Environmental exposure to cadmium induces olfactory neurotoxicity in fire ants and the molecular basis.

Cadmium (Cd) exhibits widely olfactory toxicity to animals. We previously reported that Cd exposure induces the transcriptional dysregulation of olfactory marker proteins (OMPs) of the red imported fire ant Solenopsis invicta. However, it is still unclear how environmental Cd exposure-induced deregulation of OMPs affects the olfactory signal transduction and olfaction-driven social behavior of S. invicta. Here, we showed that S. invicta displayed dull sensory perception on bait in Cd-contaminated areas and dietary Cd ingestion by S. invicta reduced the bait search efficiency. We hypothesize that deregulation of OMPs by Cd exposure blocks the olfactory signal transduction in fire ants. Our results indicated the odor binding protein 14 (SiOBP14) was consistently inhibited in antennal sensilla of fire ants across Cd exposure at 0.5, 5 and 50 mg/kg. Function analysis in vitro and in vivo demonstrated that SiOBP14 is essential in perception of S. invicta to bait odorants. Cd-exposed fire ants showed weak odorant receptor neurons (ORNs) chemosensory signaling and electroantennogram (EAG) response. Moreover, Cd exposure repeals the preference of S. invicta to the active bait odorants, including 2-methyltetrahydrofuran-3-one, 2-methyl-3-furanthiol and 4,5-dimethylthiazole, and even triggers a behavioral transition from preference to repellence. These results indicate that Cd exposure inhibits the specific OMP expression and disrupts olfactory signal transduction, thereby inducing dull sensory perception of S. invicta to bait odorants. The findings provide new implications for monitoring and control of agricultural insect pests in heavy metal polluted areas.

Proteome-based molecular subtyping and therapeutic target prediction in gastric cancer.

Different molecular classifications for gastric cancer (GC) have been proposed based on multi-omics platforms with the long-term goal of improved precision treatment. However, the GC (phospho)proteome remains incompletely characterized, particularly at the level of tyrosine phosphorylation. In addition, previous multiomics-based stratification of patient cohorts has lacked identification of corresponding cell line models and comprehensive validation of broad or subgroup-selective therapeutic targets. To address these knowledge gaps, we applied a reverse approach, undertaking the most comprehensive (phospho)proteomic analysis of GC cell lines to date and cross-validating this using publicly available data. Mass spectrometry (MS)-based (phospho)proteomic and tyrosine phosphorylation datasets were subjected to individual or integrated clustering to identify subgroups that were subsequently characterized in terms of enriched molecular processes and pathways. Significant congruence was detected between cell line proteomic and specific patient-derived transcriptomic subclassifications. Many protein kinases exhibiting 'outlier' expression or phosphorylation in the cell line dataset exhibited genomic aberrations in patient samples and association with poor prognosis, with casein kinase I isoform delta/epsilon (CSNK1D/E) being experimentally validated as potential therapeutic targets. Src family kinases were predicted to be commonly hyperactivated in GC cell lines, consistent with broad sensitivity to the next-generation Src inhibitor eCF506. In addition, phosphoproteomic and integrative clustering segregated the cell lines into two subtypes, with epithelial-mesenchyme transition (EMT) and proliferation-associated processes enriched in one, designated the EMT subtype, and metabolic pathways, cell-cell junctions, and the immune response dominating the features of the other, designated the metabolism subtype. Application of kinase activity prediction algorithms and interrogation of gene dependency and drug sensitivity databases predicted that the mechanistic target of rapamycin kinase (mTOR) and dual specificity mitogen-activated protein kinase kinase 2 (MAP2K2) represented potential therapeutic targets for the EMT and metabolism subtypes, respectively, and this was confirmed using selective inhibitors. Overall, our study provides novel, in-depth insights into GC proteomics, kinomics, and molecular taxonomy and reveals potential therapeutic targets that could provide the basis for precision treatments.

Feed-forward stimulation of CAMK2 by the oncogenic pseudokinase PEAK1 generates a therapeutically 'actionable' signalling axis in triple negative breast cancer.

