Glioblastoma cell differentiation trajectory predicts the immunotherapy response and overall survival of patients

T cell-mediated tumor killing based signature to predict the prognosis and immunotherapy for glioblastoma

Despite the significant advancements in cancer treatment brought by immune checkpoint inhibitors (ICIs), their effectiveness in treating glioblastoma (GBM) remains highly dissatisfactory. Immunotherapy relies on the fundamental concept of T cell-mediated tumor killing (TTK). Nevertheless, additional investigation is required to explore its potential in prognostic prediction and regulation of tumor microenvironment (TME) in GBM. TTK sensitivity related genes (referred to as GSTTKs) were obtained from the TISIDB. The training cohort was available from the TCGA-GBM, while the independent validation group was gathered from GEO database. Firstly, we examined differentially expressed GSTTKs (DEGs) with limma package. Afterwards, the prognostic DEGs were identified and the TTK signature was established with univariate and LASSO Cox analyses. Next, we examined the correlation between the TTK signature and outcome of GBM as well as immune phenotypes of TME. Furthermore, the evaluation of TTK signature in predicting the effectiveness of immunotherapy has also been conducted. We successfully developed a TTK signature with an independent predictive value. Patients who had a high score experienced a worse prognosis compared to patients with low scores. The TTK signature showed a strong positive association with the infiltration degree of immunocyte and the presence of various immune checkpoints. Moreover, individuals with a lower score exhibited increased responsiveness to ICIs and experienced improved prognosis. In conclusions, we successfully developed and verified a TTK signature that has the ability to predict the outcome and immune characteristics of GBM. Furthermore, the TTK signature has the potential to direct the personalized immunotherapy for GBM.

Integrated single-cell and spatial transcriptomic analysis reveals YBX1 drives immune regulation in GBM progression

The RNA modification 5-methylcytosine (m5C) is widespread across various RNA types, significantly impacting RNA stability and translational efficiency. Accumulating evidence highlights its significant role within the tumorigenesis and progression of multiple malignancies. Nevertheless, the specific process through m5C is implicated in Glioblastoma (GBM) remains unclear. We conducted acomprehensive analysis of m5C expression distribution in single-cell GBM data. Our findings revealed elevated m5C scores in GBM single-cell data compared to the normal group. Additionally, multiple tumors exhibited significantly higher m5C scores than the normal group. Moreover, there was a positive correlation observed between the m5C score and inflammation score. m5C regulatory factor YBX1 exhibited a heightened expression in GBM, correlating closely with metastatic tendencies and an unfavorable prognosis across various cancer types. YBX1 has different biological functions in myeloid cells 1 and myeloid cells 2. YBX1 may act as immunosuppressive regulator by inhibiting the NF-κB pathway and inflammatory response in myeloid cells 1. YBX1 is essential for immune infiltrates, which creates a highly immunosuppressive tumor microenvironment by TNF signaling pathway in myeloid cells 2. YBX1+ neoplastic cells promote cell proliferation by NF-κB pathway. APOE mediates the interaction of YBX1+ myeloid cells and neoplastic cells by NF-κB.

Differentiation Potential of Hypodifferentiated Subsets of Nephrogenic Rests and Its Relationship to Prognosis in Wilms Tumor

Background Wilms tumor (WT) is highly curable, although anaplastic histology or relapse imparts a worse prognosis. Nephrogenic rests (NR) associated with a high risk of developing WT are abnormally retained embryonic kidney precursor cells. Methods After pseudo-time analysis using single-cell RNA sequencing (scRNA-seq) data, we generated and validated a WT differentiation-related gene (WTDRG) signature to predict overall survival (OS) in children with a poor OS. Results A differentiation trajectory from NR to WT was identified and showed that hypodifferentiated subsets of NR could differentiate into WT. Classification of WT children with anaplastic histology or relapse based on the expression patterns of WTDRGs suggested that patients with relatively high levels of hypodifferentiated NR presented a poorer prognosis. A WTDRG-based risk model and a clinically applicable nomogram was developed. Conclusions These findings may inform oncogenesis of WT and interventions directed toward poor prognosis in WT children of anaplastic histology or relapse.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain.

AIM

To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.

RESULTS

An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression.

CONCLUSION

We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery

Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.

Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients

The aim of this study is to explore a novel classification and investigate the clinical significance of hepatocellular carcinoma (HCC) cells. We analyzed integrated single-cell RNA sequencing and bulk RNA-seq data obtained from HCC samples. Cell trajectory analysis divided HCC cells into three subgroups with different differentiation states: state 1 was closely related to phosphoric ester hydrolase activity, state 2 was involved in eukaryotic initiation factor 4E binding, translation regulator activity and ribosome, and state 3 was associated with oxidoreductase activity and metabolism. Three molecular classes based on HCC differentiation-related genes (HDRGs) from HCC samples were identified, which revealed immune checkpoint gene expression and overall survival (OS) of HCC patients. Moreover, a prognostic risk scoring (RS) model was generated based on eight HDRGs, and the results showed that the OS of the high-risk group was worse than that of the low-risk group. Further, potential therapeutic drugs were screened out based on eight prognostic RS-HDRGs. This study highlights the importance of HCC cell differentiation in immunotherapy, clinical prognosis, and potential molecular-targeted drugs for HCC patients, and proposes a direction for the development of individualized treatments for HCC.

Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma

BACKGROUND

Brain metastasis (BM) is the most common intracranial malignancy causing significant mortality, and lung cancer is the most common origin of BM. However, the cellular origins and drivers of BM from lung adenocarcinoma (LUAD) have yet to be defined.

METHODS

The cellular constitutions were characterized by single-cell transcriptomic profiles of 11 LUAD primary tumor (PT) and 10 BM samples (GSE131907). Copy number variation (CNV) and clonality analysis were applied to illustrate the cellular origins of BM tumors. Brain metastasis-associated epithelial cells (BMAECs) were identified by pseudotime trajectory analysis. By using machine-learning algorithms, we developed the BM-index representing the relative abundance of BMAECs in the bulk RNA-seq data indicating a high risk of BM. Therapeutic drugs targeting BMAECs were predicted based on the drug sensitivity data of cancer cell lines.

RESULTS

Differences in macrophages and T cells between PTs and BMs were investigated by single-cell RNA (scRNA) and immunohistochemistry and immunofluorescence data. CNV analysis demonstrated BM was derived from subclones of PT with a gain of chromosome 7. We then identified BMAECs and their biomarker, S100A9. Immunofluorescence indicated strong correlations of BMAECs with metastasis and prognosis evaluated by the paired PT and BM samples from Peking Union Medical College Hospital. We further evaluated the clinical significance of the BM-index and identified 7 drugs that potentially target BMAECs.

CONCLUSIONS

This study clarified possible cellular origins and drivers of metastatic LUAD at the single-cell level and laid a foundation for early detection of LUAD patients with a high risk of BM.

IGFBP3 induced by the TGF-β/EGFRvIII transactivation contributes to the malignant phenotype of glioblastoma

Dual or multi-targets therapy targeting epidermal growth factor receptor variant III (EGFRvIII) and other molecular may relax the constraint for glioblastoma (GBM), putting forward the urgent requirement of finding candidate molecules. Here, the insulin-like growth factor binding protein-3 (IGFBP3) was considered a candidate, whereas the mechanisms of IGFBP3 production remain unclear. We treated GBM cells with exogenous transforming growth factor β (TGF-β) to simulate the microenvironment. We found that TGF-β and EGFRvIII transactivation induced the activation of transcription factor c-Jun, which specifically bound to the promoter region of IGFBP3 through Smad2/3 and ERK1/2 pathways and promoted the production and secretion of IGFBP3. IGFBP3 knockdown inhibited the activation of TGF-β and EGFRvIII signals and the malignant behaviors triggered by them in vitro and in vivo. Collectively, our results indicated a positive feedback loop of p-EGFRvIII/IGFBP3 under administration of TGF-β, blocking IGFBP3 may be an additional target in EGFRvIII-expressing GBM-selective therapeutic strategy.

