Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma

Graphene therapy-related lncRNAs as prognostic and immune microenvironmental biomarkers in hepatocellular carcinoma

BACKGROUND

Graphene materials have the capacity to influence the tumor microenvironment and intracellular signaling responsiveness. However, the process of graphene-assisted liver cancer treatment still lacks specific biomarkers for assessing its efficacy.

METHODS

We identified graphene therapy-related lncRNAs (GTLncRNAs) through gene analysis and correlation tests. Multivariate COX and LASSO regression analyses yielded significant lncRNAs for a risk score model. We evaluated clinicopathological factors and tumor microenvironment using ssGSEA. We scrutinized the pathways of immune function, the evasion of tumor immunity, and the potential for immunotherapy. GTLncRNAs with differential expression were subjected to GO/KEGG analysis, and prospective chemotherapy drugs were discerned utilizing the pRRophetic algorithm. The prognostic model was authenticated through the examination of the Imvigor210 cohort, and an analysis of mRNA stemness was executed.

RESULTS

The researchers constructed a prognostic model based on 22 graphene therapy-related lncRNAs. Protective lncRNAs (AC010280.2, AL365361.1, and LINC01549) and negative lncRNAs (AC026412.3, AL031985.3, ELFN1-AS1, SNHG4, and EB2-AS1) were identified. Higher risk scores correlated with shorter survival. Low-risk immune pathways included Type_II_IFN_Reponse and cytolytic_activity. Subgroups differed significantly in TMB, TIDE, MDSC, exclusion, and dysfunction. Low TMB values correlated with longer survival. The high-risk subgroup showed increased sensitivity to screened compounds, and mRNAsi was higher in cancer tissue.

CONCLUSIONS

Our GTLncRNAs-based model accurately predicted survival of HCC patients and underscored the influence of graphene therapy-related genes on the tumor microenvironment. Potential treatment compounds were identified, and the mRNAsi index demonstrated prognostic value.

The role of alternative pre-mRNA splicing in cancer progression

Alternative pre-mRNA splicing is a critical mechanism that generates multiple mRNA from a single gene, thereby increasing the diversity of the proteome. Recent research has highlighted the significance of specific splicing isoforms in cellular processes, particularly in regulating cell numbers. In this review, we examine the current understanding of the role of alternative splicing in controlling cancer cell growth and discuss specific splicing factors and isoforms and their molecular mechanisms in cancer progression. These isoforms have been found to intricately control signaling pathways crucial for cell cycle progression, proliferation, and apoptosis. Furthermore, studies have elucidated the characteristics and functional importance of splicing factors that influence cell numbers. Abnormal expression of oncogenic splicing isoforms and splicing factors, as well as disruptions in splicing caused by genetic mutations, have been implicated in the development and progression of tumors. Collectively, these findings provide valuable insights into the complex interplay between alternative splicing and cell proliferation, thereby suggesting the potential of alternative splicing as a therapeutic target for cancer.

Identification of tumor-infiltrating immune cells and microenvironment-relevant genes in nasopharyngeal carcinoma based on gene expression profiling

AIMS

The purpose of this study was to investigate the prognostic significance of tumor-infiltrating immune cells and microenvironment-relevant genes in nasopharyngeal carcinoma (NPC) and their correlations.

MATERIALS AND METHODS

The "xCell" algorithm was used to calculate the enrichment scores for 33 immune cells in the samples of GSE12452, GSE40290, GSE53819, GSE68799, and GSE102349. The difference of immune cells between NPC group and non-cancerous group and the prognostic value of the immune cells were analyzed. Besides, based on the Microenvironment scores, the differentially expressed genes (DEGs) between the high- and low-score groups were screened to identify the microenvironment-relevant hub genes. Furthermore, the DEGs were used to establish a risk score model for predicting progression-free survival (PFS) via LASSO penalized Cox regression.

