FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer and is highly lethal. Clonorchis sinensis (C. sinensis) infection is an important risk factor for iCCA. Here we investigated the clinical impact and underlying molecular characteristics of C. sinensis infection-related iCCA. METHODS: We performed single-cell RNA sequencing, whole-exome sequencing, RNA sequencing, metabolomics and spatial transcriptomics in 251 patients with iCCA from three medical centers. Alterations in metabolism and the immune microenvironment of C. sinensis-related iCCAs were validated through an in vitro co-culture system and in a mouse model of iCCA. RESULTS: We revealed that C. sinensis infection was significantly associated with iCCA patients' overall survival and response to immunotherapy. Fatty acid biosynthesis and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly enriched in C. sinensis-related iCCAs. iCCA cell lines treated with excretory/secretory products of C. sinensis displayed elevated FASN and free fatty acids. The metabolic alteration of tumor cells was closely correlated with the enrichment of tumor-associated macrophage (TAM)-like macrophages and the impaired function of T cells, which led to formation of an immunosuppressive microenvironment and tumor progression. Spatial transcriptomics analysis revealed that malignant cells were in closer juxtaposition with TAM-like macrophages in C. sinensis-related iCCAs than non-C. sinensis-related iCCAs. Importantly, treatment with a FASN inhibitor significantly reversed the immunosuppressive microenvironment and enhanced anti-PD-1 efficacy in iCCA mouse models treated with excretory/secretory products from C. sinensis. CONCLUSIONS: We provide novel insights into metabolic alterations and the immune microenvironment in C. sinensis infection-related iCCAs. We also demonstrate that the combination of a FASN inhibitor with immunotherapy could be a promising strategy for the treatment of C. sinensis-related iCCAs. IMPACT AND IMPLICATIONS: Clonorchis sinensis (C. sinensis)-infected patients with intrahepatic cholangiocarcinoma (iCCA) have a worse prognosis and response to immunotherapy than non-C. sinensis-infected patients with iCCA. The underlying molecular characteristics of C. sinensis infection-related iCCAs remain unclear. Herein, we demonstrate that upregulation of FASN (fatty acid synthase) and free fatty acids in C. sinensis-related iCCAs leads to formation of an immunosuppressive microenvironment and tumor progression. Thus, administration of FASN inhibitors could significantly reverse the immunosuppressive microenvironment and further enhance the efficacy of anti-PD-1 against C. sinensis-related iCCAs.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma.

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Th17 Cells Secrete TWEAK to Trigger Epithelial-Mesenchymal Transition and Promote Colorectal Cancer Liver Metastasis.

Liver metastasis is the leading cause of mortality in patients with colorectal cancer. Given the significance of both epithelial-mesenchymal transition (EMT) of tumor cells and the immune microenvironment in colorectal cancer liver metastasis (CRLM), the interplay between them could hold the key for developing improved treatment options. We employed multiomics analysis of 130 samples from 18 patients with synchronous CRLM integrated with external datasets to comprehensively evaluate the interaction between immune cells and EMT of tumor cells in liver metastasis. Single-cell RNA sequencing analysis revealed distinct distributions of nonmalignant cells between primary tumors from patients with metastatic colorectal cancer (mCRC) and non-metastatic colorectal cancer, showing that Th17 cells were predominantly enriched in the primary lesion of mCRC. TWEAK, a cytokine secreted by Th17 cells, promoted EMT by binding to receptor Fn14 on tumor cells, and the TWEAK-Fn14 interaction enhanced tumor migration and invasion. In mouse models, targeting Fn14 using CRISPR-induced knockout or lipid nanoparticle-encapsulated siRNA alleviated metastasis and prolonged survival. Mice lacking Il17a or Tnfsf12 (encoding TWEAK) exhibited fewer metastases compared with wild-type mice, while cotransfer of Th17 with tumor cells promoted liver metastasis. Higher TWEAK expression was associated with a worse prognosis in patients with colorectal cancer. In addition, CD163L1+ macrophages interacted with Th17 cells, recruiting Th17 via the CCL4-CCR5 axis. Collectively, this study unveils the role of immune cells in the EMT process and identifies TWEAK secreted by Th17 as a driver of CRLM. SIGNIFICANCE: TWEAK secreted by Th17 cells promotes EMT by binding to Fn14 on colorectal cancer cells, suggesting that blocking the TWEAK-Fn14 interaction may be a promising therapeutic approach to inhibit liver metastasis.