The PEAK family of pseudokinases, comprising PEAK1-3, are signalling scaffolds that play oncogenic roles in several poor prognosis human cancers, including triple negative breast cancer (TNBC). However, therapeutic targeting of pseudokinases is challenging due to their lack of catalytic activity. To address this, we screened for PEAK1 effectors by affinity purification and mass spectrometry, identifying calcium/calmodulin-dependent protein kinase 2 (CAMK2)D and CAMK2G. PEAK1 promoted CAMK2D/G activation in TNBC cells via a novel feed-forward mechanism involving PEAK1/PLCγ1/Ca 2+ signalling and direct binding via a consensus CAMK2 interaction motif in the PEAK1 N-terminus. In turn, CAMK2 phosphorylated PEAK1 to enhance association with PEAK2, which is critical for PEAK1 oncogenic signalling. To achieve pharmacologic targeting of PEAK1/CAMK2, we repurposed RA306, a second generation CAMK2 inhibitor under pre-clinical development for treatment of cardiovascular disease. RA306 demonstrated on-target activity against CAMK2 in TNBC cells and inhibited PEAK1-enhanced migration and invasion in vitro . Moreover, RA306 significantly attenuated TNBC xenograft growth and blocked metastasis in a manner mirrored by CRISPR-mediated PEAK1 ablation. Overall, these studies establish PEAK1 as a critical cell signalling nexus, identify a novel mechanism for regulation of Ca 2+ signalling and its integration with tyrosine kinase signals, and identify CAMK2 as a therapeutically 'actionable' target downstream of PEAK1.

Improving the Surface Oxygen Vacancy Concentration of Bi2O4 through the Pretreatment of the NaBiO3·2H2O Precursor as a High-Performance Visible Light Photocatalyst.

The oxygen-deficient bismuth oxide, Bi2O4, synthesized by a typical hydrothermal method using commercial NaBiO3·2H2O as a raw material only has a relatively low concentration of surface oxygen vacancies (OVs). How to improve the visible light photocatalytic performance of Bi2O4 via tuning its surface OV concentration is still a huge challenge. In this study, improving the surface OVs of Bi2O4 was successfully realized through the pretreatment of commercial NaBiO3·2H2O, including thermal treatment in air and hydrothermal treatment in 10 M NaOH solution, forming NaBiO3·xH2O intermediate products first, and then hydrothermal preparation of Bi2O4 target products using NaBiO3·xH2O instead of commercial NaBiO3·2H2O as the precursor. The enhanced surface OV content not only narrows the band gap of Bi2O4 and thus extends its optical response range but also captures more photoexcited electrons and thus increases the charge carriers' separation efficiency and prolongs the charge carriers' lifetime of Bi2O4. Among the above-mentioned two pretreatment methods, the effects of the hydrothermal pretreatment are superior to those of the thermal treatment, involving the increase of surface OVs, the optical harvesting capacity, and the charge carriers' separation efficiency. Accordingly, Bi2O4 prepared by the hydrothermal pretreatment route exhibits the optimal visible light catalytic performance toward the removal of methyl orange (MO) and phenol due to its most abundant surface OV concentration, which is 2.59 times and 4.26 times higher than that of Bi2O4 synthesized directly by the commercial NaBiO3·2H2O route, respectively. Holes (h+) and superoxide radicals (•O2-) are identified as the main active species, while singlet oxygen (1O2) and hydroxyl radicals (•OH) are verified as the second and third important active species for organic pollutant removal, respectively. This work has developed a novel strategy to promote the catalytic performance of single Bi2O4 induced by the enhanced surface OV concentration through the pretreatment of the precursor, commercial NaBiO3·2H2O.

DEMoS: a deep learning-based ensemble approach for predicting the molecular subtypes of gastric adenocarcinomas from histopathological images.

