Global trends in indocyanine green fluorescence navigation in the field of gastric cancer: bibliometrics and knowledge atlas analysis.

Background: Indocyanine green (ICG) fluorescence navigation can enhance the visualization of gastric cancer (GC) lesions, increase the lymph node detection rate, and reduce the incidence of anastomotic leakage in the treatment of GC. It thus holds considerable potential for application in GC clinical surgery and has attracted widespread research interest. The purpose of this study was to visualize the current topics and emerging trends in research regarding ICG in GC. Methods: We searched the Web of Science Core Collection (WoSCC) for articles relevant to the use of ICG in GC. The resulting information was then analyzed from a bibliometric and knowledge graph analysis perspective using CiteSpace, Scimago Graphica, and R Studio so that the key trends and hot spots in research within this field could be identified and visualized. Results: Ultimately, 1,385 papers from 58 countries or regions published from 1991 to 2022 were included in this study. The largest number of publications were from China, followed by Japan and the United States. High-yield institutions were concentrated in Asian countries, especially China. The top publication contributors were Shanghai Jiao Tong University. Li Y and Bang YJ ranked first among the top 10 most productive authors and top 10 most cocited authors, respectively. World Journal of Gastroenterology was the most productive academic journal on ICG in GC, while Cancer Research was the most commonly cocited journal. The keyword "indocyanine green" was among the top 5 keywords, and will likely remain a popular topic in future research. Furthermore, the emerging themes including surgery, biopsy, lymphadenectomy, dissection, and gastrectomy have attracted increasing attention. Conclusions: Current research hotspots in this area focus on the clinical implementation of ICG in precision surgery for GC. Given the imaging tracer characteristics of ICG and its utility in GC surgery, the optimization and application of ICG-guided precision surgery techniques for GC will be a research hot spot going forward.

Hub gene identification and molecular subtype construction for Helicobacter pylori in gastric cancer via machine learning methods and NMF algorithm.

Helicobacter pylori (HP) is a gram-negative and spiral-shaped bacterium colonizing the human stomach and has been recognized as the risk factor of gastritis, peptic ulcer disease, and gastric cancer (GC). Moreover, it was recently identified as a class I carcinogen, which affects the occurrence and progression of GC via inducing various oncogenic pathways. Therefore, identifying the HP-related key genes is crucial for understanding the oncogenic mechanisms and improving the outcomes of GC patients. We retrieved the list of HP-related gene sets from the Molecular Signatures Database. Based on the HP-related genes, unsupervised non-negative matrix factorization (NMF) clustering method was conducted to stratify TCGA-STAD, GSE15459, GSE84433 samples into two clusters with distinct clinical outcomes and immune infiltration characterization. Subsequently, two machine learning (ML) strategies, including support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), were employed to determine twelve hub HP-related genes. Beyond that, receiver operating characteristic and Kaplan-Meier curves further confirmed the diagnostic value and prognostic significance of hub genes. Finally, expression of HP-related hub genes was tested by qRT-PCR array and immunohistochemical images. Additionally, functional pathway enrichment analysis indicated that these hub genes were implicated in the genesis and progression of GC by activating or inhibiting the classical cancer-associated pathways, such as epithelial-mesenchymal transition, cell cycle, apoptosis, RAS/MAPK, etc. In the present study, we constructed a novel HP-related tumor classification in different datasets, and screened out twelve hub genes via performing the ML algorithms, which may contribute to the molecular diagnosis and personalized therapy of GC.

Exploring Cancer Dependency Map genes and immune subtypes in colon cancer, in which TIGD1 contributes to colon cancer progression.

BACKGROUND: Tumour-dependent genes identified in CRISPR-Cas9 screens have been widely reported in Cancer Dependency Maps (CDMs). CDM-derived tumour-dependent genes play an important role in tumorigenesis and progression; however, they have not been investigated in colon cancer (CC). METHODS: CDM genes overexpressed in CC were identified from the TCGA-COAD dataset and CDM platform. A CDM signature and prognostic nomogram were constructed by Lasso Cox regression and multivariate Cox analyses. A weighted correlation network analysis (WGCNA) and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. A comprehensive investigation was performed between the two subtypes and immune regulation, the immune microenvironment and the impact of immunotherapy. RESULTS: First, 1304 overexpressed CDM genes were identified. Then, a CDM signature with five cancer-dependent genes (MMS19, NOP14, POLRMT, SNAPC5 and TIGD1) and a prognostic nomogram were constructed, and they demonstrated robust predictive performance and a close relationship with clinical characteristics in different CC datasets. Patients with high CDM risk scores showed worse survival outcome and weaker response to chemotherapy. Additionally, TIGD1 genes were oncogenes that affected the CC cell cycle, according to cell functional experiments that involved the suppression of the TIGD1 gene. Furthermore, WGCNA and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. Finally, systematic investigations were conducted with the relationship between the CDM subtypes and immune regulation. CONCLUSIONS: This study constructed a CDM signature consisting of five risk genes that predict survival in CC patients. In addition, the immune subtypes provided valuable insights into immunotherapy for CC patients. TIGD1, as an oncogene, is independent prognostic factors for CC, and contributes to CC progression.

Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.

""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726-0.945) and 0.779 (95% CI 0.634-0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.

Nonfunctional ectopic adrenocortical carcinoma in the lung: A case report and literature review.

Background: Ectopic adrenocortical tissues and neoplasms are rare and usually found in the genitourinary system and abdominal cavity. The thorax is an extremely rare ectopic site. Here, we report the first case of nonfunctional ectopic adrenocortical carcinoma (ACC) in the lung. Case presentation: A 71-year-old Chinese man presented with vague left-sided chest pain and irritable cough for 1 month. Thoracic computed tomography revealed a heterogeneously enhancing 5.3 × 5.8 × 6.0-cm solitary mass in the left lung. Radiological findings suggested a benign tumor. The tumor was surgically excised upon detection. Histopathological examination using hematoxylin and eosin staining showed that the cytoplasm of the tumor cells was rich and eosinophilic. Immunohistochemical profiles (inhibin-a+, melan-A+, Syn+) indicated that the tumor had an adrenocortical origin. The patient showed no symptoms of hormonal hypersecretion. The final pathological diagnosis was non-functional ectopic ACC. The patient was disease-free for 22 months and is still under follow-up. Conclusions: Nonfunctional ectopic ACC in the lung is an extremely rare neoplasm that can be easily misdiagnosed as primary lung cancer or lung metastasis, both preoperatively and on postoperative pathological examination. This report may provide clues to clinicians and pathologists regarding the diagnosis and treatment of nonfunctional ectopic ACC.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022).

Background: Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis. Objective: This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions. Method: We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results. Result: This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination. Conclusion: AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Multiomics characteristics and immunotherapeutic potential of EZH2 in pan-cancer.

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy.

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images.

Background: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. Methods: We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. Results: The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. Conclusions: These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

Role of STX6 as a prognostic factor associated with immune infiltration in hepatocellular carcinoma.

Syntaxin 6 (STX6), a soluble N-ethylmaleimide-sensitive factor-activating receptor protein, has formed an increasing part of cancer research. However, to the best of our knowledge, the role of STX6 in hepatocellular carcinoma (HCC) is still unclear. In the present study, data from multiple bioinformatics databases, including The Cancer Genome Atlas, Gene Expression Omnibus, Kaplan-Meier plotter, Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Integrative Analysis (GEPIA2), and immunohistochemistry (IHC) were utilized to assess the role of STX6 in HCC. The results demonstrated that STX6 expression was upregulated in HCC tissues compared with normal tissues. STX6 expression was significantly associated with tumor size, Edmondson grade and α-fetoprotein (AFP) level. Furthermore, survival analysis demonstrated that high STX6 expression was significantly associated with poor prognosis in patients with HCC. Furthermore, assessment of the immune infiltrates demonstrated that CD163 expression was positively correlated with the STX6 level when analyzed using the TIMER and GEPIA2 databases. IHC results further demonstrated this association. Furthermore, compared with the typically used AFP, STX6 could have an improved diagnostic value in the diagnosis of HCC. In conclusion, STX6 expression was not only positively associated with poor prognosis but may also be involved in the immune inflammatory reaction in HCC. STX6 may become a potential therapeutic and diagnosis maker for patients with HCC.

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.

Background: This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods: This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared. Results: We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively. Conclusion: We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.

Background: DNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC). Methods: In this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts. Results: The radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values <0.001). A radiomic nomogram incorporating a radiomic signature and two clinical characteristics (age and CT-reported N stage) represented good discrimination in the training cohort with an AUC of 0.902 (95% CI: 0.853-0.951), in the internal validation cohort with an AUC of 0.972 (95% CI: 0.945-1.000) and in the external validation cohort with an AUC of 0.891 (95% CI: 0.825-0.958). Conclusion: The CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.

A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer.

The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, AC007277.1, AC005324.4, AL512506.1, AC068790.7, AC022509.2, AC113139.1, LINC00106, AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.

A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.

