Whole transcriptome sequencing indicated the Anti-tumor immunity of NLRP3 in breast cancer.

Breast cancer (BC) is a prevalent cancer of the female reproductive system and a major contributor to cancer-related mortality. The activation of NLRP3, a key inflammasome, has been extensively associated with tumor-related molecular and cellular processes; however, the regulatory mechanisms and specific role of NLRP3 in breast cancer remain incompletely elucidated. This study aimed to evaluate the molecular mechanisms of NLRP3-related genes in BC. Utilizing bioinformatics methods, the present research analyzed the TCGA-BRCA dataset, which included four groups of transcriptome sequencing data as follows, normal (WT), NLRP3 knockout (KO), non-knockout-BRCA (BC-WT), and NLRP3-knockout-BRCA (BC-KO). Results indicated that NLRP3 was significantly down-regulated in TCGA-BRCA. Key module genes were mainly enriched in leukocyte cell-cell adhesion and cytokine-cytokine receptor interaction. Moreover, correlation analysis showed that NLRP3 was positively associated with cancer-associated fibroblasts and negatively associated with CD4+ Th1 T-cells. In addition, the DEGs1 and DEGs2 overlapping indicated 505 feature genes, with Chac1 (negative) and Ugt8a (positive) had the strongest correlation with differential immune cells (class-switched memory B cells). Pathway intersection revealed 13 co-KEGG pathways. The BC-KO group indicated markedly reduced levels of four genes (Ccl19, Ccl20, Ccl21a, and H2-Oa) and increased levels of two genes (Il2ra and H2-Ob). This study delved into the role of NLRP3 in BC, exploring its regulatory mechanisms and the impact gene knockout. Bioinformatics approaches identified NLRP3-associated genes, their enriched pathways, and interactions within the tumor microenvironment (TME), providing novel insights into NLRP3 function, TME dynamics, and potential targets for BC prevention and treatment.

STRN3 promotes tumour growth in hepatocellular carcinoma by inhibiting the hippo pathway.

HCC is a globally high-incidence malignant tumour, and its pathogenesis is still unclear. Recently, STRN3 has been found to be elevated in various tumours, but its expression and biological functions in HCC have not been studied. In the study, clinical correlation analysis was performed on 371 liver cancer patients from TCGA database and liver cancer tissues and normal tissues from the GEO database. qRT-PCR and western blotting were used to detect relevant proteins in cells, and CCK8 and colony formation experiments were performed to analyse cell proliferation ability. Transwell and wound healing experiments were performed to detect cell invasion ability, and flow cytometry was used to detect cell apoptosis. Single-cell sequencing data and multiple immunofluorescence were analysed for the expression abundance and distribution of certain proteins. Immunohistochemistry was used to assess the expression of STRN3 in patients' tumour and adjacent non-cancerous tissues. The results indicated STRN3 was highly expressed in liver tumour tissues and was closely associated with poor prognosis. Knockdown of STRN3 could significantly inhibit cell proliferation and migration ability. At the same time, we found that STRN3 could inhibit the Hippo pathway and promote the entry of YAP protein into the nucleus. Our study first found that STRN3 could promote tumour growth by inhibiting the Hippo pathway. The study of STRN3 can promote the understanding and treatment of the occurrence and development of HCC.

Exploring serine-arginine rich splicing factors: potential predictive markers for dysregulation in oral cancer.