MOTIVATION: The molecular subtyping of gastric cancer (adenocarcinoma) into four main subtypes based on integrated multiomics profiles, as proposed by The Cancer Genome Atlas (TCGA) initiative, represents an effective strategy for patient stratification. However, this approach requires the use of multiple technological platforms, and is quite expensive and time-consuming to perform. A computational approach that uses histopathological image data to infer molecular subtypes could be a practical, cost- and time-efficient complementary tool for prognostic and clinical management purposes. RESULTS: Here, we propose a deep learning ensemble approach (called DEMoS) capable of predicting the four recognized molecular subtypes of gastric cancer directly from histopathological images. DEMoS achieved tile-level area under the receiver-operating characteristic curve (AUROC) values of 0.785, 0.668, 0.762 and 0.811 for the prediction of these four subtypes of gastric cancer [i.e. (i) Epstein-Barr (EBV)-infected, (ii) microsatellite instability (MSI), (iii) genomically stable (GS) and (iv) chromosomally unstable tumors (CIN)] using an independent test dataset, respectively. At the patient-level, it achieved AUROC values of 0.897, 0.764, 0.890 and 0.898, respectively. Thus, these four subtypes are well-predicted by DEMoS. Benchmarking experiments further suggest that DEMoS is able to achieve an improved classification performance for image-based subtyping and prevent model overfitting. This study highlights the feasibility of using a deep learning ensemble-based method to rapidly and reliably subtype gastric cancer (adenocarcinoma) solely using features from histopathological images. AVAILABILITY AND IMPLEMENTATION: All whole slide images used in this study was collected from the TCGA database. This study builds upon our previously published HEAL framework, with related documentation and tutorials available at http://heal.erc.monash.edu.au. The source code and related models are freely accessible at https://github.com/Docurdt/DEMoS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the "digital grade" cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.

HEAL: an automated deep learning framework for cancer histopathology image analysis.

MOTIVATION: Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational environment and of hyperparameter optimization, which hinder deployment and reduce reproducibility. RESULTS: Here, we propose HEAL, a deep learning-based automated framework for easy, flexible and multi-faceted histopathological image analysis. We demonstrate its utility and functionality by performing two case studies on lung cancer and one on colon cancer. Leveraging the capability of Docker, HEAL represents an ideal end-to-end tool to conduct complex histopathological analysis and enables deep learning in a broad range of applications for cancer image analysis. AVAILABILITY AND IMPLEMENTATION: The docker image of HEAL is available at https://hub.docker.com/r/docurdt/heal and related documentation and datasets are available at http://heal.erc.monash.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies.

INTRODUCTION: Genes contain multiple promoters that can drive the expression of various transcript isoforms. Although transcript isoforms from the same gene could have diverse and non-overlapping functions, current loss-of-function methodologies are not able to differentiate between isoform-specific phenotypes. RESULTS: Here, we show that CRISPR interference (CRISPRi) can be adopted for targeting specific promoters within a gene, enabling isoform-specific loss-of-function genetic screens. We use this strategy to test functional dependencies of 820 transcript isoforms that are gained in gastric cancer (GC). We identify a subset of GC-gained transcript isoform dependencies, and of these, we validate CIT kinase as a novel GC dependency. We further show that some genes express isoforms with opposite functions. Specifically, we find that the tumour suppressor ZFHX3 expresses an isoform that has a paradoxical oncogenic role that correlates with poor patient outcome. CONCLUSIONS: Our work finds isoform-specific phenotypes that would not be identified using current loss-of-function approaches that are not designed to target specific transcript isoforms.

Tumor-infiltrating B cell is associated with the control of progression of gastric cancer.

This study aimed to further explore the clinicopathological correlation of B cell infiltration in gastric cancer (GC) and its impact on prognostic. By immunohistochemical method, CD20+ B cells, CD3+ T cells, CD66b+ tumor-associated neutrophils, CD163+ tumor-associated macrophages, and CD57+ natural killer cells were analyzed in consecutive sections of 584 GC tissues and 69 normal adjacent tissues. Kaplan-Meier and Cox regression analyses determined the relationship between clinical relevance or prognosis and B cell infiltration. The correlation between total B cell infiltration and various T cell subtype infiltration in GC tissues from 407 patients in the TCGA data was also analyzed. Kaplan-Meier and Cox regression analyses determined the effects of total B cell infiltration and various B cell subtype infiltration on the prognosis of patients with GC. The infiltration level of CD20+ B cells was positively correlated with that of T cells (risk ratio [RR] = 0.0930), especially CD4+ T cells and CD8+ T cells (P < 0.05). A high level of CD20+ B cell infiltration was significantly associated with low lymph node involvement and low TNM stage (P < 0.05). High levels of CD20+ B cell infiltration were significantly associated with improvements in overall survival and disease-free survival. Univariate Cox regression and multivariate Cox regression analysis showed that CD20+ B cell infiltration was an independent protective factor of prognosis. Higher levels of class-switched memory B cell and plasma cell also reflected better overall survival, and class-switched memory B cell and plasma cell were independent protective factors for prognosis. The findings indicate that B cell infiltration in GC, especially switched memory B cells and plasma cells, has a significant effect on tumor progression and prognosis.

