Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network

Prognostic value of lncRNA LINC01018 in prostate cancer by regulating miR-182-5p (The role of LINC01018 in prostate cancer)

LncRNAs are abnormally expressed in a variety of cancers and play unique roles in therapy. Based on this, the prognostic value of lncRNA LINC01018 in prostate cancer was discussed in this study. LINC01018 was underexpressed in prostate cancer tissues and cells, while miR-182-5p was elevated (***p < 0.001). Overexpression of LINC01018 may inhibit the progression of prostate cancer by targeting miR-182-5p. This study revealed that upregulated LINC01018 may prolong the overall survival of patients with prostate cancer (log-rank p = 0.042), and LINC01018 may become a prognostic biomarker for patients with prostate cancer, which brings a new direction for the treatment of patients.

Implications of obstructive sleep apnea in lung adenocarcinoma: A valuable omission in cancer prognosis and immunotherapy

Lung adenocarcinoma (LUAD) is a highly invasive malignant tumor with poor prognosis, and there is growing evidence that obstructive sleep apnea (OSA) could significantly promotes the risk of LUAD. In order to improve the treatment outcomes of patients with LUAD and OSA, we aim to screen OSA-related genes that may potentially affect LUAD and to discover a high sensitivity prognostic signature that can stratify LUAD/OSA patients and to further accurately identify LUAD patients who might respond to immunotherapy. Molecular subtypes classified by the prognostic signature did not belong to any previously reported subtypes of LUAD. The tumor microenvironment (TME), mutation, and so on, were significantly distinct between patients within different risk groups or clusters. Combined with gene set variation analysis (GSVA) and drug susceptibility analysis, patients in the low-risk group (The vast majority of patients belonging to cluster2 by molecular subtyping) were not suitable for immunotherapy due to T-cell exhaustion caused by long-term inflammatory response; the question of how to reverse T-cell exhaustion may be a primary consideration. Cluster3 patients had the highest benefit from immunotherapy, and although cluster1 patients had the worst prognosis, they were more sensitive to traditional chemotherapeutic drugs. Animal experiments showed that chronic intermittent hypoxia (CIH) could not only significantly promote the tumor growth of LUAD, but also increase the expression levels of risk genes. This risk model may contribute greatly to the evaluation of prognosis, molecular characteristics, and treatment modalities of LUAD/OSA, and could be further translated into clinical applications to ameliorate the treatment dilemmas.

Significance of Identifying Key Genes Involved in HBV-Related Hepatocellular Carcinoma for Primary Care Surveillance of Patients with Cirrhosis

Cirrhosis is frequently the final stage of disease preceding the development of hepatocellular carcinoma (HCC) and is one of the risk factors for HCC. Preventive surveillance for early HCC in patients with cirrhosis is advantageous for achieving early HCC prevention and diagnosis, thereby enhancing patient prognosis and reducing mortality. However, there is no highly sensitive diagnostic marker for the clinical surveillance of HCC in patients with cirrhosis, which significantly restricts its use in primary care for HCC. To increase the accuracy of illness diagnosis, the study of the effective and sensitive genetic biomarkers involved in HCC incidence is crucial. In this study, a set of 120 significantly differentially expressed genes (DEGs) was identified in the GSE121248 dataset. A protein-protein interaction (PPI) network was constructed among the DEGs, and Cytoscape was used to extract hub genes from the network. In TCGA database, the expression levels, correlation analysis, and predictive performance of hub genes were validated. In total, 15 hub genes showed increased expression, and their positive correlation ranged from 0.80 to 0.90, suggesting they may be involved in the same signaling pathway governing HBV-related HCC. The GSE10143, GSE25097, GSE54236, and GSE17548 datasets were used to investigate the expression pattern of these hub genes in the progression from cirrhosis to HCC. Using Cox regression analysis, a prediction model was then developed. The ROC curves, DCA, and calibration analysis demonstrated the superior disease prediction accuracy of this model. In addition, using proteomic analysis, we investigated whether these key hub genes interact with the HBV-encoded oncogene X protein (HBx), the oncogenic protein in HCC. We constructed stable HBx-expressing LO2-HBx and Huh-7-HBx cell lines. Co-immunoprecipitation coupled with mass spectrometry (Co-IP/MS) results demonstrated that CDK1, RRM2, ANLN, and HMMR interacted specifically with HBx in both cell models. Importantly, we investigated 15 potential key genes (CCNB1, CDK1, BUB1B, ECT2, RACGAP1, ANLN, PBK, TOP2A, ASPM, RRM2, NEK2, PRC1, SPP1, HMMR, and DTL) participating in the transformation process of HBV infection to HCC, of which 4 hub genes (CDK1, RRM2, ANLN, and HMMR) probably serve as potential oncogenic HBx downstream target molecules. All these findings of our study provided valuable research direction for the diagnostic gene detection of HBV-related HCC in primary care surveillance for HCC in patients with cirrhosis.