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics.

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

METTL3-m6A-EGFR-axis drives lenvatinib resistance in hepatocellular carcinoma.

Lenvatinib is emerging as the first-line therapeutic option for advanced hepatocellular carcinoma (HCC), but drug resistance remains a major hurdle for its long-term therapy efficiency in clinic. N6-methyladenosine (m6A) is the most abundant mRNA modification. Here, we aimed to investigate the modulatory effects and underlying mechanisms of m6A in lenvatinib resistance in HCC. Our data revealed that m6A mRNA modification was significantly upregulated in the HCC lenvatinib resistance (HCC-LR) cells compared to parental cells. Methyltransferase-like 3 (METTL3) was the most significantly upregulated protein among the m6A regulators. Either genetic or pharmacological inhibition of m6A methylation through METTL3 deactivation in primary resistant cell line MHCC97H and acquired resistant Huh7-LR cells decreased cell proliferation and increased cell apoptosis upon lenvatinib treatment in vitro and in vivo. In addition, the specific METTL3 inhibitor STM2457 improved tumor response to lenvatinib in multiple mouse HCC models, including subcutaneous, orthotopic and hydrodynamic models. The MeRIP-seq results showed that epidermal growth factor receptor (EGFR) was a downstream target of METTL3. EGFR overexpression abrogated the METTL3 knocked down-induced cell growth arrest upon lenvatinib treatment in HCC-LR cells. Thus, we concluded that targeting METTL3 using specific inhibitor STM2457 improved the sensitivity to lenvatinib in vitro and in vivo, indicating that METTL3 may be a potential therapeutic target to overcome lenvatinib resistance in HCC.

Implant survival rate and marginal bone loss with the all-on-4 immediate-loading strategy: A clinical retrospective study with 1 to 4 years of follow-up.

STATEMENT OF PROBLEM: Assessing peri-implant marginal bone loss (MBL) and its risk factors with cone beam computed tomography (CBCT) may clarify the risk factors for the all-on-4 (5 or 6) strategy and further improve its survival rate. PURPOSE: The purpose of this retrospective clinical study was to evaluate the implant survival rate, MBL, and associated risk factors of all-on-4 (5 or 6) prostheses after 1 to 4 years of follow-up with CBCT. MATERIAL AND METHODS: A total of 56 participants rehabilitated with 325 implants by using the all-on-4 (5 or 6) concept between October 2015 and December 2019 were included. Outcome measures were cumulative implant survival (life-table analysis) and MBL. Four CBCT scans, a scan immediately after surgery (T0), a scan 1 year after surgery (T1), a scan 2 years after surgery (T2), and a scan 3 to 4 years after treatment (T3), were obtained to evaluate the MBL. The Pearson correlation coefficient analysis and linear mixed models were performed to assess the potential risk factors for MBL (α=.05). RESULTS: The implant survival rate was 99.38%, and the prosthesis survival rate was 100%. The reductions in the vertical buccal bone height (△VBBH) were 0.74 ±0.10 mm (T0-T1), 0.37 ±0.12 mm (T1-T2), and 0.15 ±0.14 mm (T2-T3). Except for T2-T3, the △VBBH showed a significant difference at T0-T1 and T1-T2 (P≤.05). The alterations in vertical mesial bone height (VMBH), vertical distal bone height (VDBH), and vertical lingual bone height (VLBH) were similar to the trend observed in VBBH. The △VBBH (T0-T3) was negatively correlated with the horizontal buccal bone thickness (HBBT) (T0) (r=-.394, P<.001). Linear mixed models revealed that factors such as smoking (P=.001), mandible implant site (P<.001), immediate implant (P=.026), tilted implant (P<.001), female sex (P=.003), systemic disease (P=.025), and bruxism (P=.022) negatively affected MBL. The cantilever length (CL) also had a negative effect on MBL around the implants at the distal extension (P<.001). CONCLUSIONS: The high implant and prosthesis survival rates and low MBL confirmed the predictability of the all-on-4 (5 or 6) concept. Smoking, mandible implant site, systemic disease, bruxism, female sex, immediate implant, tilted implant, and CL were identified as potential risk factors for MBL.