Development and validation of a prognostic model based on a single-cell RNA-seq in Wilms tumor in children

To analyze the heterogeneity between different cell types in pediatric Wilms tumor (WT) tissue, and identify the differentially expressed genes (DEGs) of malignant tumor cells, thereby establishing a prognostic model. The single-cell sequencing data of pediatric WT tissues were downloaded from the public database. Data filtration and normalization, principal component analysis, and T-distributed stochastic neighbor embedding cluster analysis were performed using the Seurat package of R language. Cells were divided into different clusters, malignant tumor cells were extracted, and DEGs were obtained. Then, the pseudo-time trajectory analysis was performed. Prognostic biomarkers were determined by univariate and multivariate COX regression analyses and LASSO regression analysis. Kaplan-Meier survival analysis and receiver operator characteristic curve analysis were performed. Combined with the prognostic biomarkers and clinical characteristics, a nomogram was generated to predict WT prognosis. The prognostic power was validated in the external datasets. Cells in the WT tissue were divided into 10 clusters. Three prognostic biomarkers that affected the survival time of patients were screened from 215 DEGs in malignant tumor cells, and a nomogram was constructed using the three genes and clinical characteristics. The area under the curve (AUC) values of 3- and 5-year disease-free survival were 0.756 and 0.734, respectively. In the external validation dataset, the AUC value of this nomogram model was 0.826. Based on the single-cell RNA-seq, we recognized cell clusters in the WT tissue of children, identified prognostic biomarkers in malignant tumor cells, and established a comprehensive prognostic model. Our findings might provide new ideas and methods for the diagnosis and treatment of WT.

A novel signature constructed by super-enhancer-related genes for the prediction of prognosis in hepatocellular carcinoma and associated with immune infiltration

Background

Super-enhancer (SE) refers to a regulatory element with super transcriptional activity, which can enrich transcription factors and drive gene expression. SE-related genes play an important role in the pathogenesis of malignant tumors, including hepatocellular carcinoma (HCC).

Methods

The SE-related genes were obtained from the human super-enhancer database (SEdb). Data from the transcriptome analysis and related clinical information with HCC were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The upregulated SE-related genes from TCGA-LIHC were identified by the DESeq2R package. Multivariate Cox regression analysis was used to construct a four-gene prognostic signature. According to the median risk score, HCC patients were divided into high-risk and low-risk group patients.

Results

The Kaplan-Meier (KM) curve showed that a significantly worse prognosis was found for the high-risk group (P<0.001). In the TCGA-LIHC dataset, the area under the curve (AUC) values were 0.737, 0.662, and 0.667 for the model predicting overall survival (OS) over 1-, 3-, and 5- years, respectively, indicating the good prediction ability of our prediction model. This model's prognostic value was further validated in the LIRI-JP dataset and HCC samples (n=65). Furthermore, we found that higher infiltration level of M0 macrophages and upregulated of CTLA4 and PD1 in the high-risk group, implying that immunotherapy could be effective for those patients.

Conclusion

These results provide further evidence that the unique SE-related gene model could accurately predict the prognosis of HCC.

A novel molecular classification method for osteosarcoma based on tumor cell differentiation trajectories

Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients. However, the highly complex cell origin involved in osteosarcoma (OS) limits the utility of traditional bulk RNA sequencing for OS subclassification. Single-cell RNA sequencing (scRNA-seq) holds great promise for identifying cell heterogeneity. However, this technique has rarely been used in the study of tumor subclassification. By analyzing scRNA-seq data for six conventional OS and nine cancellous bone (CB) samples, we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell (CSC)-like subset, which allowed us to classify OS samples into three groups. The classification model was further examined using the TARGET dataset. Each subgroup of OS had different prognoses and possible drug sensitivities, and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment. In addition, we verified the classification model through IHC staining in 138 OS samples, revealing a worse prognosis for Group B patients. Furthermore, we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS. These findings provide a novel subclassification method based on scRNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.

Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

5-Demethylnobiletin mediates cell cycle arrest and apoptosis via the ERK1/2/AKT/STAT3 signaling pathways in glioblastoma cells

5-Demethylnobiletin is the active ingredient in citrus polymethoxyflavones that could inhibit the proliferation of several tumor cells. However, the anti-tumor effect of 5-Demethylnobiletin on glioblastoma and the underlying molecular mechanisms are remains unknown. In our study, 5-Demethylnobiletin markedly inhibited the viability, migration and invasion of glioblastoma U87-MG, A172 and U251 cells. Further research revealed that 5-Demethylnobiletin induces cell cycle arrest at the G0/G1 phase in glioblastoma cells by downregulating Cyclin D1 and CDK6 expression levels. Furthermore, 5-Demethylnobiletin significantly induced glioblastoma cells apoptosis by upregulating the protein levels of Bax and downregulating the protein level of Bcl-2, subsequently increasing the expression of cleaved caspase-3 and cleaved caspase-9. Mechanically, 5-Demethylnobiletin trigged G0/G1 phase arrest and apoptosis by inhibiting the ERK1/2, AKT and STAT3 signaling pathway. Furthermore, 5-Demethylnobiletin inhibition of U87-MG cell growth was reproducible in vivo model. Therefore, 5-Demethylnobiletin is a promising bioactive agent that might be used as glioblastoma treatment drug.