KEY FINDINGS

The scores of B-cells and Memory B-cells of NPC were significantly lower than those of non-cancerous tissues, and they were positively associated with PFS. Moreover, 10 hub genes (PTPRC, CD19, CD79B, BTK, CD79A, SELL, MS4A1, CD38, CD52, and CD22) were identified and positively correlated with B-cells, Memory B-cells, and Microenvironment scores in GSE12452, GSE68799, and GSE102349. High expression levels of CD22, CD38, CD79B, MS4A1, SELL, and PTPRC were associated with longer PFS. Besides, a risk score model composed of DARC, IL33, IGHG1, and SLC6A8 was established with a good performance for PFS prediction.

SIGNIFICANCE

These results enhance our understanding of the composition and prognostic significance of tumor-infiltrating immune cells in NPC lesions, and provide potential targets for prognostication and immunotherapy for NPC patients.

Gene Biomarkers Derived from Clinical Data of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common cancer of high mortality, mainly due to the difficulty in diagnosis during its clinical stage. Here we aim to find the gene biomarkers, which are of important significance for diagnosis and treatment. In this work, 3682 differentially expressed genes on HCC were firstly differentiated based on the Cancer Genome Atlas database (TCGA). Co-expression modules of these differentially expressed genes were then constructed based on the weighted correlation network algorithm. The correlation coefficient between the co-expression module and clinical data from the Broad GDAC Firehose was thereafter derived. Finally, the interactive network of genes was then constructed. Then, the hub genes were used to implement enrichment analysis and pathway analysis in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Results revealed that the abnormally expressed genes in the module played an important role in the biological process including cell division, sister chromatid cohesion, DNA repair, and G1/S transition of mitotic cell cycle. Meanwhile, these genes also enriched in a few crucial pathways related to Cell cycle, Oocyte meiosis, and p53 signaling. Via investigating the closeness centrality of the interactive network, eight gene biomarkers including the CKAP2, TPX2, CDCA8, KIFC1, MELK, SGO1, RACGAP1, and KIAA1524 gene were discovered, whose functions had been indeed revealed to be correlated with HCC. This study, therefore, suggests that the abnormal expression of those eight genes may be taken as gene biomarkers of HCC.

The SF3b complex: splicing and beyond

The SF3b complex is an intrinsic component of the functional U2 small nuclear ribonucleoprotein (snRNP). As U2 snRNP enters nuclear pre-mRNA splicing, SF3b plays key roles in recognizing the branch point sequence (BPS) and facilitating spliceosome assembly and activation. Since the discovery of SF3b, substantial progress has been made in elucidating its molecular mechanism during splicing. In addition, numerous recent studies indicate that SF3b and its components are engaged in various molecular and cellular events that are beyond the canonical role in splicing. This review summarizes the current knowledge on the SF3b complex and highlights its multiple roles in splicing and beyond.

Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves' disease and orbitopathy

Graves’ Disease (GD) is an autoimmune condition in which thyroid-stimulating antibodies (TRAB) mimic thyroid-stimulating hormone function causing hyperthyroidism. 5% of GD patients develop inflammatory Graves’ orbitopathy (GO) characterized by proptosis and attendant sight problems. A major challenge is to identify which GD patients are most likely to develop GO and has relied on TRAB measurement. We screened sera/plasma from 14 GD, 19 GO and 13 healthy controls using high-throughput proteomics and miRNA sequencing (Illumina’s HiSeq2000 and Agilent-6550 Funnel quadrupole-time-of-flight mass spectrometry) to identify potential biomarkers for diagnosis or prognosis evaluation. Euclidean distances and differential expression (DE) based on miRNA and protein quantification were analysed by multidimensional scaling (MDS) and multinomial regression respectively. We detected 3025 miRNAs and 1886 proteins and MDS revealed good separation of the 3 groups. Biomarkers were identified by combined DE and Lasso-penalized predictive models; accuracy of predictions was 0.86 (±0:18), and 5 miRNA and 20 proteins were found including Zonulin, Alpha-2 macroglobulin, Beta-2 glycoprotein 1 and Fibronectin. Functional analysis identified relevant metabolic pathways, including hippo signaling, bacterial invasion of epithelial cells and mRNA surveillance. Proteomic and miRNA analyses, combined with robust bioinformatics, identified circulating biomarkers applicable to diagnose GD, predict GO disease status and optimize patient management.