Background: As a caspase-independent type of cell death, necroptosis plays a significant role in the initiation, and progression of gastric cancer (GC). Numerous studies have confirmed that long non-coding RNAs (lncRNAs) are closely related to the prognosis of patients with GC. However, the relationship between necroptosis and lncRNAs in GC remains unclear. Methods: The molecular profiling data (RNA-sequencing and somatic mutation data) and clinical information of patients with stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted to identify the necroptosis-related lncRNAs (NRLs). Subsequently, univariate Cox regression and LASSO-Cox regression were conducted to establish a 12-NRLs signature in the training set and validate it in the testing set. Finally, the prognostic power of the 12-NRLs signature was appraised via survival analysis, nomogram, Cox regression, clinicopathological characteristics correlation analysis, and the receiver operating characteristic (ROC) curve. Furthermore, correlations between the signature risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed. Results: In the present study, a 12-NRLs signature comprising REPIN1-AS1, UBL7-AS1, LINC00460, LINC02773, CHROMR, LINC01094, FLNB-AS1, ITFG1-AS1, LASTR, PINK1-AS, LINC01638, and PVT1 was developed to improve the prognosis prediction of STAD patients. Unsupervised methods, including principal component analysis and t-distributed stochastic neighbor embedding, confirmed the capability of the present signature to separate samples with RS. Kaplan-Meier and ROC curves revealed that the signature had an acceptable predictive potency in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 12-NRLs signature were risk factors independent of various clinical parameters. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and half-inhibitory concentration differed significantly among different risk subtypes, which implied that the signature could assess the clinical efficacy of chemotherapy and immunotherapy. Conclusion: This 12-NRLs risk signature may help assess the prognosis and molecular features of patients with STAD and improve treatment modalities, thus can be further applied clinically.

RNF114 Silencing Inhibits the Proliferation and Metastasis of Gastric Cancer.

RNF114 (E3 ubiquitin ligase RING finger protein 114) was first identified as a zinc-binding protein that promotes psoriasis development; however, its role in gastric cancer is still unclear. We explored the relationship between RNF114 and gastric cancer using bioinformatics and molecular biology techniques. The results showed that RNF114 was highly expressed in gastric cancer and negatively correlated with the patient's prognosis. Functional assays suggested that RNF114 silencing suppressed the proliferation and metastasis of gastric cancer cells to a certain extent. Further studies showed that RNF114 expression was potentially targeted by miR-218-5p and methylation modification, and mediated downstream EGR1 (early growth response 1) degradation by the ubiquitylation approach. Together, the present results highlight the detrimental effects of RNF114 overexpression in gastric cancer and contribute to a better understanding of the mechanisms underlying RNF114 functionality.

Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace.

Objective: This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis. Methods: The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field. Results: A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC. Conclusions: Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

Identification of Ferroptosis-Related Long Noncoding RNA and Construction of a Novel Prognostic Signature for Gastric Cancer.

BACKGROUND: Gastric cancer is the most common malignant tumor of the digestive system. It has a poor prognosis and is clinically challenging to treat. Ferroptosis is a newly defined mode of programmed cell death. The roles and prognostic value of ferroptosis-related long noncoding RNAs (lncRNAs) in gastric cancer remain unknown. RESULTS: In the current study, 20 ferroptosis-related lncRNAs were identified via univariate Cox analysis, least absolute shrinkage, and selection operator Cox regression analysis and used to construct a prognostic signature and classify gastric cancer patients into high-risk and low-risk groups. The signature was validated using TCGA training and testing cohorts. The risk signature was an independent prognostic indicator of survival and accurately predicted the prognoses of patients with gastric cancer. It was also associated with immune cell infiltration. Gene set enrichment analysis was used to investigate underlying mechanisms that the 20 ferroptosis-related lncRNAs were involved in. Chemosensitivity and immune checkpoint inhibitor analyses indicated that high-risk patients were more sensitive to the immune checkpoint inhibitor programmed cell death protein 1. CONCLUSIONS: The important role of ferroptosis-related lncRNAs in immune infiltration identified in the current study may assist the determination of personalized prognoses and treatments in patients with gastric cancer. These 20 lncRNAs can be used as the diagnostic and prognostic markers for gastric cancer.

miR-365 inhibits the progression of gallbladder carcinoma and predicts the prognosis of Gallbladder carcinoma patients.

Gallbladder carcinoma (GBC) is one of the most common fatal biliary tract tumors in the world. Its 3-year survival rate is 30% and the recurrence rate remains very high. miR-365 was downregulated in numerous tumors and worked as tumor suppressor gene. However, the role of miR-365 in GBC was unclear. In this study, our results found that the expression of miR-365 in GBC tissues was reduced rather than that in non-cancerous tissues. miR-365 overexpression inhibited the proliferation, metastasis and expansion of GBC CSCs. Mechanically, bioinformatic and luciferase reporter analysis identified Ras-related C3 botulinum toxin substrate 1 (RAC1) as a direct target of miR-365. Overexpression of miR-365 in GBC cells reduced the RAC1 mRNA and protein expression. The special RAC1 inhibitor EHop-106 abolished the discrepancy of growth, metastasis and self-renewal ability between miR-365-overexpression GBC cells and their control cells, which further demonstrated that RAC1 was involved in miR-365-disrupted GBC cells growth, metastasis and self-renewal. More importantly, reduced expression of miR-365 was a predictor of poor prognosis of GBC patients. In conclusion, miR-365 inhibited GBC cell growth, metastasis and self-renewal capacity by directly targeting RAC1, and may therefore prove to be a novel prognosis biomarker for GBC patients.