BACKGROUND: Dysregulated splicing events are a common phenomenon in cancer with the Serine-arginine-rich splicing factor (SRSF) family emerging as pivotal regulators of gene expression, exerting influence over constitutive and alternative splicing processes. Although aberrations in a few SRSF family members have been implicated in various cancers, the comprehensive roles of other family constituents remain underexplored. METHODS: This study delves into the expression profile of the entire SRSF family (SRSF1-SRSF12) in 23 cancerous cell lines originating from diverse tissues using quantitative Real-Time PCR. Further, the transcript levels of the SRSF family were examined in oral cancer patient samples stratified into Pre-cancer (n = 15), Early cancer (n = 11), Late cancer (n = 14), and adjacent non-tumor tissues (n = 26) as controls. The results were corroborated by a parallel investigation utilizing the transcriptomics data of oral squamous cell carcinoma (OSCC) patients (n = 319) and controls (n = 35) available in The Cancer Genome Atlas (TCGA) database. RESULTS: Our investigation reveals a notable upregulation in the expression levels of key splicing factors, namely SRSF3, SRSF9, and SRSF10 in all oral cancer cell lines (SCC-4, UM-SCC-84, CAL33, SAS-H1). Conversely, no significant associations between SRSF family members and other cancer cell lines were discerned. Further, the expression profile of the SRSF family in oral cancer patient samples revealed significant upregulation of SRSF1, SRSF3, SRSF7, SRSF9, SRSF10, and SRSF11 in patients with late-stage oral cancer compared to controls. Transcriptomics data from TCGA database demonstrated remarkable upregulation of SRSF1, SRSF4, SRSF9, SRSF10, and SRSF11 in OSCC patients. CONCLUSION: Collectively our results underscore the critical involvement of SRSF family members in the context of oral cancer, highlighting their potential as key players in the altered splicing dynamics associated with cancer progression.

A prognostic model based on tumor microenvironment and immune cell in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the second leading cause of cancer-related death. Immunotherapy is one of the new options for cancer treatment. This study aimed to develop an immune-related signature associated with CRC. METHODS: We performed differential analysis to screen out the differentially expressed genes (DEGs) of The Cancer Genome Atlas-Colorectal Cancer (TCGA-CRC) datasets. Weighted gene co-expression network analysis (WGCNA) was performed to obtain the key module genes associated with differential immune cells. The candidate genes were obtained through overlapping key DEGs and key module genes. The univariate and multivariate Cox regression analyses were adopted to build a CRC prognostic signature. We further conducted immune feature estimation and chemotherapy analysis between two risk subgroups. Finally, we verified the expression of immune-related prognostic genes at the transcriptional level. RESULTS: A total of 61 candidate genes were obtained by overlapping key DEGs and key module genes associated with differential immune cells. Then, an immune-related prognostic signature was built based on the three prognostic genes (HAMP, ADAM8, and CD1B). The independent prognostic analysis suggested that age, stage, and RiskScore could be used as independent prognostic factors. Further, we found significantly higher expression of three prognostic genes in the CRC group compared with the normal group. Finally, real-time polymerase chain reaction verified the expression of three genes in patients with CRC. CONCLUSION: The prognostic signature comprising HAMP, ADAM8, and CD1B based on immune cells was established, providing a theoretical basis and reference value for the research of CRC.

Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes.

BACKGROUND: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. METHODS: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. RESULTS: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. CONCLUSION: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

[Expression and prognostic significance of KAP1 gene in malignant pleural mesothelioma].

Objective: To explore the expression of KAP1 (KRAB-associated protein 1, KAP1) in Malignant pleural mesothelioma (MPM) based on the cancer genome atlas (TCGA) and clinical trials. And elucidate the correlation between the expression of KAP1 and the clinical pathological parameters of patients with MPM and its prognosis. Methods: In April 2022, Based on the second generation KAP1mRNA sequencing data and clinicopathological data of MPM patients downloaded from TCGA database, the correlation between KAP1mRNA expression and clinical parameters was analyzed, and the correlation between KAP1 protein expression and clinicopathological parameters and its prognostic value were analyzed based on Chuxiong data set cohort clinical samples. The expression of KAP1 mRNA in MPM samples and matched normal tumor adjacent tissues was detected by qRT-PCR, and the expression of KAP1 protein in MPM and normal pleural tissues was detected by immunohistochemistry and Westernblotting. To construct a Kaplan-Meier model to explore the effect of KAP1 expression on the prognosis of MPM patients, and to analyze the prognostic factors of MPM patients by Cox regression. Results: qRT-PCR and Western blotting detection showed that the expression levels of KAP1 gene in four different MPM cells (NCI-H28, NCI-H2052, NCI-H2452, and MTSO-211H) were significantly higher than those in normal pleural mesothelial cells Met-5A. qRT-PCR, Western blotting and IHC results demonstrated that the mRNA and protein expression levels of KAP1 in MPM tissues was significantly higher than that in matching normal mesothelial tissues, and the expression level of KAP1 protein was correlated with TP 53 protein expression levels and serum CEA levels (P<0.05) . The mRNA expression level was significantly correlated with the prognosis, The overall survival time of mesothelioma patients with high KAP1mRNA expression was significantly shorter (HR=3.7, Logrank P<0.001) . Tumor type, age and the mRNA expression were related to the prognosis of MPM patients (P<0.05) . Multivariate analysis showed that tumor type and KAP1 mRNA expression level were independent prognostic factors of MPM patients (P<0.05) . Conclusion: In this study, TCGA database and Chuxiong cohort experiment samples were used to collect the relevant information of KAP1 expression in malignant melanoma tissues. It was confirmed that KAP1 is highly expressed in MPM tissues. The mRNA expression level and pathological type are correlated with the prognosis of patients.

