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Background: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood‐based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. Methods: A three‐phase multicentre study was conducted to screen, optimise and validate HCC‐specific differentially methylated regions (DMRs) using next‐generation sequencing and quantitative methylation‐specific PCR (qMSP). Results: Genome‐wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre‐ and postoperative plasma samples from 103 HCC patients and correlated with 2‐year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p <.001). Conclusions: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC‐specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at‐risk populations. [ABSTRACT FROM AUTHOR]

Tertiary lymphoid structures (TLSs), referred to as tertiary lymphoid organs and lymphoid tissue neogenesis, are aggregates of immune cells that occur in nonlymphoid tissues. In recent years, it has been found that TLSs within the tumor microenvironment have been associated with local adaptive immune immunity against cancer and favorable prognosis in several human solid tumors, including gynecological cancers. The issue of the prognosis of gynecological cancers, including endometrial, cervical, and ovarian cancer, is an enormous challenge thatmany clinical doctors and researchers are now facing. Concerning the predictive prognostic role of TLSs, effective evaluation, and quantification of TLSs in human tissues may be used to assist gynecologists in assessing the clinical outcome of gynecological cancer patients. This review summarizes the current knowledge of TLSs in gynecological cancers, mainly focusing on the potential mechanism of TLS neogenesis, methods for evaluating TLSs, their prognostic value, and their role in antitumor immune immunity. This review also discusses the new therapeutic methods currently being explored in gynecological cancers to induce the formation of TLSs. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hepatocellular carcinoma (HCC) has a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A) modifications and microRNAs (miRNAs) play pivotal roles in tumorigenesis and development. However, the role of m6A-related miRNAs in HCC has not been clarified yet. This study aimed to identify the role of m6A-miRNAs in HCC prognosis through bioinformatics analysis. METHODS: The clinicopathological information and RNA sequencing data of 369 HCC tumor tissues and 49 tumor-adjacent tissues were downloaded from the TCGA database. A total of 23 m6A regulators were extracted to evaluated the m6A-related miRNAs using Pearson's correlation analysis. Then, we selected prognosis-related m6A-miRNAs using a univariate Cox regression model and used the consensus cluster analysis to explore the characteristics of the m6A-miRNAs. The coefficient of the least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct a prognostic risk score model. The receiver operated characteristic (ROC) analysis was applied to evaluate the prognostic value of the signature. The biological functions of targeted genes were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, to validate the potential predictive value for prognosis, the miRNA expression profiles from the GSE76903 and GSE6857 were used. Single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were applied to assess the immune microenvironment of HCC. Additionally, a meta-analysis was used to verify the prognostic value of the m6A-microRNAs. RT-PCR was applied to validated the expression of miRNAs in HCC tissues. Cell viability, transwell assay and RNA m6A dot blot assays of HCC cells was applied to access the function of miR-17-5p. RESULTS: The expression of 48 m6A-related miRNAs was identified and 17 prognostic m6A-miRNAs was discovered. The expression profile of those 17 miRNAs was divided into three clusters, and these clusters were associated with the tumor microenvironment (TME) and prognosis. The nine m6A-related miRNA signature was associated with the prognosis of HCC, the AUC of the ROC was 0.771(TCGA dataset), 0.788(GSE76903) and 0.646(GSE6857). The TME and the expression of immune checkpoint molecules were associated with the risk score. The meta-analysis also validated the prognostic value of the m6A-related miRNAs (miR182-5p (HR:1.58, 95%CI:1.04-2.40) and miR-17-5p (HR:1.58, 95%CI: 1.04–2.40)). The expression of miR-17-5p was upregulated in HCC tissues and miR-17-5p showed an oncogenic role in HCC cells. CONCLUSION: The clinical innovation is the use of m6A-miRNAs as biomarkers for predicting prognosis regarding immunotherapy response in HCC patients. [ABSTRACT FROM AUTHOR]