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

BACKGROUND AND AIM: Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS: A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. RESULTS: We grouped 35 HCC patients into 2 categories, including the MVI- group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI- group than that in the predicted MVI + group. CONCLUSIONS: In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.

Potential Chemoprevention of Paeoniflorin in Colitis-Associated Colorectal Cancer by Network Pharmacology, Molecular Docking, and In Vivo Experiment.

Chronic inflammation plays a positive role in the development and progression of colitis-associated colorectal cancer (CAC). Medicinal plants and their extracts with anti-inflammatory and immunoregulatory properties may be an effective treatment and prevention strategy for CAC. This research aimed to explore the potential chemoprevention of paeoniflorin (PF) for CAC by network pharmacology, molecular docking technology, and in vivo experiments. The results showed that interleukin-6 (IL-6) is a key target of PF against CAC. In the CAC mouse model, PF increased the survival rate of mice and decreased the number and size of colon tumors. Moreover, reduced histological score of colitis and expression of Ki-67 and PCNA were observed in PF-treated mice. In addition, the chemoprevention mechanisms of PF in CAC may be associated with suppression of the IL-6/STAT3 signaling pathway and the IL-17 level. This research provides experimental evidence of potential chemoprevention strategies for CAC treatment.

Patient-derived cancer organoids for drug screening: Basic technology and clinical application.

Cancer organoids, a three-dimensional (3D) culture system of cancer cells derived from tumor tissues, recapitulate physiological structure of the parental tumor. Different tumor organoids have been established for a variety of tumor types, such as colorectal, liver, stomach, pancreatic and brain tumors. Some tumor organoid biobanks are built to screen and discover novel antitumor drug targets. Moreover, patients-derived tumor organoids (PDOs) could predict treatment response to chemoradiotherapy, targeted therapy and immunotherapy to provide guidance for personalized cancer therapy. In this review, we provide an updated overview of tumor organoid development, summarize general approach to establish tumor organoids, and discuss the application of anti-cancer drug screening based on tumor organoid and its application in personalized therapy. We also outline the opportunities and challenges for organoids to guide precision medicine.

METTL1 promotes hepatocarcinogenesis via m7 G tRNA modification-dependent translation control.

BACKGROUND: N7 -methylguanosine (m7 G) modification is one of the most common transfer RNA (tRNA) modifications in humans. The precise function and molecular mechanism of m7 G tRNA modification in hepatocellular carcinoma (HCC) remain poorly understood. METHODS: The prognostic value and expression level of m7 G tRNA methyltransferase complex components methyltransferase-like protein-1 (METTL1) and WD repeat domain 4 (WDR4) in HCC were evaluated using clinical samples and TCGA data. The biological functions and mechanisms of m7 G tRNA modification in HCC progression were studied in vitro and in vivo using cell culture, xenograft model, knockin and knockout mouse models. The m7 G reduction and cleavage sequencing (TRAC-seq), polysome profiling and polyribosome-associated mRNA sequencing methods were used to study the levels of m7 G tRNA modification, tRNA expression and mRNA translation efficiency. RESULTS: The levels of METTL1 and WDR4 are elevated in HCC and associated with advanced tumour stages and poor patient survival. Functionally, silencing METTL1 or WDR4 inhibits HCC cell proliferation, migration and invasion, while forced expression of wild-type METTL1 but not its catalytic dead mutant promotes HCC progression. Knockdown of METTL1 reduces m7 G tRNA modification and decreases m7 G-modified tRNA expression in HCC cells. Mechanistically, METTL1-mediated tRNA m7 G modification promotes the translation of target mRNAs with higher frequencies of m7 G-related codons. Furthermore, in vivo studies with Mettl1 knockin and conditional knockout mice reveal the essential physiological function of Mettl1 in hepatocarcinogenesis using hydrodynamics transfection HCC model. CONCLUSIONS: Our work reveals new insights into the role of the misregulated tRNA modifications in liver cancer and provides molecular basis for HCC diagnosis and treatment.