SOCS1 as a Biomarker Candidate for HPV Infection and Prognosis of Head and Neck Squamous Cell Carcinomas.

The pathogenesis of head and neck squamous cell carcinoma (HNSCC) is associated with human papillomavirus (HPV) infection. However, the molecular mechanisms underlying the interactions between HNSCC and HPV remain unclear. Bioinformatics was used to analyze the gene expression dataset of HPV-associated HNSCC based on the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) in HPV-positive and HPV-negative HNSCC were screened. Gene function enrichment, protein-protein interactions (PPI), survival analysis, and immune cell infiltration of DEGs were performed. Furthermore, the clinical data of HNSCC tissue samples were analyzed using immunohistochemistry. In total, 194 DEGs were identified. A PPI network was constructed and 10 hub genes (EREG, PLCG1, ERBB4, HBEGF, ZFP42, CBX6, NFKBIA, SOCS1, ATP2B2, and CEND1) were identified. Survival analysis indicated that low expression of SOCS1 was associated with worse overall survival. Immunohistochemistry demonstrated that SOCS1 expression was higher in HPV-negative HNSCC than in HPV-positive HNSCC, and there was a positive correlation between SOCS1 expression and patient survival. This study provides new information on biological targets that may be relevant to the molecular mechanisms underpinning the occurrence and development of HNSCC. SOCS1 may play an important role in the interaction between HPV and HNSCC and serve as a potential biomarker for future therapeutic targets.

Transcription factor LHX9 (LIM Homeobox 9) enhances pyruvate kinase PKM2 activity to induce glycolytic metabolic reprogramming in cancer stem cells, promoting gastric cancer progression.

