Could microtubule inhibitors be the best choice of therapy in gastric cancer with high immune activity: mutant DYNC1H1 as a biomarker

A pan-cancer analysis of the association of METRN with prognosis and immune infiltration in human tumors

Background

Meteorin (METRN) is expressed predominantly in the central nervous system (CNS), where it functions by regulating glial cell differentiation and promoting axonal elongation. Nonetheless, its function within tumors is still not well understood. In this study, we focused on investigating its expression across various cancers and delving deeper into how METRN expression correlates with prognosis and immune infiltration.

Methods

We explored METRN expression patterns in pan-cancers utilizing data obtained from the UCSC Xena and TCGA. In addition, analyses of survival and clinical association were conducted for tumors where METRN could affect the prognosis. Subsequently, nomogram models were constructed for sarcoma (SARC) and prostate adenocarcinoma (PRAD) to verify METRN's prognostic value in tumors. Furthermore, we also discussed the link between METRN and immune infiltration. As far as mechanisms are concerned, functional enrichment analysis was conducted to analyze the functional components and signaling pathways involved in METRN.

Results

This study found that METRN was abnormally expressed in various tumors, closely connected with the prognosis and clinical characteristics of several tumors, and had good prognostic value. Moreover, analysis of immune infiltration revealed that METRN interacts with multiple immune cells, with alterations in the immune microenvironment potentially influencing tumor prognosis. Enrichment analysis indicates that METRN may influence tumorigenesis and progression through immune-related pathways.

Conclusion

To sum up, our study demonstrates that METRN can be a prospective predictive biomarker in diverse cancer types and a promising target for cancer immunotherapy for pan-cancer.

Potent therapeutic strategy in gastric cancer with microsatellite instability-high and/or deficient mismatch repair

Gastric cancer (GC) is a common malignancy that presents challenges in patient care worldwide. The mismatch repair (MMR) system is a highly conserved DNA repair mechanism that protects genome integrity during replication. Deficient MMR (dMMR) results in an increased accumulation of genetic errors in microsatellite sequences, leading to the development of a microsatellite instability-high (MSI-H) phenotype. Most MSI-H/dMMR GCs arise sporadically, mainly due to MutL homolog 1 (MLH1) epigenetic silencing. Unlike microsatellite-stable (MSS)/proficient MMR (pMMR) GCs, MSI-H/dMMR GCs are relatively rare and represent a distinct subtype with genomic instability, a high somatic mutational burden, favorable immunogenicity, different responses to treatment, and prognosis. dMMR/MSI-H status is a robust predictive biomarker for treatment with immune checkpoint inhibitors (ICIs) due to high neoantigen load, prominent tumor-infiltrating lymphocytes, and programmed cell death ligand 1 (PD-L1) overexpression. However, a subset of MSI-H/dMMR GC patients does not benefit from immunotherapy, highlighting the need for further research into predictive biomarkers and resistance mechanisms. This review provides a comprehensive overview of the clinical, molecular, immunogenic, and therapeutic aspects of MSI-H/dMMR GC, with a focus on the impact of ICIs in immunotherapy and their potential as neoadjuvant therapies. Understanding the complexity and diversity of the molecular and immunological profiles of MSI-H/dMMR GC will drive the development of more effective therapeutic strategies and molecular targets for future precision medicine.

Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing

BACKGROUND

Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood.

OBJECTIVE

To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas.

METHODS

ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level.

RESULTS

A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells.

CONCLUSION

We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks

OBJECTIVE

This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA.

METHODS

Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA.

RESULTS

The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions.

CONCLUSION

The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

DYNC1H1-related epilepsy: Genotype-phenotype correlation

AIM

To explore the phenotypic spectrum and refine the genotype-phenotype correlation of DYNC1H1-related epilepsy.

METHOD

The clinical data of 15 patients with epilepsy in our cohort and 50 patients with epilepsy from 24 published studies with the DYNC1H1 variants were evaluated.