Background: The purpose was to assess the contribution of tumor size to the prognosis of patients with gastric cancer. Methods: Patient data were sourced from the Surveillance, Epidemiology, and End Results program (SEER) database. Cox proportional risk regression was performed to determine the prognostic role of tumor size. Kaplan-Meier curves were conducted to calculate survival curves. Consistency index (c-index) and subject exercise curve (ROC) were utilized to assess the predictive ability of each factor on the prognosis of gastric cancer. Results: Tumor size is preferable to other widely accepted prognostic clinical features in forecasting the survival of patients with gastric cancer. Conclusions: The discriminatory ability of tumor size at T1 stage is superior to many other clinical prognostic factors. [ABSTRACT FROM AUTHOR]

Background: The tumor microenvironment (TME) is crucial for tumor recurrence, prognosis, and therapeutic responses. We comprehensively investigated the TME characterization associated with relapse and survival outcomes of gastric cancer (GC) to predict chemotherapy and immunotherapy response. Methods: A total of 2,456 GC patients with complete gene-expression data and clinical annotations from twelve cohorts were included. The TME characteristics were evaluated using three proposed computational algorithms. We then developed a TME-classifier, a TME-cluster, and a TME-based risk score for the assessment of tumor recurrence and prognosis in patients with GC to predict chemotherapy and immunotherapy response. Results: Patients with tumor recurrence presented with inactive immunogenicity, namely, high infiltration of tumor-associated stromal cells, low infiltration of tumor-associated immunoactivated lymphocytes, high stromal score, and low immune score. The TME-classifier of 4 subtypes with distinct clinicopathology, genomic, and molecular characteristics was significantly associated with tumor recurrence (P = 0.002), disease-free survival (DFS, P <0.001), and overall survival (OS, P <0.001) adjusted by confounding variables in 1,193 stage I–III GC patients who underwent potential radical surgery. The TME cluster and TME-based risk score can also predict DFS (P <0.001) and OS (P <0.001). More importantly, we found that patients in the TMEclassifier-A, TMEclassifier-C, and TMEclassifier-D groups benefited from adjuvant chemotherapy, and patients in the TMEclassifier-B group without chemotherapy benefit responded best to pembrolizumab treatment (PD-1 inhibitor), followed by patients in the TMEclassifier-A, while patients in the C and D groups of the TMEclassifier responded poorly to immunotherapy. Conclusion: We determined that TME characterization is significantly associated with tumor recurrence and prognosis. The TME-classifier we proposed can guide individualized chemotherapy and immunotherapy decision-making. [ABSTRACT FROM AUTHOR]

Background: Since interferon regulatory factor (IRF) family functions in immune response to viral infection, its role in colorectal cancer (CRC) has not been inspected before. This study tries to investigate members of IRF family using bioinformatics approaches in aspect of differential expressions, biological function, tumor immune infiltration and clinical prognostic value for patients with CRC.Methods: Transcriptome profiles data, somatic mutations and clinical information of CRC were obtained from COAD/READ dataset of The Cancer Genome Atlas (TCGA) as a training set. Gene expression data (GSE17536 and GSE39582) were downloaded from the Gene Expression Omnibus as a validating set. A random forest algorithm was used to score the risk for every case. Analyzing gene and function enrichment, constructing protein-protein interaction and noncoding RNA network, identifying hub-gene, characterizing tumor immune infiltration, evaluating differences in tumor mutational burden (TMB) and sensitivity to chemotherapeutics or immunotherapy were performed by a series of online tools and R packages. Immunohistochemical (IHC) examinations were carried out validation in tissue samples.Results: Principal-component analysis (PCA) suggested that the transcript expression levels of nine members of IRF family differed between normal colorectum and CRC. The risk score constructed by IRF family not only acted as an independent factor for predicting survival in CRC patients with different biological processes, signaling pathways and TMB, but also indicated different immunotherapy response with diverse immune and stromal cells infiltration. IRF3 and IRF7 were upregulated in CRC and suggested a shorter survival time in patients with CRC. Differentially expressed members of IRF family exhibited varying degrees of immune cell infiltration. IHC analysis showed a positive association between IRF3 and IRF7 expression and tumor-infiltrating immune cells, including CD4+ T cell and CD68+ macrophages.Conclusions: On account of differential expression, IRF family members can help to predict both response to immunotherapy and clinical prognosis of patients with CRC. Our bioinformatic investigation not only gives a preliminary picture of the genetic features as well as tumor microenvironment, but it may provide a clue for further experimental exploration and verification on IRF family members in CRC. [ABSTRACT FROM AUTHOR]