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils Novel Prognostic Biomarkers in Multiple Myeloma

Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.

Identification of immune and stromal cell infiltration-related gene signature for prognosis prediction in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common and life-threatening hematologic malignancy, its occurrence and progression are closely related to immune/stromal cell infiltration in the bone marrow (BM) microenvironment. However, no studies have described an immune/stromal cell infiltration-related gene (ISCIRG)-based prognostic signature for ALL. A total of 444 patients involving 437 bulk and 7 single-cell RNA-seq datasets were included in this study. Eligible datasets were searched and reviewed from the database of TCGA, TARGET project and GEO. Then an integrated bioinformatics analysis was performed to select optimal prognosis-related genes from ISCIRGs, construct a nomogram model for predicting prognosis, and assess the predictive power. After LASSO and multivariate Cox regression analyses, a seven ISCIRGs-based signature was proved to be able to significantly stratify patients into high- and low-risk groups in terms of OS. The seven genes were confirmed that directly related to the composition and status of immune/stromal cells in BM microenvironment by analyzing bulk and single-cell RNA-seq datasets. The calibration plot showed that the predicted results of the nomogram were consistent with the actual observation results of training/validation cohort. This study offers a reference for future research regarding the role of ISCIRGs in ALL and the clinical care of patients.

Ligand-based CAR-T cell: Different strategies to drive T cells in future new treatments

Chimeric antigen receptor (CAR)-based therapies are presented as innovative treatments for multiple malignancies. Despite their clinical success, there is scientific evidence of the limitations of these therapies mainly due to immunogenicity issues, toxicities associated with the infusion of the product, and relapses of the tumor. As a result, novel approaches are appearing aiming to solve and/or mitigate the harmful effects of CAR-T therapies. These include strategies based on the use of ligands as binding moieties or ligand-based CAR-T cells. Several proposals are currently under development, with some undergoing clinical trials to assess their potential benefits. In addition to these, therapies such as chimeric autoantibody receptor (CAAR), B-cell receptor antigen for reverse targeting (BAR), and even chimeric human leukocyte antigen (HLA) antibody receptor (CHAR) have emerged, benefiting from the advantages of antigenic ligands as antibody-binding motifs. This review focuses on the potential role that ligands can play in current and future antitumor treatments and in other types of diseases, such as autoimmune diseases or problems associated with transplantation.

Endoglin (CD105) and proliferation index in recurrent glioblastoma treated with anti-angiogenic therapy

Introduction

CD105 is an angiogenic biomarker that is useful to determine the microvessel density (MVD) within a tumor, namely, in highly vascularized tumors like glioblastoma (GBM). However, its expression has shown inconsistent associations with the prognosis of GBM patients. The aim of this study was to evaluate the value of MVD-CD105 (microvessel density assessed with anti-CD105 antibody) and Ki-67 (proliferation index marker) as prognostic and therapy response biomarkers, specifically in primary tumors and in recurrent tumoral specimens of a cohort of GBM patients treated with bevacizumab upon recurrence.

Materials and methods

We conducted a retrospective study of 102 consecutive GBM patients treated with bevacizumab upon recurrence at CHUSJ between 2010 and 2017. Demographic, clinical, and survival data of all patients were collected and analyzed. The tissue expression of MVD-CD105 and Ki-67 in primary and recurrent specimens was correlated with progression-free survival after temozolomide (PFS-1), progression-free survival after bevacizumab (PFS-2), and overall survival (OS).