Acid etching followed by hydrothermal preparation of nanosized Bi2O4/Bi2O3 p-n junction as highly efficient visible-light photocatalyst for organic pollutants removal.

As a promising visible-light photocatalyst, Bi2O4 has the advantage of broadband spectral response range. However, the high recombination rate of photoexcited charge carriers induced by the submicrorod morphology of pure Bi2O4 greatly restricts its visible-light photocatalytic performance. Herein, a novel nanosized Bi2O4/Bi2O3 p-n junction was developed by a dilute HCl acid etching and subsequent hydrothermal method, using NaBiO3·2H2O as the sole bismuth precursor. A product of NaBiO3·2H2O@BiOCl was formed firstly when NaBiO3·2H2O was partially reduced by insufficient dilute HCl aqueous solution. Then, BiOCl reacted with NaBiO3·2H2O during the following hydrothermal reaction process, resulting in the formation of Bi2O4 nanoparticles (NPs) anchored on the surface of plate-like Bi2O3. The content of Bi2O3 in the junction can be easily controlled by changing the added amount of dilute HCl acid. This strategy could not only realize the NPs-sized Bi2O4 but also construct nanometered Bi2O4/Bi2O3 p-n junction simultaneously, which remarkably improves the separation efficiency of charge carriers. Furthermore, the obtained Bi2O4/Bi2O3 heterojunctions have larger specific surface areas than Bi2O4 alone. Due to these advantages, the photocatalytic removal rate of methyl orange (MO) and phenol for the optimal Bi2O4/Bi2O3 heterostructure increased respectively by 5.06 and 2.16 times under visible light, when compared with single Bi2O4. The results of active species trapping experiment and electron spin resonance (ESR) spectra indicate that holes (h+) and superoxide radicals (O2-) are the primary and secondary reactive active species during the photocatalytic degradation process, respectively. This work provides a novel perspective for the design and preparation of high performance Bi2O4-based photocatalyst.

Overexpression of the human cytomegalovirus UL111A is correlated with favorable survival of patients with gastric cancer and changes T-cell infiltration and suppresses carcinogenesis.

PURPOSE: We previously found that human cytomegalovirus (HCMV) infection is associated with gastric cancer (GC) development. UL111A plays a role during HCMV productive or latent infection. However, UL111A expression profiles in GC tissues and their relationship with this disease are unknown. METHODS: PCR and nested RT-PCR were performed to verify UL111A expression in 71 GC tissues and its transcripts in 16 UL111A-positive GC samples. UL111A expression levels in GC patients were evaluated by immunohistochemistry on a tissue microarray for 620 GC patients. The correlations among UL111A expression levels, clinicopathological characteristics, and prognosis were analyzed. Further, the effects of overexpression of latency-associated viral interleukin-10 (LAcmvIL-10) and cmvIL-10 on GC cell proliferation, colony formation, migration, and invasion were assessed. RESULTS: The UL111A detection rate in GC tissues was 32.4% (23/71) and that of its mRNA expression was 68.75% (11/16). High expression of UL111A was also related to better overall and disease-free survival in GC patients. GC patients with TNM II/III stage expressing higher UL111A levels might benefit from adjuvant chemotherapy (ACT) after surgery. Moreover, high UL111A expression was also associated with increased CD4+ , CD8+ T-lymphocyte and Foxp3+ T-cell infiltration. In vitro assays further demonstrated that LAcmvIL-10 and cmvIL-10 overexpression inhibits GC cell line proliferation, colony formation, migration, and invasion. CONCLUSIONS: High UL111A expression changes the number of infiltrating T cells and is associated with favorable survival. Therefore, UL111A could be used as an independent prognostic biomarker and might be a potential therapeutic target for GC.