RESULTS

In our cohort, 13 variants were identified from 15 patients (seven males, eight females). Twelve variants were de novo and seven were new. Age at seizure onset ranged from 3 months to 4 years 5 months (median age 1 year). Common seizure types were epileptic spasms, focal seizures, tonic seizures, and myoclonic seizures. Mild-to-severe developmental delay was present in all patients. Six patients were diagnosed with West syndrome and one was diagnosed with epileptic encephalopathy with continuous spikes and waves during slow sleep (CSWS). Collectively, in our cohort and published studies, 17% had ophthalmic diseases, 31% of variants were located in the stalk domain, and 92% patients with epilepsy had a malformation of cortical development (MCD).

INTERPRETATION

The phenotypes of DYNC1H1-related epilepsy included multiple seizure types; the most common epileptic syndrome was West syndrome. CSWS is a new phenotype of DYNC1H1-related epilepsy. One-third of the variants in patients with epilepsy were located in the stalk domain. Most patients had a MCD and developmental delay.

WHAT THIS PAPER ADDS

Nearly 40% of patients with DYNC1H1 variants had epilepsy. Ninety-two percent of patients with DYNC1H1-related epilepsy had malformation of cortical development. More than 10% of patients with DYNC1H1-related epilepsy were diagnosed with West syndrome. Continuous spikes and waves during slow sleep could be a new phenotype of DYNC1H1 variants. One-third of the variants in patients with epilepsy were located in the stalk domain.

Identification of a 6-RBP gene signature for a comprehensive analysis of glioma and ischemic stroke: Cognitive impairment and aging-related hypoxic stress

There is mounting evidence that ischemic cerebral infarction contributes to vascular cognitive impairment and dementia in elderly. Ischemic stroke and glioma are two majorly fatal diseases worldwide, which promote each other's development based on some common underlying mechanisms. As a post-transcriptional regulatory protein, RNA-binding protein is important in the development of a tumor and ischemic stroke (IS). The purpose of this study was to search for a group of RNA-binding protein (RBP) gene markers related to the prognosis of glioma and the occurrence of IS, and elucidate their underlying mechanisms in glioma and IS. First, a 6-RBP (POLR2F, DYNC1H1, SMAD9, TRIM21, BRCA1, and ERI1) gene signature (RBPS) showing an independent overall survival prognostic prediction was identified using the transcriptome data from TCGA-glioma cohort (n = 677); following which, it was independently verified in the CGGA-glioma cohort (n = 970). A nomogram, including RBPS, 1p19q codeletion, radiotherapy, chemotherapy, grade, and age, was established to predict the overall survival of patients with glioma, convenient for further clinical transformation. In addition, an automatic machine learning classification model based on radiomics features from MRI was developed to stratify according to the RBPS risk. The RBPS was associated with immunosuppression, energy metabolism, and tumor growth of gliomas. Subsequently, the six RBP genes from blood samples showed good classification performance for IS diagnosis (AUC = 0.95, 95% CI: 0.902–0.997). The RBPS was associated with hypoxic responses, angiogenesis, and increased coagulation in IS. Upregulation of SMAD9 was associated with dementia, while downregulation of POLR2F was associated with aging-related hypoxic stress. Irf5/Trim21 in microglia and Taf7/Trim21 in pericytes from the mouse cerebral cortex were identified as RBPS-related molecules in each cell type under hypoxic conditions. The RBPS is expected to serve as a novel biomarker for studying the common mechanisms underlying glioma and IS.

Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

BACKGROUND

At present, the diagnostic ability of hepatocellular carcinoma (HCC) based on serum alpha-fetoprotein level is limited. Finding markers that can effectively distinguish cancer and non-cancerous tissues is important for improving the diagnostic efficiency of HCC.

