid tumors are called cancers. Cancer is the second leading cause of death in the world, and the global incidence of cancer is increasing yearly New cancer patients in China are always the first. After the concept of stem cells was introduced in the tumor community, the CSC markers represented by ALDH1 have been widely studied due to their strong CSC cell characteristics and potential to be the driving force of tumor metastasis. In the research results in the past five years, it has been found that ALDH1 is highly expressed in various solid cancers such as breast cancer, lung cancer, colorectal cancer, liver cancer, gastric cancer, cervical cancer, esophageal cancer, ovarian cancer, head,and neck cancer. ALDH1 can activate and transform various pathways (such as the USP28/MYC signaling pathway, ALDH1A1/HIF-1α/VEGF axis, wnt/ß-catenin signaling pathway), as well as change the intracellular pH value to promote formation and maintenance, resulting in drug resistance in tumors. By targeting and inhibiting ALDH1 in tumor stem cells, it can enhance the sensitivity of drugs and inhibit the proliferation, differentiation, and metastasis of solid tumor stem cells to some extent. This review discusses the relationship and pathway of ALDH1 with various solid tumors. It proposes that ALDH1 may serve as a diagnosis and therapeutic target for CSC, providing new insights and new strategies for reliable tumor treatment.

A case of giant nasal septal angiomyolipoma.

Angiomyolipoma is an extremely rare, benign mesenchymal tumor of the nasal cavity, primarily common in the kidney and secondarily common in the liver. According to the author's knowledge, no cases of angiomyolipoma of the nasal septum have been identified to date. We report a case of a patient with a giant angiomyolipoma at the posterior end of the nasal septum who recovered after surgery without any complication.

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares.

BACKGROUND: A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. METHODS: In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. RESULTS: In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. CONCLUSIONS: Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures.

The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of - 7.0 kcal/mol and - 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.

Assessing wetland nitrogen removal and reed (Phragmites australis) nutrient responses for the selection of optimal harvest time.

Wetlands play an important role in reducing the impact of nitrogen pollution on natural aquatic environments. However, during the plant wilting period (winter) there will inevitably be a reduction in nitrogen removal from wetlands. Understanding optimum harvest time will allow the use of management practices to balance the trade-off between nitrogen removal and the sustainability of wetlands. In this study, we investigated wetland nitrogen removal and reed (Phragmites australis) nutrient responses for two years [first year: influent total nitrogen (TN) 17.6-34.7 mg L-1; second year: influent TN 3.2-10.0 mg L-1] to identify the optimal harvest time: before wilting, mid-wilting, or late wilting. Harvesting decreased wetland nitrogen removal in both years, with later harvest time producing a smaller decrease in TN and ammonium-nitrogen (NH4+-N) removal. In addition to harvest before wilting, aboveground reed harvest at mid-wilting harvested more nutrients [carbon (C) 7.9%, nitrogen (N) 46.6% and phosphorus (P) 43.6%] in the first year, while harvest at late wilting harvested more nutrients (C 4.9%, N 7.8% and P 24.1%) in the second year, although this was not statistically significant. The late wilting harvest caused fewer disturbances to root stoichiometric homeostasis in the first year, while mid-wilting harvest promoted root nutrient availability in the second year. In addition, redundancy analysis (RDA) showed that root stoichiometry was interrelated with wetland nitrogen removal. Our results suggest that optimal harvest time was late wilting on the basis of wetland nitrogen removal, or either mid- or late wilting according to reed nutrient response to influent nitrogen concentration in some years. Our results provide crucial information for winter wetlands management.

Protocadherin 8 (PCDH8) Inhibits Proliferation, Migration, Invasion, and Angiogenesis in Esophageal Squamous Cell Carcinoma.

BACKGROUND Protocadherin 8 (PCDH8) functions as a tumor-suppressor gene in many types of cancer. This study aimed to investigate the role of PCDH8 in esophageal squamous cell carcinoma (ESCC). MATERIAL AND METHODS Cell proliferation, apoptosis, transwell assay, tube formation assays, and tumor xenograft experiment were performed to explore the role of PCDH8 in the progression of ESCC. RESULTS PCDH8 was found to be downregulated in ESCC cells. Ectopic expression of PCDH8 blocked proliferation, invasion, and migration and induced apoptosis in ESCC cells. Furthermore, vascular endothelial growth factor A (VEGFA) secretion and the AKT signaling pathway were also inhibited when PCDH8 was upregulated. PCDH8 overexpression suppressed epithelial-mesenchymal transition (EMT) and pro-angiogenic activity of ESCC cells. In a mouse model of ESCC xenograft tumors, PCDH8 overexpression remarkably restrained tumor cell growth, with the tumor inhibition rate of 75.2%. PCDH8 was the target of miR-200c and had a negative correlation with miR-200c. CONCLUSIONS PCDH8 exerts a tumor-suppressive effect against ESCC cells. However, further studies are required to elucidate the exact molecular mechanism underlying the antitumor activity of PCDH8 in ESCC.

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk.

Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.

Responses of Antioxidant Enzymes to Chronic Free-Air Ozone Stress in Rice (Oryza sativa L.) Cultivars with Different Ozone-Sensitivities.