Downregulation of lncRNA CCDC26 contributes to imatinib resistance in human gastrointestinal stromal tumors through IGF-1R upregulation.

Imatinib is the first line of therapy for patients with metastatic or gastrointestinal stromal tumors (GIST). However, drug resistance limits the long-term effect of imatinib. Long non-coding RNAs (lncRNAs) are emerging as key players in regulating drug resistance in cancer. In this study, we investigated the association between lncRNA CCDC26 and IGF-1R in GIST and their involvement in drug resistance. Considering the key role of lncRNAs in drug resistance in cancer, we hypothesized that IGF-1R is regulated by lncRNAs. The expression of a series of reported drug resistance-related lncRNAs, including CCDC26, ARF, H19, NBR2, NEAT1, and HOTAIR, in GIST cells treated with imatinib H19 was examined at various time-points by qRT-PCR. Based on our results and published literature, CCDC26, a strongly down-regulated lncRNA following imatinib treatment, was chosen as our research target. GIST cells with high expression of CCDC26 were sensitive to imatinib treatment while knockdown of CCDC26 significantly increased the resistance to imatinib. Furthermore, we found that CCDC26 interacted with c-KIT by RNA pull down, and that CCDC26 knockdown up-regulated the expression of IGF-1R. Moreover, IGF-1R inhibition reversed CCDC26 knockdown-mediated imatinib resistance in GIST. These results indicated that treatments targeting CCDC26-IGF-1R axis would be useful in increasing sensitivity to imatinib in GIST.

Low periostin expression predicts poor survival in intestinal type gastric cancer patients.

BACKGROUND AND AIM: Periostin is a protein from the Fascilin family. It is commonly present in normal tissues and is responsible for cell adhesion. Evidence has emerged showing that changes in periostin expression play an important role in tumor initiation, development, and progression. This study aims to investigate the effect of periostin in gastric cancer (GC) patients who underwent gastrectomy. Seven hundred and forty-seven GC patients who underwent gastrectomy between December 2006 and July 2011 were included in this study. METHODS: Seven hundred and forty-seven cancer tissues and 70 paired adjacent normal tissues were collected. Periostin expression was evaluated by immunohistochemistry. The Gene Expression Omnibus database was used to study the association between the mRNA level and patient's overall survival. The tumor microenvironment was also studied. RESULTS: Periostin expression in stroma was downregulated in tumor tissues but it was upregulated in the epithelial cells. After dividing the tissues according to the Lauren Classification, we found that periostin expression in stroma and epithelial cells was higher in intestinal type than in diffuse type (P<0.001 and P=0.010, respectively). Periostin was an independent predictor of lymph node (LN) metastasis in GC patients. The study of CD163(+) tumor-associated macrophages (TAMs) revealed that in diffuse type GC, periostin expression was associated with CD163(+) TAMs. CONCLUSION: We found that the periostin expression can predict LN metastasis in patients undergoing curative gastrectomy. Intestinal type GC patients with high periostin level had both a favorable survival and lesser LN metastasis.

Esophageal cancer lymph node metastasis-associated gene signature optimizes overall survival prediction of esophageal cancer.

Esophageal cancer (EC) is characteristic of early regional lymph node metastasis (LNM) and most patients with metastasis have a poor prognosis. However, the current diagnostic techniques do not enable precise differentiation of EC LNM, prognostic stratification, and individual survival estimation. To identify potential molecular biomarkers for EC patients with LNM, we explored differently expressed genes in The Cancer Genome Atlas database between 77 non-LNM cases and 88 LNM cases by limma package R. Then, according to univariate and multivariate Cox regression analyses, we constructed an 8-messenger RNA (mRNA) prognostic signature model, which could predict the outcome in a more exact way. The area under the curve of the risk score is significantly higher than other clinical information, indicating that the 8-mRNA-based risk score is a good indicator for prognosis. Then, combined with other individual risk factors, such as age, sex, T stage, M stage, etc, we could precisely calculate the individual 1-, 3-, and 5-year survival rates. The Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis indicate that the risk model is mainly associated with cancer-related pathways, such as cell division, cellular meiosis, and cell cycle regulation. In summary, the 8-mRNA-based risk score model that we developed successfully predicts the survival of EC. It is independent of clinical information and performing better than other clinical information for prognosis.