BACKGROUND: Glycolytic metabolic reprogramming is a phenomenon in which cells undergo altered metabolic patterns during malignant transformation, mainly involving various aspects of glycolysis, electron transport chain, oxidative phosphorylation, and pentose phosphate pathway. This reprogramming phenomenon can be used as one of the markers of tumorigenesis and development. Pyruvate kinase is the third rate-limiting enzyme in the sugar metabolism process by specifically catalyzing the irreversible conversion of PEP to pyruvate. PURPOSE: This study aimed to reveal the critical mediator(s) that regulate glycolytic metabolism reprogramming in gastric cancer and their underlying molecular mechanism and then explore the molecular mechanisms by which LHX9 may be involved in regulating gastric cancer (GC) progression. METHODS: Firstly, we downloaded the GC and glycolysis-related microarray datasets from TCGA and MSigDB databases and took the intersection to screen out the transcription factor LHX9 that regulates GC glycolytic metabolic reprogramming. Software packages were used for differential analysis, single gene predictive analysis, and Venn diagram. In addition, an enrichment analysis of the glycolytic pathway was performed. Immunohistochemical staining was performed for LHX9 and PKM2 protein expression in 90 GC patients, and the association between their expressions was evaluated by Spearman's correlation coefficient method. Three human GC cell lines (AGS, NCI-N87, HGC-27) were selected for in vitro experimental validation. Flow cytometry was utilized to determine the stem cell marker CD44 expression status in GCSCs. A sphere formation assay was performed to evaluate the sphere-forming capabilities of GCSCs. In addition, RT-qPCR and Western blot experiments were employed to investigate the tumor stem cell markers OCT4 and SOX2 expression levels in GCSCs. Furthermore, a lentiviral expression vector was constructed to assess the impact of downregulating LHX9 or PKM2 on the glycolytic metabolic reprogramming of GCSCs. The proliferation, migration, and invasion of GCSCs were then detected by CCK-8, EdU, and Transwell assays. Subsequently, the mutual binding of LHX9 and PKM2 was verified using chromatin immunoprecipitation and dual luciferase reporter genes. In vivo experiments were verified by establishing a subcutaneous transplantation tumor model in nude mice, observing the size and volume of tumors in vivo in nude mice, and obtaining fresh tissues for subsequent experiments. RESULTS: Bioinformatics analysis revealed that LHX9 might be involved in the occurrence and development of GC through regulating glycolytic metabolism. High LHX9 expression could be used as a reference marker for prognosis prediction of GC patients. Clinical tissue assays revealed that LHX9 and PKM2 were highly expressed in GC tissues. Meanwhile, GC tissues also highly expressed glycolysis-associated protein GLUT1 and tumor cell stemness marker CD44. In vitro cellular assays showed that LHX9 could enhance its activity and induce glycolytic metabolic reprogramming in GCSCs through direct binding to PKM2. In addition, the knockdown of LHX9 inhibited PKM2 activity and glycolytic metabolic reprogramming and suppressed the proliferation, migration, and invasive ability of GCSCs. In vivo animal experiments further confirmed that the knockdown of LHX9 could reduce the tumorigenic ability of GCSCs in nude mice by inhibiting PKM2 activity and glycolytic metabolic reprogramming. CONCLUSION: The findings suggest that both LHX9 and PKM2 are highly expressed in GCs, and LHX9 may induce the reprogramming of glycolytic metabolism through transcriptional activation of PKM2, enhancing the malignant biological properties of GCSCs and ultimately promoting GC progression.

Construction of iron metabolism-related prognostic features of gastric cancer based on RNA sequencing and TCGA database.

BACKGROUND: Researches have manifested that the disorder of iron metabolism is participated in Gastric cancer (GC), but whether iron metabolism-relevant genes (IMRGs) is related to the survival outcome of GC remain unknown. METHODS: Eleven tumor as well as nine adjacent normal tissues from GC patients were underwent mRNA sequencing, and the The Cancer Genome Atlas Stomach Cancer (TCGA-STAD) datasets were acquired from the TCGA database. Cox analyses and least absolute shrinkage and selection operator (LASSO) regression were applied to build a IMRGs signature. The relationship between signature genes and the infiltration profiling of 24 immune cells were investigated using single-sample GSEA (ssGSEA). Meanwhile, the potential biological significance, genes that act synergistically with signature genes, and the upstream regulatory targets were predicted. Finally, the abundance of the signature genes were measured via the quantitative real-time PCR (qRT-PCR). RESULTS: A IMRGs signature was constructed according to the expression and corresponding coefficient of DOHH, P4HA3 and MMP1 (The Schoenfeld individual test showed risk score was not significant with P values = 0.83). The prognostic outcome of patients in the high-risk group was terrible (p < 0.05). Receiver operating characteristic (ROC) curves confirmed that the IMRGs signature presented good efficiency for predicting GC prognosis (AUC > 0.6). The nomogram was performed well for clinical utilize (C-index = 0.60), and the MMP1 expression significantly increased in the cohorts at age > 60 and Stage II-IV (p < 0.05). The positive correlation of P4HA3 and MMP1 expression as well as the negative correlation of DOHH expression with risk score (p < 0.0001) and worse prognosis (p < 0.05) were detected as well. Furthermore, 11 differential immune cells were associated with these signature genes (most p < 0.01). Finally, qRT-PCR revealed that the abundance of DOHH, P4HA3 and MMP1 were high in tumor cases, indicating the complex mechanism between the high expression of DOHH as a protective factor and the high expression of P4HA3 and MMP1 as the risk factors in the development of GC. CONCLUSION: An iron metabolism-related signature was constructed and has significant values for foretelling the OS of GC.

Expression analysis, clinical significance and potential function of PLXNB2 in acute myeloid leukaemia.

