MAPK-RAP1A Signaling Enriched in Hepatocellular Carcinoma Is Associated With Favorable Tumor-Infiltrating Immune Cells and Clinical Prognosis

Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma

Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network

BACKGROUND

Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC.

METHODS

We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth.

RESULTS

NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%).

CONCLUSIONS

By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.

Differential Gene Expression of Checkpoint Markers and Cancer Markers in Mouse Models of Spontaneous Chronic Colitis

The presence of checkpoint markers in cancer cells aids in immune escape. The identification of checkpoint markers and early cancer markers is of utmost importance to gain clarity regarding the relationship between colitis and progressive inflammation leading to cancer. Herein, the gene expression levels of checkpoint makers, cancer-related pathways, and cancer genes in colon tissues of mouse models of chronic colitis (Winnie and Winnie-Prolapse mice) using next-generation sequencing are determined. Winnie mice are a result of a Muc2 missense mutation. The identification of such genes and their subsequent expression and role at the protein level would enable novel markers for the early diagnosis of cancer in IBD patients. The differentially expressed genes in the colonic transcriptome were analysed based on the Kyoto Encyclopedia of Genes and Genomes pathway. The expression of several oncogenes is associated with the severity of IBD, with Winnie-Prolapse mice expressing a large number of key genes associated with development of cancer. This research presents a number of new targets to evaluate for the development of biomarkers and therapeutics.

Blood cell DNA methylation biomarkers in preclinical malignant pleural mesothelioma: The EPIC prospective cohort

Malignant pleural mesothelioma (MPM) is a rare and aggressive cancer mainly caused by asbestos exposure. Specific and sensitive noninvasive biomarkers may facilitate and enhance screening programs for the early detection of cancer. We investigated DNA methylation (DNAm) profiles in MPM prediagnostic blood samples in a case-control study nested in the European Prospective Investigation into Cancer and nutrition (EPIC) cohort, aiming to characterise DNAm biomarkers associated with MPM. From the EPIC cohort, we included samples from 135 participants who developed MPM during 20 years of follow-up and from 135 matched, cancer-free, controls. For the discovery phase we selected EPIC participants who developed MPM within 5 years from enrolment (n = 36) with matched controls. We identified nine differentially methylated CpGs, selected by 10-fold cross-validation and correlation analyses: cg25755428 (MRI1), cg20389709 (KLF11), cg23870316, cg13862711 (LHX6), cg06417478 (HOOK2), cg00667948, cg01879420 (AMD1), cg25317025 (RPL17) and cg06205333 (RAP1A). Receiver operating characteristic (ROC) analysis showed that the model including baseline characteristics (age, sex and PC1wbc) along with the nine MPM-related CpGs has a better predictive value for MPM occurrence than the baseline model alone, maintaining some performance also at more than 5 years before diagnosis (area under the curve [AUC] < 5 years = 0.89; AUC 5-10 years = 0.80; AUC >10 years = 0.75; baseline AUC range = 0.63-0.67). DNAm changes as noninvasive biomarkers in prediagnostic blood samples of MPM cases were investigated for the first time. Their application can improve the identification of asbestos-exposed individuals at higher MPM risk to possibly adopt more intensive monitoring for early disease identification.

Overcoming resistance to immune checkpoint inhibitors in hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma is one of the leading causes of cancer mortality globally, and its incidence is increasing. Immune checkpoint therapy has revolutionized the treatment of hepatocellular carcinoma over the past few years. However, only a limited proportion of patients with hepatocellular carcinoma respond to immunotherapy. Despite the significant breakthroughs, the molecular mechanisms that drive immune responses and evasion are largely unresolved. Predicting tumor response and resistance to immune checkpoint inhibitors is a significant challenge. In this review, we focus on the current research progress of immune checkpoint inhibitors in hepatocellular carcinoma. Importantly, this review highlights the underlying mechanisms of resistance to immune checkpoint inhibitors and summarizes potential strategies to overcome the resistance to immune checkpoint inhibitors in hepatocellular carcinoma.

Investigations of nitazoxanide molecular targets and pathways for the treatment of hepatocellular carcinoma using network pharmacology and molecular docking

Nitazoxanide has been investigated for colorectal cancer and breast cancer. However, its molecular targets and pathways have not yet been explored for hepatocellular carcinoma (HCC) treatment. Utilizing a network pharmacology approach, nitazoxanide’s potential targets and molecular pathways for HCC treatment were investigated. HCC targets were extracted from the GeneCards database. Potential targets of nitazoxanide were predicted using Swiss Target Prediction and Super Pred. Intersecting targets were analyzed with VENNY online tool. Using Cytoscape, a protein-protein interaction (PPI), cluster, and core targets-pathways networks were constructed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. The nitazoxanide was molecularly docked with anti-HCC core targets by employing Auto Dock Vina. A total of 168 potential targets of nitazoxanide, 13,415 HCC-related targets, and 153 intersecting targets were identified. The top eight anti-HCC core targets were identified: SRC, EGFR, CASP3, MMP9, mTOR, HIF1A, ERBB2, and PPARG. GO enrichment analysis showed that nitazoxanide might have anti-HCC effects by affecting gene targets involved in multiple biological processes (BP) (protein phosphorylation, transmembrane receptor protein tyrosine kinase (RTKs) signaling pathway, positive regulation of MAP kinase activity, etc.). KEGG pathways and core targets-pathways network analysis indicated that pathways in cancer and proteoglycans in cancer are two key pathways that significantly contribute to the anti-HCC effects of nitazoxanide. Results of molecular docking demonstrated the potential for active interaction between the top eight anti-HCC core targets and nitazoxanide. Our research offers a theoretical basis for the notion that nitazoxanide may have distinct therapeutic effects in HCC, and the identified pharmacological targets and pathways might function as biomarkers for HCC therapy.

Prognostic Significance of Alternative Splicing Genes in Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Background

Alternative splicing (AS) acts on many tumors and its relationship with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) needs to be researched.

Methods

RNA sequencing data and clinical information of CESC cohorts were obtained from the Cancer Genome Atlas (TCGA) and SpliceSeq was used to analyze the splicing profile of mRNA in CESC. UpSetR displayed the intersections among AS events and univariate analysis chose survival-associated AS and splicing factor (SF) genes. Functional analysis was operated on Enrichr, STRING database and MCODE analysis were used to evaluate protein-protein interaction (PPI) information. LASSO and multivariate analysis constructed prognostic model and risk analysis of tumor infiltrating immune cells was also conducted.

Results

A total of 402 AS-generated genes were found to be associated with CESC prognosis. Functional analysis showed that Golgi to lysosome transport was enriched. PPI network suggested that UBA52 was most functional. Dendritic cells activated, dendritic cells resting, macrophages M0, mast cells resting, T cells CD4 memory activated and T cells CD8 were most correlative with the risk score.

Conclusion

SFs and AS events can directly or indirectly affect the prognosis of CESC patients and this study identified SNRPA and CELF2 as two CESC-engaged SFs.