Free-air O3 enrichment was used to investigate the responses of different antioxidant mechanisms in different rice (Oryza sativa L.) cultivars - O3-sensitive hybrid indica (O3-S) cultivars and O3-tolerant conventional japonica (O3-T) cultivars across all growth stages. Elevated [O3] induced increases in reactive oxygen species (ROS) production in O3-S cultivars, which were more pronounced in the later growing stages. In O3-S cultivars, continuous O3 stress decreased catalase (CAT), peroxidase (POD) and glutathione peroxidase (GPX) activities, while in O3-T cultivars, short-term O3 stress decreased superoxide dismutase (SOD), CAT, POD and GPX activities. The same POD isozyme patterns were observed in both O3-S and O3-T cultivars, while SOD and APX isozymes varied by cultivar. The results suggest that O3 tolerance might be improved at different rice development stages through regulating the responses of antioxidant mechanisms to O3 stress.

Revealing Drug-Target Interactions with Computational Models and Algorithms.

BACKGROUND: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. METHODS: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. RESULTS: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.

Increased expression of plakophilin 3 is associated with poor prognosis in ovarian cancer.

Considering the essential role of plakophilin 3 (PKP3) in the maintenance cell-cell adhesion, dysregulation of PKP3 is involved in human diseases. This study aimed to explore the clinical significance of PKP3 in ovarian cancer. Immunohistochemistry was performed to examine the PKP3 expression in 157 cancer specimens from primary ovarian cancer patients. PKP3 was expressed in both the cytoplasm and nucleus. Eighty-one (51.6%) out of 157 ovarian cancer tissues showed PKP3 expression, while absent expression was observed in normal ovarian tissues. High PKP3 expression was associated with lymph node metastasis (LNM, P = .004) and advanced International Federation of Gynecology and Obstetrics (FIGO) stage (P = .013). Patients with high PKP3 expression had shorter overall survival (OS) than those with low PKP3 expression (60.2 months vs 74.2 months, P = .021). However, no association between PKP3 expression and progression-free survival (PFS) was observed (P = .790). Cox regression analysis indicated that PKP3 expression was an independently predictive factor for the OS of patient with ovarian cancer (adjusted HR = 1.601, 95%CI: 1.014-2.528, P = .043), especially those with FIGO stages III and IV disease (adjusted HR = 1.607, 95%CI: 1.006-2.567, P = .047). The gene expression profiling interactive analysis (GEPIA) databases also showed that PKP3 was upregulated in ovarian cancer (P < .001) and patients with high PKP3 expression had shorter OS (P = .004). In conclusion, our findings suggest that PKP3 is upregulated in ovarian cancer and is likely involved in the progression of ovarian cancer. PKP3 might therefore serve as a prognostic biomarker for patients with ovarian cancer.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.

PELP1 Suppression Inhibits Gastric Cancer Through Downregulation of c-Src-PI3K-ERK Pathway.

Background: Proline-, glutamic acid-, and leucine-rich protein 1 (PELP1), a co-activator of estrogen receptors alpha, was confirmed to be directly associated with the oncogenic process of multiple cancers, especially hormone-dependent cancers. The purpose of our research was to explore the biological function, clinical significance, and therapeutic targeted value of PELP1 in gastric cancer (GC). Methods: The expression status of PELP1 in GC cell lines or tissues was analyzed through bioinformatics data mining. Thirty-six GC tissue chip was applied to demonstrate the results of bioinformatics data mining assayed by immunohistochemical method. The expression status of PELP1 in GC cell lines was also analyzed using western blot. Correlation analysis between PELP1 expression and clinicopathological parameter was performed. Kaplan-Meier survival analysis was applied to analyze the relationship between PELP1 expression and total survival time. Three pairs of siRNA were designed to silence the expression of PELP1 in GC. After PELP1 was silenced by siRNA or activated by saRNA, the growth, plate colony formation, migration and invasion ability of the GC cell or normal gastric epithelium cell line was tested in vitro. Cell cycle was tested by flow cytometry. Nude mice xenograft experiment was performed after PELP1 was silenced. The downstream molecular pathway regulated by PELP1 was explored. Molecular docking tool was applied to combine chlorpromazine with PELP1. The inhibitory effect of chlorpromazine in GC was assayed, then it was tested whether PELP1 was a therapeutic target of chlorpromazine in GC. Results: PELP1 expression was elevated in GC cell lines and clinical GC tissue samples. PELP1 silence by siRNA compromised the malignant traits of GC. PELP1 expression positively correlated with tumor invasion depth, lymph node metastasis, tissue grade, TNM stage, but had no correlation with patient age, sex, tumor size, and tumor numbers. Kaplan-Meier survival analysis revealed high PELP1 expression had a shorter survival period in GC patients after follow-up. Q-PCR and western blot revealed PELP1 suppression in GC decreased expression of the c-Src-PI3K-ERK pathway. It was also implied that chlorpromazine (CPZ) can inhibit the malignant traits of GC and downregulate the expression of PELP1. Conclusions: In a word, PELP1 is an oncogene in gastric cancer and c-Src-PI3K-ERK pathway activation may be responsible for its tumorigenesis, PELP1 may be a potential therapeutic target of chlorpromazine in GC.