BACKGROUND: The overall survival (OS) rate of adult patients suffering from acute myeloid leukaemia (AML) remains unsatisfactory at less than 40%. Current risk stratification systems fail to provide accurate guidelines for precise treatment. Novel biomarkers for predicting prognosis are urgently needed. Plexin B2 (PLXNB2), a functional receptor of angiogenin (ANG), has been found to be aberrantly expressed in multitudinous tumours. We detected overexpression of PLXNB2 mRNA in AML via transcriptome microarray analysis. This study aims to explore the potential role of PLXNB2 as a biomarker of prognosis and a prospective target of AML. METHODS: qRT‒PCR was conducted to verify the expression of PLXNB2 mRNA in bone marrow mononuclear cells from AML patients. Immunohistochemical and immunofluorescence staining were performed and confirmed increased expression of PLXNB2 protein in AML bone marrow tissues. Data on PLXNB2 expression, prognosis and clinical features were accessed from multiple bioinformatic databases, including The Cancer Genome Atlas (TCGA). Genes coexpressed and correlated with PLXNB2 were identified and analysed in the TCGA AML cohort. Metascape was applied for functional and pathway enrichment analysis of genes related to PLXNB2. Small molecular agents and traditional Chinese medicines potentially targeting genes related to PLXNB2 were screened via the Connectivity Map, TCMSP and HIT databases. RESULTS: PLXNB2 mRNA and protein levels are higher in AML samples than in normal controls. Overexpression of PLXNB2 is associated with worse OS in AML. Patients with high PLXNB2 expression might benefit more from haematopoietic stem cell transplantation (HSCT) (indicated by prolonged OS) than those with only chemotherapy treatment. Differentially expressed genes between the high and low PLXNB2 expression groups were overlapped with PLXNB2-coexpressed genes, and genes that overlapped were enriched in immune functions, endothelial cell regulation and cell interaction gene sets, indicating the potential function of PLXNB2 in AML. A total of 36 hub genes were identified from the differentially expressed genes, and MRC1, IL10, CD163 and CCL22 had significant prognostic value for AML. Analysis of the connectivity map and traditional agents revealed that honokiol, morphines, triptolide and paeoniflorin could be potential treatment regimens. CONCLUSIONS: The overexpression of PLXNB2 is an adverse prognostic factor in adult AML patients and could be used as a potential biomarker. PLXNB2 might exert an oncogenic role by modulating immune functions, endothelial cell functions and cell interactions. AML patients with high PLXNB2 expression could benefit more from HSCT.

Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning.

OBJECTIVES: To explore the molecular mechanisms underlying aggressive progression of papillary thyroid microcarcinoma and identify potential biomarkers. METHODS: Samples were collected and sequenced using tandem mass tag-labeled liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were identified and further analyzed using Mfuzz and protein-protein interaction analysis (PPI). Parallel reaction monitoring (PRM) and immunohistochemistry (IHC) were performed to validate the DEPs. RESULTS: Five thousand, two hundred and three DEPs were identified and quantified from the tumor/normal comparison group or the N1/N0 comparison group. Mfuzz analysis showed that clusters of DEPs were enriched according to progressive status, followed by normal tissue, tumors without lymphatic metastases, and tumors with lymphatic metastases. Analysis of PPI revealed that DEPs interacted with and were enriched in the following metabolic pathways: apoptosis, tricarboxylic acid cycle, PI3K-Akt pathway, cholesterol metabolism, pyruvate metabolism, and thyroid hormone synthesis. In addition, 18 of the 20 target proteins were successfully validated with PRM and IHC in another 20 paired validation samples. Based on machine learning, the five proteins that showed the best performance in discriminating between tumor and normal nodules were PDLIM4, ANXA1, PKM, NPC2, and LMNA. FN1 performed well in discriminating between patients with lymph node metastases (N1) and N0 with an AUC of 0.690. Finally, five validated DEPs showed a potential prognostic role after examining The Cancer Genome Atlas database: FN1, IDH2, VDAC1, FABP4, and TG. Accordingly, a nomogram was constructed whose concordance index was 0.685 (confidence interval: 0.645-0.726). CONCLUSIONS: PDLIM4, ANXA1, PKM, NPC2, LMNA, and FN1 are potential diagnostic biomarkers. The five-protein nomogram could be a prognostic biomarker.