Results

The immunohistochemical expression score for MVD-CD105 was similar in primary and recurrent tumoral specimens (mean scores of 15 and 16, respectively). Likewise, the mean Ki-67 expression was similar in primary (mean of 31% of tumor cells) and recurrent tumoral specimens (mean of 29% of tumor cells). MVD-CD105 expression in primary tumors had no impact on PFS-1, PFS-2, or OS. At recurrence, patients whose tumors showed increased MVD-CD105 had worse median PFS-2 (2 vs. 8 months, p = 0.045) and OS (17 vs. 26 months, p = 0.007) compared to those whose tumors showed lower MVD-CD105. CD105 tumoral pattern and localization had no impact on prognosis. Ki-67 expression was not associated with differences in survival outcomes.

Conclusion

In this study, higher MVD-CD105 expression in recurrent GBM patients seems to be associated with a worse PFS-2 and OS while portending no prognostic significance in the primary tumors. This highlights the importance of keeping track of the molecular evolution of the tumor over the course of the disease.

Drug resistance in NSCLC is associated with tumor micro-environment

BACKGROUND

Tumor cell resistance to chemotherapy is the most critical factor that influences the prognosis of cancer patients. It is generally believed that drug resistance is caused by genetic alterations in tumor cells; however, the relationship between drug resistance and the tumor microenvironment (TME) has not been adequately studied. Herein, we successfully identified drug resistance and sensitivity clusters using single-cell transcriptome sequencing data from GSE149383 and established a proportional hazards model to find genes that affected prognosis. The results showed that marker genes between resistant and sensitive clusters were significantly associated with the TME; additionally, the model showed good reliability. Furthermore, we used bulk RNA-seq data to analyze the expression of CD24 and CYP1B1, which revealed little difference in the levels of the two genes in normal and tumor tissues but a significant difference in their expression between drug-resistant and -sensitive cells. In conclusion, our study demonstrated a link between drug resistance and the TME, and we found that CD24 and CYP1B1 may be key regulators of drug resistance development in tumor cells via altering the TME.

Identification of Human Retinal Organoid Cell Differentiation-Related Genes via Single-Cell Sequencing Data Analysis

Objective. To study the development process of the human retina, we analyzed the development track of main cell types and transitional cell populations, identifying the retinal organoid cell differentiation-related genes (RDRGs). Methods. Single-cell RNA sequencing data (scRNA-Seq) of human retinal organoids were downloaded from Gene Expression Omnibus (GEO) database in this study. Data were processed with quality analysis and analysis of variance. Principal component analysis and $t$ -distributed stochastic neighbor embedding were used to conduct dimension reduction analysis and type annotation for the screened data. Marker genes and RDRGs were identified by differential analysis. Cell differentiation characteristics were determined by trajectory analysis. Enrichment pathways were analyzed by Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG), and functional modules were obtained by protein-protein interaction (PPI) network analysis. Results. iPSCs were mainly located at the root of differentiation trajectory, while neurons and astrocytes were distributed in different branches, respectively. Meanwhile, 220 RDRGs were obtained. They were involved in the biological functions related to vision and visual development, as well as significantly enriched in signaling pathways associated with retinal vascular development and retinal neuroregulation. Protein-protein interaction network construction and functional subnetwork analysis were conducted on RDRGs, and two functional submodules were obtained. The enrichment analysis presented that the two submodules played a vital role in retinal development, visual perception, and cell respiration. Conclusions. This study identified RDRGs and revealed the biological functions involved in these genes, which are expected to provide evidence for researching retinal development and diseases.

A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma

Background

Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot.

Methods

By searching for differentially expressed genes related to m7G, we generated a prognostic signature via cluster analysis and established classification criteria of high and low risk scores. The effectiveness of classification was validated using the Non-negative matrix factorization (NMF) algorithm, and repeatedly verified using training and test groups. The dimension reduction method was used to clearly show the difference and clinical significance of the data. All analyses were performed via R (version 4.1.2).

Results

According to the signature that included four genes (TMOD2, CACNG2, PLOD3, and TMSB10), glioblastoma patients were divided into high and low risk score groups. The survival rates between the two groups were significantly different, and the predictive abilities for 1-, 3-, and 5-year survivals were effective. We further established a Nomogram model to further examine the signature,as well as other clinical factors, with remaining significant results. Our signature can act as an independent prognostic factor related to immune-related processes in glioblastoma.

Conclusions

Our research addresses the gap in knowledge in the m7G and glioblastoma research fields. The establishment of a prognostic signature and the extended analysis of the tumor microenvironment, immune correlation, and tumor mutation burden further suggest the important role of m7G in the development and development of this disease. This work will provide support for future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09791-y.