Combined identification of three lncRNAs in serum as effective diagnostic and prognostic biomarkers for hepatitis B virus-related hepatocellular carcinoma

Hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC) is a common, highly invasive malignant tumor associated with a high mortality rate. This study aimed to identify the effective diagnostic and prognostic biomarkers for HBV-related HCC. With HBV-related HCC RNA-sequencing data of The Cancer Genome Atlas (TCGA) database, 159 differentially expressed long non-coding RNAs (lncRNAs) between HBV-related HCC and para-carcinoma normal samples were identified, and 12 lncRNAs were eventually assessed for deeper research. Classification analysis developed a three-lncRNA signature of AC005332.5, ELF3-AS1, and LINC00665, which was demonstrated to be the most discriminatory with an AUC (Area Under the Curve) value of 0.913 (95% CI: 0.8610-0.9665) and verified in validation patients. The expression levels of AC005332.5, ELF3-AS1, and LINC00665 were significantly changed with different tumor stages or grades. Survival analysis revealed that AC005332.5, ELF3-AS1, and LINC00665 were highly associated with the prognosis of overall survival. Additionally, the lncRNA signature yielded statistical significance to predict clinical outcomes independently from other clinical variables in validation patients, as suggested in the multivariate Cox hazards analysis. Conclusively, a three-lncRNA signature of AC005332.5, ELF3-AS1, and LINC00665 may serve as an excellent diagnostic biomarker for HBV-related HCC and potential prognostic significance for HBV-related HCC sufferers. This article is protected by copyright. All rights reserved.

lncRNA–mRNA Expression Patterns in Invasive Pituitary Adenomas: A Microarray Analysis

Background. Long noncoding RNAs (lncRNAs) play important roles in the tumorigenesis and progression of various cancer types; however, their roles in the development of invasive pituitary adenomas (PAs) remain to be investigated. Methods. lncRNA microarray analysis was performed for three invasive and three noninvasive PAs. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed, and coexpression networks between lncRNA and mRNA were constructed. Furthermore, three differentially expressed lncRNAs were selected for validation in PA samples by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). The diagnostic values of these three lncRNAs were further evaluated by a receiver operating characteristic (ROC) curve analysis. Results. A total of 8872 lncRNAs were identified in invasive and paired noninvasive PAs via lncRNA microarray analysis. Among these, the differentially expressed lncRNAs included 81 that were upregulated and 165 that were downregulated. GO enrichment and KEGG pathway analysis showed that these differentially expressed lncRNAs were associated with the posttranslational modifications of proteins. Furthermore, we performed target gene prediction and coexpression analysis. The interrelationships between the significantly differentially expressed lncRNAs and mRNAs were identified. Additionally, three differentially expressed lncRNAs were selected for validation in 41 PA samples by qRT-PCR. The expression levels of FAM182B, LOC105371531, and LOC105375785 were significantly lower in the invasive PAs than in the noninvasive PAs ( $P<0.05$ ). These results were consistent with the microarray data. ROC curve analysis suggested that the expression levels of FAM182B and LOC105375785 could be used to distinguish invasive PAs from noninvasive PAs. Conclusion. Our findings demonstrated the expression patterns of lncRNAs in invasive PAs. FAM182B and LOC105375785 may be involved in the invasiveness of PAs and serve as new candidate biomarkers for the diagnosis of invasive PAs.

Defining Diffuse Large B-Cell Lymphoma Immunotypes by CD8+ T Cells and Natural Killer Cells

Background

There is a poor prognosis for diffuse large B-cell lymphoma (DLBCL), one of the most common types of non-Hodgkin lymphoma (NHL). Through gene expression profiles, this study intends to reveal potential subtypes among patients with DLBCL by evaluating their prognostic impact on immune cells.

Methods

Immune subtypes were developed based on CD8+ T cells and natural killer cells calculated from gene expression profiles. The comparison of prognoses and enriched pathways was made between immune subtypes. Following this validation step, samples from the independent data set were analyzed to determine the correlation between immune subtype and prognosis and immune checkpoint blockade (ICB) response. To provide a model to predict the DLBCL immune subtypes, machine learning methods were used. The virtual screening and molecular docking were adopted to identify small molecules to target the immune subtype biomarkers.

Results

A training data set containing 432 DLBCL samples from five data sets and a testing dataset containing 420 DLBCL samples from GSE10846 were used to develop and validate immune subtypes. There were two novel immune subtypes identified in this study: an inflamed subtype (IS) and a noninflamed subtype (NIS). When compared with NIS, IS was associated with higher levels of immune cells and a better prognosis for immunotherapy. Based on the random forest algorithm, a robust machine learning model has been established by 12 hub genes, and the area under the curve (AUC) value is 0.948. Three small molecules were selected to target NIS biomarkers, including VGF, RAD54L, and FKBP8.

Conclusion

This study assessed immune cells as prognostic factors in DLBCL, constructed an immune subtype that could be used to identify patients who would benefit from ICB, and constructed a model to predict the immune subtype.