RESULTS

In this study, we developed a predictive model for HCC diagnosis using personalized biological pathways combined with a machine learning algorithm based on regularized regression and carry out relevant examinations. In two training sets, the overall cross-study-validated area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve and the Brier score of the diagnostic model were 0.987 [95%confidence interval (CI): 0.979-0.996], 0.981 and 0.091, respectively. Besides, the model showed good transferability in external validation set. In TCGA-LIHC cohort, the AUROC, AURPC and Brier score were 0.992 (95%CI: 0.985-0.998), 0.967 and 0.112, respectively. The diagnostic model has accomplished very impressive performance in distinguishing HCC from non-cancerous liver tissues. Moreover, we further analyzed the extracted biological pathways to explore molecular features and prognostic factors. The risk score generated from a 12-gene signature extracted from the characteristic pathways was correlated with some immune related pathways and served as an independent prognostic factor for HCC.

CONCLUSION

We used personalized biological pathways analysis and machine learning algorithm to construct a highly accurate HCC diagnostic model. The excellent interpretable performance and good transferability of this model enables it with great potential for personalized medicine, which can assist clinicians in diagnosis for HCC patients.

A Prognosis Marker Dynein Cytoplasmic 1 Heavy Chain 1 Correlates with EMT and Immune Signature in Liver Hepatocellular Carcinoma by Bioinformatics and Experimental Analysis

Background

Liver hepatocellular carcinoma (LIHC) has had a continuous increase in incidence and mortality rates over the last 40 years. Dynein Cytoplasmic 1 Heavy Chain 1 (DYNC1H1) is a protein coding gene which encodes the cytoplasmic dynein heavy chain family. This is the first investigation into the expression of DYNC1H1 and its mechanisms of action in LIHC patients.

Methods

Based on the DYNC1H1 expression data from the TCGA database, we performed the DYNC1H1 expression, clinicopathological data, gene enrichment, and immune infiltration analysis. TIMER and CIBERSORT were used to assess immune responses of DYNC1H1 in LIHC. GEPIA, K-M survival analysis, and immunohistochemical staining pictures from the THPA were used to validate the results. In order to evaluate the diagnostic value of DYNC1H1, GEO datasets were analyzed by using ROC analysis. And quantitative real-time polymerase chain reaction was also carried out to evaluate the expression of DYNC1H1.

Results

DYNC1H1 expression levels were associated with T classification, pathologic stage, histologic grade, and serum AFP levels. DYNC1H1 is an independent factor for a poor prognosis in patients with LIHC. Further study showed that high expression of DYNC1H1 was enriched in epithelial-mesenchymal transition (EMT) and the TGF β signaling pathway by GSEA analysis enrichment, indicating that DYNC1H1 might play a key role in the progression of CRC through EMT and immune response, which also had been validated by the experimental assays.

Conclusions

DYNC1H1 will provide a novel and important perspective for the mechanisms of LIHC by regulating EMT. This gene will be able to act as an efficacious tool for the early diagnosis and effective intervention of LIHC.

Development of Tumor Mutation Burden-Related Prognostic Model and Novel Biomarker Identification in Stomach Adenocarcinoma

Background: Stomach adenocarcinoma (STAD) is one of the most common tumors. Tumor mutation burden (TMB) has been linked to immunotherapy response. We wanted to see if there was any link between TMB and cancer prognosis.

Methods: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to obtain mutation data, gene expression profiles, and clinical data. We looked at the differences in gene expression and immune markers between low and high TMB groups, built an immune prognostic model, and created a dynamic nomograph App that may be used in the clinic. Simultaneously, We ran the immunotherapy prediction and model comparison at the same time. Finally, model gene mutation and copy number variation (CNV) were displayed. The cellular functional experiments were used to investigate the potential role of GLP2R in gastric cancer.

Results: Firstly, basic mutation information and differences in immune infiltration in STAD are revealed. Secondly, the prognostic model developed by us has good accuracy, and the corresponding dynamic nomograph Apps online and immunotherapy prediction facilitate clinical transformation. Furthermore, GLP2R knockdown significantly inhibited the proliferation, migration of gastric cancer cells in vitro.

Conclusion: Our findings imply that TMB plays a significant role in the prognosis of STAD patients from a biological perspective. GLP2R may serve as a potential target for gastric cancer.