Identification of Circular RNA-Based Immunomodulatory Networks in Colorectal Cancer.

BACKGROUND: Circular RNAs (circRNAs) have been recently proposed as hub molecules in various diseases, especially in tumours. We found that circRNAs derived from ribonuclease P RNA component H1 (RPPH1) were highly expressed in colorectal cancer (CRC) samples from Gene Expression Omnibus (GEO) datasets. OBJECTIVE: We sought to identify new circRNAs derived from RPPH1 and investigate their regulation of the competing endogenous RNA (ceRNA) and RNA binding protein (RBP) networks of CRC immune infiltration. METHODS: The circRNA expression profiles miRNA and mRNA data were extracted from the GEO and The Cancer Genome Atlas (TCGA) datasets, respectively. The differentially expressed (DE) RNAs were identified using R software and online server tools, and the circRNA-miRNA-mRNA and circRNA-protein networks were constructed using Cytoscape. The relationship between targeted genes and immune infiltration was identified using the GEPIA2 and TIMER2 online server tools. RESULTS: A ceRNA network, including eight circRNAs, five miRNAs, and six mRNAs, was revealed. Moreover, a circRNA-protein network, including eight circRNAs and 49 proteins, was established. The targeted genes, ENOX1, NCAM1, SAMD4A, and ZC3H10, are closely related to CRC tumour-infiltrating macrophages. CONCLUSIONS: We analysed the characteristics of circRNA from RPPH1 as competing for endogenous RNA binding miRNA or protein in CRC macrophage infiltration. The results point towards the development of a new diagnostic and therapeutic paradigm for CRC.

LCP1 is a prognostic biomarker correlated with immune infiltrates in gastric cancer.

BACKGROUND: Previous studies have identified LCP1 as a diagnostic and prognostic marker in several cancers. However, the role of LCP1 in gastric cancer (GC) and its effect on tumor immune infiltration remain unclear. OBJECTIVE: The aim was to explore the role of LCP1 in GC and its effect on tumor immune infiltration. METHODS: We explored the expression of LCP1 relative to clinicopathology in GC patients by bioinformatics analysis and immunohistochemistry. Using cBioportal database, we analyzed the characteristic genetic variations of LCP1 in GC. In addition, we evaluated the correlation between LCP1 expression and tumor-infiltrating lymphocytes (TILs) using R software, TIMER and TISIDB databases. Finally, we analyzed the biological functions in which LCP1 may participate and the signaling pathways it may regulate. RESULTS: Here, we showed that LCP1 expression is significantly correlated with tumor aggressiveness and poor prognosis in GC patients. Additionally, the results indicated that LCP1 was associated with TILs, including both immunosuppressive and immunosupportive cells, and was strongly correlated with various immune marker sets in GC. GSEA analysis demonstrated that LCP1 expression played an important role in lymphocyte formation and immune reaction. CONCLUSIONS: LCP1 may be a potential prognostic biomarker for GC patients and a marker for tumor immunotherapy.

Epigenetics mechanisms mediate the miR-125a/BRMS1 axis to regulate invasion and metastasis in gastric cancer.

PURPOSE: Altered expression of breast cancer metastasis suppressor 1 (BRMS1), is a tumor suppressor, which is found in many types of cancers, including gastric cancer (GC), but the mechanism by which BRMS1 inhibits invasion and metastasis in GC is unknown. The aim of the study was to investigate the molecular mechanisms of miR-125a/BRMS1 in GC. MATERIALS AND METHODS: The expression of BRMS1 and miR-125a were detected by quantitative real-time PCR (qRT-PCR) and analyzed by bioinformatics. BSP and MSP were used to detecte the methylation status of miR-125a and BRMS1 which was treated by 5-Aza or not. Western Blot and qRT-PCR were used to analyze the expression of BRMS1 and EZH2. Transwell was performed to explore the invasion and metastasis ability of GC cells. The nude mice were used for the tumor formation assay. RESULTS: BRMS1 may be regulated by copy number variation (CNV), methylation and miR-125a-5p. As one of the essential components of PRC2, EZH2 is an important regulatory factor resulting in the low expression of miR-125a. An epigenetic mechanism mediates the miR-125a/BRMS1 axis to inhibit the invasion and metastasis of GC cells. In vivo experiments, it is also showed that BRMS1 is involved in invasion and metastasis but not the proliferation in GC. CONCLUSION: These studies shed light on the mechanism of BRMS1 inhibition of GC invasion and metastasis and the development of new drugs targeting the miR-125a/BRMS1 axis, which will be a promising therapeutic strategy for GC and other human cancers.