Genome-wide association and multi-omics studies identify MGMT as a novel risk gene for Alzheimer's disease among women

INTRODUCTION

Variants in the tau gene (MAPT) region are associated with breast cancer in women and Alzheimer's disease (AD) among persons lacking apolipoprotein E ε4 (ε4-).

METHODS

To identify novel genes associated with tau-related pathology, we conducted two genome-wide association studies (GWAS) for AD, one among 10,340 ε4- women in the Alzheimer's Disease Genetics Consortium (ADGC) and another in 31 members (22 women) of a consanguineous Hutterite kindred.

RESULTS

We identified novel associations of AD with MGMT variants in the ADGC (rs12775171, odds ratio [OR] = 1.4, P = 4.9 × 10-8 ) and Hutterite (rs12256016 and rs2803456, OR = 2.0, P = 1.9 × 10-14 ) datasets. Multi-omics analyses showed that the most significant and largest number of associations among the single nucleotide polymorphisms (SNPs), DNA-methylated CpGs, MGMT expression, and AD-related neuropathological traits were observed among women. Furthermore, promoter capture Hi-C analyses revealed long-range interactions of the MGMT promoter with MGMT SNPs and CpG sites.

DISCUSSION

These findings suggest that epigenetically regulated MGMT expression is involved in AD pathogenesis, especially in women.

Construction of a Risk Model to Predict the Prognosis and Immunotherapy of Low-Grade Glioma Ground on 7 Ferroptosis-Related Genes

Purpose

Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model.

Patients and Methods

The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated.

Results

FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different.

Conclusion

A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.

Comprehensive Analysis of mTORC1 Signaling Pathway–Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy

Objective: The mammalian target of the rapamycin complex 1 (mTORC1) signaling pathway has emerged as a crucial player in the oncogenesis and development of head and neck squamous cell carcinoma (HNSCC), however, to date, no relevant gene signature has been identified. Therefore, we aimed to construct a novel gene signature based on the mTORC1 pathway for predicting the outcomes of patients with HNSCC and their response to treatment.

Methods: The gene expression and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The key prognostic genes associated with the mTORC1 pathway were screened by univariate Cox regression analyses. A prognostic signature was then established based on significant factors identified in the multivariate Cox regression analysis. The performance of the multigene signature was evaluated by the Kaplan–Meier (K–M) survival analysis and receiver operating characteristic (ROC) analysis. Based on the median risk score, patients were categorized into high- and low-risk groups. Subsequently, a hybrid prognostic nomogram was constructed and estimated by a calibration plot and decision curve analysis. Furthermore, immune cell infiltration and therapeutic responses were compared between the two risk groups. Finally, we measured the expression levels of seven genes by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results: The mTORC1 pathway–based signature was constructed using the seven identified genes (SEC11A, CYB5B, HPRT1, SLC2A3, SC5D, CORO1A, and PIK3R3). Patients in the high-risk group exhibited a lower overall survival (OS) rate than those in the low-risk group in both datasets. Through the univariate and multivariate Cox regression analyses, this gene signature was confirmed to be an independent prognostic risk factor for HNSCC. The constructed nomogram based on age, American Joint Committee on Cancer (AJCC) stage, and the risk score exhibited satisfactory performance in predicting the OS. In addition, immune cell infiltration and chemotherapeutic and immunotherapeutic responses differed significantly between the two risk groups. The expression levels of SEC11A and CYB5B were higher in HNSCC tissues than in normal tissues.

Conclusion: Our study established and verified an mTORC1 signaling pathway–related gene signature that could be used as a novel prognostic factor for HNSCC.

Cell Differentiation Trajectory in Liver Cirrhosis Predicts Hepatocellular Carcinoma Prognosis and Reveals Potential Biomarkers for Progression of Liver Cirrhosis to Hepatocellular Carcinoma