Epstein-Barr Virus Infection Is Associated with Elevated Hepcidin Levels.

EBV and Helicobacter pylori (H. pylori) cause highly prevalent persistent infections as early as in childhood. Both pathogens are associated with gastric carcinogenesis. H. pylori interferes with iron metabolism, enhancing the synthesis of acute-phase proteins hepcidin, C-reactive protein (CRP), and α-1 glycoprotein (AGP), but we do not know whether EBV does the same. In this study, we correlated the EBV antibody levels and the serum levels of hepcidin, CRP, and AGP in 145 children from boarding schools in Mexico City. We found that children IgG positive to EBV antigens (VCA, EBNA1, and EA) presented hepcidin, AGP, and CRP levels higher than uninfected children. Hepcidin and AGP remained high in children solely infected with EBV, while CRP was only significantly high in coinfected children. We observed positive correlations between hepcidin and EBV IgG antibodies (p < 0.5). Using the TCGA gastric cancer database, we also observed an association between EBV and hepcidin upregulation. The TCGA database also allowed us to analyze the two important pathways controlling hepcidin expression, BMP−SMAD and IL-1ß/IL-6. We observed only the IL-1ß/IL-6-dependent inflammatory pathway being significantly associated with EBV infection. We showed here for the first time an association between EBV and enhanced levels of hepcidin. Further studies should consider EBV when evaluating iron metabolism and anemia, and whether in the long run this is an important mechanism of undernourishment and EBV gastric carcinogenesis.

An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease. METHODS: We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort. RESULTS: The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set. CONCLUSIONS: Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma's OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles.

[Establishment of a prostate cancer prognostic risk model based on the TCGA database and inflammation-related genes].

OBJECTIVE: To investigate the effect of inflammation-related genes on the prognosis of prostate cancer (PCa). METHODS: We downloaded PCa-related clinical data and mRNA sequencing data from the database Cancer Genome Atlas (TCGA) and inflammation-related pathway gene sets from MsigDB. Using univariate regression and LASSO regression analyses, we screened inflammation-related genes for the construction of a prognostic risk model and evaluated the performance of the model in predicting the prognosis of PCa by Kaplan-Meier and ROC analyses. Based on the nomogram, we calculated the risk scores of the patients, divided them into a high-risk and a low-risk group based on the median values of their risk scores, identified differentially expressed genes for enrichment analysis and verified the expression level of SPHK1 in the PCa tissue microarrays by immunohistochemical staining. RESULTS: Totally 19 inflammation-related genes were identified from 172 candidate genes for the construction of the prognostic risk model, including the risk genes CD14, PIK3R5, GABBR1, RELA, IRF7, SCARF1, MSR1, SPHK1, OSM and STAB1, and the protective genes AQP9, LPAR1, ATP2C1, NDP, CXCL6, P2RY2, DCBLD2, PCDH7, and IFNAR1. Kaplan-Meier analysis showed that the patients with high risk scores had a significantly lower recurrence-free survival and a worse prognosis than those with low risk scores. Differentially expressed genes were involved mainly in the activation of inflammatory response pathways. Immunohistochemical results indicated that the expression of SPHK1 was significantly higher in the tumorous than in the normal tissue and increased with the Gleason score. There was a correlation between the SPHK1 expression and envelope invasion. CONCLUSION: The prognostic risk model of inflammation-related genes constructed based on the TCGA database can effectively predict the prognosis of PCa.

[Expression and clinical significance of COL1A1 and COL1A2 genes in malignant pleural mesothelioma tissues].