Downregulation of lncRNA CCDC26 contributes to imatinib resistance in human gastrointestinal stromal tumors through IGF-1R upregulation

Imatinib is the first line of therapy for patients with metastatic or gastrointestinal stromal tumors (GIST). However, drug resistance limits the long-term effect of imatinib. Long non-coding RNAs (lncRNAs) are emerging as key players in regulating drug resistance in cancer. In this study, we investigated the association between lncRNA CCDC26 and IGF-1R in GIST and their involvement in drug resistance. Considering the key role of lncRNAs in drug resistance in cancer, we hypothesized that IGF-1R is regulated by lncRNAs. The expression of a series of reported drug resistance-related lncRNAs, including CCDC26, ARF, H19, NBR2, NEAT1, and HOTAIR, in GIST cells treated with imatinib H19 was examined at various time-points by qRT-PCR. Based on our results and published literature, CCDC26, a strongly down-regulated lncRNA following imatinib treatment, was chosen as our research target. GIST cells with high expression of CCDC26 were sensitive to imatinib treatment while knockdown of CCDC26 significantly increased the resistance to imatinib. Furthermore, we found that CCDC26 interacted with c-KIT by RNA pull down, and that CCDC26 knockdown up-regulated the expression of IGF-1R. Moreover, IGF-1R inhibition reversed CCDC26 knockdown-mediated imatinib resistance in GIST. These results indicated that treatments targeting CCDC26-IGF-1R axis would be useful in increasing sensitivity to imatinib in GIST.

Sarcopenic Obesity Is Associated with Severe Postoperative Complications in Gastric Cancer Patients Undergoing Gastrectomy: a Prospective Study.

OBJECTIVE: This study aimed to determine the risk of severe postoperative complications (SPCs) in patients with gastric cancer and to construct a nomogram based on independently related factors to identify high-risk patients. METHODS: We conducted a prospective study of 636 consecutive patients with gastric cancer who underwent radical gastrectomy. Degrees of sarcopenia and obesity were calculated before surgery. Factors contributing to SPCs were determined using univariate and multivariate analysis. A nomogram consisting of the independent risk factors was constructed to quantify the individual risk of SPCs. RESULTS: Logistic analysis revealed that sarcopenic obesity, age, open surgery, and combined resection were independent prognostic factors for SPCs. Sarcopenic obese patients have the highest risk in all patients (sarcopenic obesity vs normal, OR = 6.575 p = 0.001; sarcopenic obesity vs obesity, OR = 5.833 p = 0.001; sarcopenic obesity vs sarcopenia, OR = 2.571 p = 0.032), while obese patients share the similar rate of SPCs with normal people (obesity vs normal, OR = 1.056 p = 0.723). The nomogram we constructed was able to quantify the risk of SPCs reliably (c-index, 0.737). CONCLUSIONS: Sarcopenic obesity, together with age, open surgery, and combined resection are independent predictors of SPCs. Obesity will significantly increase the risk of SPCs in sarcopenic patient with gastric cancer, but it will not bring higher risk to normal patients. Our nomogram is a simple and practical instrument to identify patients at high risk of surgical complications.

Human cytomegalovirus detection in gastric cancer and its possible association with lymphatic metastasis.