Synergism therapeutic and immunoregulatory effects of Albendazole + rAd-mIL-28B against Echinococcosis in experiment-infected mice with protoscoleces.

The metacestode stage of Echinococcus granulosus can cause cystic echinococcosis (CE), which still widely occurs around the world. Since the early 1970s, benzimidazoles have been shown to inhibit the growth of cysts and used to treat CE. However, benzimidazoles are still ineffective in 20%-40% of cases. In order to explore the new agents against CE, we have investigated the therapeutic effect of the recombinant adenoviral vector expressing mouse IL-28B (rAd-mIL-28B) on protoscoleces-infected mice. In our study, we successfully established the model mice which infected with protoscoleces intraperitoneally. At 18 weeks post-infection, the mice received rAd-mIL-28B (1×107 PFU) weekly by intramuscular injection for 6 weeks. Compared with the untreated control (13.1 ± 2.2 g), there was a significant reduction in cysts wet weight in rAd-mIL-28B group (8.3 ± 3.5 g) (P < 0.05), especially in Albendazole (ABZ) + rAd-mIL-28B group (5.8 ± 1.4 g) (P < 0.01). We also observed the severe damage of the germinal layer and the laminated layer of cysts after treatment. rAd-mIL-28B group showed a prominent increase in the level of Th1 type cytokines (such as IFN-γ, IL-2 and TNF-α). Meanwhile, the frequency of Foxp3+ T cells was decreased in the rAd-mIL-28B group (4.83 ± 0.81%) and ABZ + rAd-mIL-28B group (4.60 ± 0.51%), comparing with the untreated group (8.13 ± 2.60%) (P < 0.05). In addition, compared with the untreated control (122.14 ± 81.09 pg/ml), the level of IFN-γ significantly increased in peritoneal fluid in the rAd-mIL-28B group (628.87 ± 467.16 pg/ml) (P < 0.05) and ABZ + rAd-mIL-28B group (999.76 ± 587.60 pg/ml) (P < 0.001). Taken together, it suggested that ABZ + IL-28B may be a potential therapeutic agent against CE.

Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data.

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5-15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student's t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

A systematic review and network meta-analysis protocol of adjuvant chemotherapy regimens for resected gastric cancer.

BACKGROUND: Gastric cancer is the third leading cause of cancer death in the world. The benefit of adjuvant chemotherapy has been demonstrated by published individual patient data meta-analysis and Cochrane systematic review. However, there is no consensus on which is the optimal adjuvant chemotherapy regimens. Present network meta-analysis aims to compare the differences of effect between all available adjuvant chemotherapy regimens in improving overall survival and disease-free survival, and to rate the certainty of evidence from present network meta-analysis. METHODS: We will conduct this systematic review and network meta-analysis using Bayesian method and according to Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) statement. We will search PubMed, EMBASE.com, the Cochrane Central Register of Controlled Trials (CENTRAL), Chinese National Knowledge Infrastructure (CNKI), and Chinese Biological Medical Database (CBM), and ClinicalTrials.gov (http://clinicaltrials.gov/) to identify randomized controlled trials (RCTs) comparing adjuvant chemotherapy to surgery alone. We will assess the risk of bias of individual RCTs using a modified version of Cochrane tool. We will also use the advance of GRADE to rate the certainty of network meta-analysis. Data analysis will be performed with R-3.4.1 and WinBUGS software. RESULTS: The results of this study will be published in a peer-reviewed journal. DISCUSSION: To the best of our knowledge, this systematic review and network meta-analysis will firstly use both direct and indirect evidence to compare the differences of all available adjuvant chemotherapy regimens for resected gastric cancer patients. This is a protocol of systematic review and meta-analysis, so the ethical approval and patient consent are not required.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools.

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

Computational Advances in the Label-free Quantification of Cancer Proteomics Data.

BACKGROUND: Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders. OBJECTIVE: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key "technical challenge" in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed. METHODS: PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances. RESULTS: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed. CONCLUSION: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.