Objective: To investigate the expression levels and clinical significance of collagen typeⅠ α1 chain (COL1A1) and collagen type Ⅰ α2 chain (COL1A2) in malignant pleural mesothelioma (MPM) tissues. Methods: In January 2020, MPM tissues and adjacent normal pleural tissues were collected from 26 MPM patients, and the expression levels of COL1A1 and COL1A2 genes in the tissues were determined by quantitative reverse transcription PCR, and the efficacy of both levels in diagnosing MPM was assessed using receiver operating characteristic (ROC) curves. The relationship between COL1A1 and COL1A2 gene expression and clinicopathological features was analyzed by the Cancer Genome Atlas (TCGA) database, and the relationship between the expression levels of both and overall survival (OS) and disease-free progression survival (DFS) of MPM patients was dynamically analyzed by gene expression profiling, and the factors affecting the prognosis of MPM patients were explored by Cox proportional risk regression model. The TIMER 2.0 platform was used to explore the relationship between COL1A1 and COL1A2 gene expression in MPM and tumor immune infiltrative cells. Results: Compared with normal pleural tissues, the expression of COL1A1 and COL1A2 genes was significantly increased in MPM tissues (P<0.01) , and their expression was positively correlated (P<0.001) . The ROC curves showed that the area under the curve for COL1A1 and COL1A2 expression levels diagnostic of MPM was 0.900 and 0.897, respectively. The expression of COL1A1 gene was correlated with tumor type in MPM patients (P<0.05) , and COL1A2 gene expression was correlated with T stage in MPM patients (P<0.05) . Both COL1A1 and COL1A2 gene expression were associated with OS in MPM patients (Logrank P<0.05) , but there was no significant correlation with DFS (Logrank P>0.05) . Cox multivariate analysis showed that patients with high COL1A1 and COL1A2 gene expression and biphasic mixed MPM had a higher risk of death (P<0.05) . TIMER 2.0 platform analysis showed that COL1A1 and COL1A2 gene expression in MPM patients was positively correlated with macrophages, COL1A2 gene expression in MPM was negatively correlated with neutrophils (P<0.05) . Conclusion: High expression of COL1A1 and COL1A2 genes in MPM tissues is valuable for diagnosis, disease prediction and prognostic assessment of MPM, and both may jointly contribute to the development of MPM.

Development of a prognostic signature of patients with esophagus adenocarcinoma by using immune-related genes.

BACKGROUND: Esophageal adenocarcinoma (EAC) is an aggressive malignancy with a poor prognosis. The immune-related genes (IRGs) are crucial to immunocytes tumor infiltration. This study aimed to construct a IRG-related prediction signature in EAC. METHODS: The related data of EAC patients and IRGs were obtained from the TCGA and ImmPort database, respectively. The cox regression analysis constructed the prediction signature and explored the transcription factors regulatory network through the Cistrome database. TIMER database and CIBERSORT analytical tool were utilized to explore the immunocytes infiltration analysis. RESULTS: The prediction signature with 12 IRGs (ADRM1, CXCL1, SEMG1, CCL26, CCL24, AREG, IL23A, UCN2, FGFR4, IL17RB, TNFRSF11A, and TNFRSF21) was constructed. Overall survival (OS) curves indicate that the survival rate of the high-risk group is significantly shorter than the low-risk group (P = 7.26e-07), and the AUC of 1-, 3- and 5- year survival prediction rates is 0.871, 0.924, and 0.961, respectively. Compared with traditional features, the ROC curve of the risk score in the EAC patients (0.967) is significant than T (0.57), N (0.738), M (0.568), and Stage (0.768). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are accurate by the combined analysis of the risk score, Sex, M stage, and Stage (The AUC of 1- and 3-years are 0.911, and 0.853). CONCLUSION: The 12 prognosis-related IRGs might be promising therapeutic targets for EAC.

A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics.

BACKGROUND: Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI. METHODS: In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients. RESULTS: Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients' HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10. CONCLUSION: We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI.

Oncological and prognostic impact of lymphovascular invasion in Colorectal Cancer patients.

Objectives: Lymphovascular invasion (LVI) is correlated with unfavorable prognoses in several types of cancers. We aimed to identify the informative features associated with LVI and to determine its prognostic value in colorectal cancer (CRC) patients. Methods: We retrospectively analyzed 1,474 CRC patients admitted in Wuhan Union Hospital between 2013 and 2017 as the development cohort and 549 CRC patients from The Cancer Genome Atlas (TCGA) database as the validation cohort. Logistical and Cox regression analyses were conducted to determine the oncological and prognostic significance of LVI in CRC patients. A survival nomogram based on LVI status was established using the Wuhan Union cohort and validated using TCGA cohort. Results: The LVI detection rates were 21.64% in the Wuhan Union cohort and 35.15% in TCGA cohort. LVI was closely correlated with advanced T stage, N stage, and TNM stage. LVI positivity was an independent biomarker for unfavorable overall survival (hazard ratio [HR]=2.25, 95% confidence interval [CI]=1.70-2.96, P<0.0001) and worse disease-free survival (HR=2.34, 95% CI=1.76-3.12, P<0.0001) in CRC patients. The survival nomogram incorporating LVI exhibited good predictive performance and reliability in the Wuhan Union cohort and TCGA cohort. Conclusion: LVI is a significant indicator of advanced stage and is remarkably correlated with worse prognosis in CRC patients. The survival nomogram incorporating LVI may assist clinicians to better strategize the therapeutic options for patients with CRC.

Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant primary cancer with high mortality. Previous studies have demonstrated that RNA binding proteins (RBPs) are involved in the biological processes of cancers, including hepatocellular cancer. METHODS: In this study, we aimed to identify the clinical value of RNA-binding proteins for hepatocellular carcinoma. We obtained gene expression and clinical data of hepatocellular carcinoma patients from the TCGA and ICGC databases. The prognostic value of RBP-related genes in patients with hepatocellular carcinoma and their function were studied by comprehensive bioinformatics analyses. The gene signature of SMG5, EZH2, FBLL1, ZNF239, and IGF2BP3 was generated by univariate and multivariate Cox regression and LASSO regression analyses. We built and verified a prognostic nomogram based on RBP-related genes. The gene signature was validated by the ICGC database. The expression of RBP-related genes was validated by the Oncomine database, the Human Protein Atlas and Kaplan-Meier plotter. RESULT: Most RBP-related genes were significantly different in cancer and normal tissues. The survival of patients in the different groups was significantly different. The gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. CONCLUSION: Gene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients.

A novel risk score system of immune genes associated with prognosis in endometrial cancer.

BACKGROUND: Endometrial cancer was the commonest gynecological malignancy in developed countries. Despite striking advances in multimodality management, however, for patients in advanced stage, targeted therapy still remained a challenge. Our study aimed to investigate new biomarkers for endometrial cancer and establish a novel risk score system of immune genes in endometrial cancer. METHODS: The clinicopathological characteristics and gene expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) of immune genes between tumors and normal tissues were identified. Protein-protein interaction (PPI) network of immune genes and transcriptional factors was integrated and visualized in Cytoscape. Univariate and multivariate analysis were employed for key genes to establish a new risk score system. Receiver operating characteristic (ROC) curve and survival analysis were performed to investigate the prognostic value of the model. Association between clinical characteristics and the model was analyzed by logistic regression. For validation, we identified 34 patients with endometrial cancer from Fudan University Shanghai Cancer Center (FUSCC). We detected 14-genes mRNA expression and calculated the risk scores of each patients and we performed survival analysis between the high-risk group and the low-risk group. RESULTS: 23 normal tissues and 552 tumor tissues were obtained from TCGA database. 410 immune-related DEGs was identified by difference analysis and correlation analysis. KEGG and GO analysis revealed these DEGs were enriched in cell adhesion, chemotaxis, MAPK pathways and PI3K-Akt signaling pathway, which might regulate tumor progression and migration. All genes were screened for risk model construction and 14 hub immune-related genes (HTR3E, CBLC, TNF, PSMC4, TRAV30, PDIA3, FGF8, PDGFRA, ESRRA, SBDS, CRHR1, LTA, NR2F1, TNFRSF18) were prognostic in endometrial cancer. The area under the curve (AUC) was 0.787 and the high-risk group estimated by the model possessed worse outcome (P < 0.001). Multivariate analysis suggested that the model was indeed an independent prognostic factor (high-risk vs. low-risk, HR = 1.14, P < 0.001). Meanwhile, the high-risk group was prone to have higher grade (P = 0.002) and advanced clinical stage (P = 0.018). In FUSCC validation set, the high-risk group had worse survival than the low-risk group (P < 0.001). CONCLUSIONS: In conclusion, the novel risk model of immune genes had some merits in predicting the prognosis of endometrial cancer and had strong correlation with clinical outcomes. Furthermore, it might provide new biomarkers for targeted therapy in endometrial cancer.