Increasing evidence suggests that human cytomegalovirus (HCMV) is associated with many human malignancies. However, its prevalence in gastric cancer (GC) and clinical association remain unknown. HCMV IgG and IgM antibodies in the sera of 80 GC patients and 80 healthy controls were detected using a microparticle enzyme immunoassay. The prevalence of HCMV UL47, UL55, UL56, and UL77 genes among 102 GC tumor tissues and adjacent normal specimens was measured by polymerase chain reaction (PCR) or nested PCR. Quantitative real-time PCR (Q-PCR) was used to determine viral load. Virus localization in neoplastic tissues was determined by immunohistochemistry. No significant difference of HCMV IgG and IgM seropositivity was found between GC patients and the healthy group. However, the overall HCMV DNA positivity rate was significantly higher in GC cancerous tissue compared with in paired normal tissue (P<0.01). HCMV infection was mainly localized in the tumorous epithelium. Q-PCR in HCMV-positive specimens indicated that the viral copy number was notably higher in GC tissues than in adjacent normal specimens (P<0.001). Clinical statistical analysis indicated that HCMV load in GC tumor tissue was positively associated with lymphatic metastasis (P=0.043), the area under the receiver operating characteristic (ROC) curve was 0.6638. Our data clearly provide the prevalence of HCMV in GC patients. We conclude that HCMV infection in malignant tissues might be associated with carcinogenesis or progression of GC and possibly relates to lymphatic metastasis.

Mining, Validation, and Clinical Significance of Colorectal Cancer (CRC)-Associated lncRNAs.

BACKGROUND: Colorectal cancer (CRC) is one of the deadliest tumours, but its pathogenesis remains unclear. The involvement of differentially expressed long non-coding RNAs (lncRNAs) in CRC tumorigenesis makes them suitable tumour biomarkers. METHODS/FINDINGS: Here, we screened 150 cases of CRC and 85 cases of paracancerous tissues in the GEO database for differentially expressed lncRNAs. The levels of lncRNA candidates in 84 CRC and paracancerous tissue samples were validated by qRT-PCR and their clinical significance was analyzed. We identified 15 lncRNAs with differential expression in CRC tumours; among them, AK098081 was significantly up-regulated, whereas AK025209, BC040303, BC037331, AK026659, and CR749831 were down-regulated in CRC. In a receiver operating characteristic curve analysis, the area under the curve for the six lncRNAs was 0.914. High expression of AK098081 and low expression of BC040303, CR749831, and BC037331 indicated poor CRC differentiation. CRC patients with lymph node metastasis had lower expression of BC037331. In addition, the group with high AK098081 expression presented significantly lower overall survival and disease-free survival rates than the low-expression group, confirming AK098081 as an independent risk factor for CRC patients. CONCLUSION/SIGNIFICANCE: In conclusion, we have identified multiple CRC-associated lncRNAs from microarray expression profiles that can serve as novel biomarkers for the diagnosis and prognosis of CRC.

Combination of preoperative platelet/lymphocyte and neutrophil/lymphocyte rates and tumor-related factors to predict lymph node metastasis in patients with gastric cancer.

OBJECTIVE: Lymph node (LN) metastasis is widely accepted as a poor prognosis indicator in patients with gastric cancer. An accurate preoperative prediction of LN status is of crucial importance for the planning treatment. The aim of the present study was to assess the predictive value of the preoperative platelet/lymphocyte (PLR) and neutrophil/lymphocyte rates (NLR) on the LN metastasis in gastric cancer patients and to develop a new preoperative score system to predict LN metastasis. PATIENTS AND METHODS: A total of 492 operable patients with gastric cancer were enrolled in our study. The clinical utility of the PLR and NLR was evaluated by receiver operating characteristic (ROC) curves. The logistic analysis was used to identify the independent parameters associated with LN metastasis. Then, a score system including those independent parameters that can be detected preoperatively was established, which was also tested by an ROC curve. RESULTS: The ideal cutoff values for predicting LN metastasis were 1.59 for NLR and 155.67 for PLR according to the ROC curve. Multivariate analyses showed that both PLR and NLR are significantly associated with LN metastasis independent of depth of invasion, lymphatic invasion, macroscopic type, and tumor size. The area under the ROC curve of the score system was 0.830 (95% confidence interval 0.782-0.878), showing a reliable ability to evaluate the status of nodal involvement. CONCLUSION: Preoperative PLR and NLR are useful biomarkers to predict LN metastasis and the score system in our study may serve as a reliable instrument to predict LN metastasis in gastric cancer patients.