Most hepatocellular carcinoma (HCC) patients occur on a background of liver cirrhosis, the molecular mechanisms of liver cirrhosis and its progression to HCC remain to be fully elucidated. Single cell differentiation trajectory analysis has been used in cell classification and tumor molecular typing, which correlated with disease progression and patient prognosis. Here we use cell differentiation trajectory analysis to investigate the relevance of liver cirrhosis and HCC. Single-cell RNA sequencing (scRNA-seq) data of liver cirrhosis and bulk RNA-seq and clinical data of HCC were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) for analysis. HCC samples were divided into three subtypes, based on differentiation-related genes (DRGs) of liver cirrhosis, each with a different expression profile and overall survival (OS). A two- DRGs (CD34 and RAMP3) based prognostic risk scoring (RS) signature was established which could differentiate OS between high-risk and low-risk groups. And expression levels of CD34 and RAMP3 were predominantly high in endothelial cells. By integrating the RS and clinicopathological features, a nomogram was constructed and can accurately predicted the 1-year, 3-years, and 5-years OS. In conclusion, cell differentiation trajectory of liver cirrhosis can predict the prognosis of HCC, and provides new perspectives on the mechanisms of progression of liver cirrhosis to HCC.

Single cell RNA sequencing reveals differentiation related genes with drawing implications in predicting prognosis and immunotherapy response in gliomas

Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. We aimed to identify differentiation related genes (DRGs) for predicting the prognosis and immunotherapy response in patients with gliomas. We identified three differentiation states and the corresponding DRGs in glioma cells through single-cell transcriptomics analysis. Based on the DRGs, we separated glioma patients into three clusters with distinct clinicopathological features in combination with bulk RNA-seq data. Weighted correlation network analysis, univariate cox regression analysis and least absolute shrinkage and selection operator analysis were involved in the construction of the prognostic model based on DRGs. Distinct clinicopathological characteristics, TIME, immunogenomic patterns and immunotherapy responses were identified across three clusters. A DRG signature composing of 12 genes were identified for predicting the survival of glioma patients and nomogram model integrating the risk score and multi-clinicopathological factors were constructed for clinical practice. Patients in high-risk group tended to get shorter overall survival and better response to immune checkpoint blockage therapy. We obtained 9 candidate drugs through comprehensive analysis of the differentially expressed genes between the low and high-risk groups in the model. Our findings indicated that the risk score may not only contribute to the determination of prognosis but also facilitate in the prediction of immunotherapy response in glioma patients.

DNAJC10 correlates with tumor immune characteristics and predicts the prognosis of glioma patients

Background: The role of DnaJ heat shock protein family (Hsp40) member C10 (DNAJC10) in cancers has been reported but its function in glioma is not clear. We reveal the prognostic role and underlying functions of DNAJC10 in glioma in the present study.

Methods: Reverse Transcription and Quantitative Polymerase Chain Reaction (RT-qPCR) was used to quantify the relative DNAJC10 messenger RNA (mRNA) expression of clinical samples. Protein expressions of clinical samples were tested by Western blot. The overall survival (OS) of glioma patients with different DNAJC10 expression was compared by Kaplan–Meier method (two-sided log-rank test). Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the immune cell infiltrations and immune-related function levels. The independent prognostic role of DNAJC10 was determined by univariate and multivariate Cox regression analyses. The DNAJC10-based nomogram model was established using multivariate Cox regression by R package ‘rms’.

Results: Higher DNAJC10 is observed in gliomas and it is up-regulated in higher grade, isocitrate dehydrogenase (IDH)-wild, 1p/19q non-codeletion, O(6)-methylguanine-DNA methyltransferase (MGMT) unmethylated gliomas. Gliomas with higher DNAJC10 expression present poorer prognosis compared with low-DNAJC10 gliomas. The predictive accuracy of 1/3/5-OS of DNAJC10 is found to be stable and robust using time-dependent ROC model. Enrichment analysis recognized that T-cell activation and T-cell receptor signaling were enriched in higher DNAJC10 gliomas. Immune/stromal cell infiltrations, tumor mutation burden (TMB), copy number alteration (CNA) burden and immune checkpoint genes (ICPGs) were also positively correlated with DNAJC10 expression in gliomas. DNAJ10-based nomogram model was established and showed strong prognosis-predictive ability.

Conclusion: Higher DNAJC10 expression correlates with poor prognosis of glioma and it was a potential prognostic biomarker for glioma.

Colorectal Cancer Cell Differentiation Trajectory Predicts Patient Immunotherapy Response and Prognosis

Objectives

This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer.

Introduction

Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates.

Methods

We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer.

Results

We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables.

Conclusion

This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.

Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm

Objectives: Abdominal aortic aneurysms (AAAs) are associated with high mortality rates. The genes and pathways linked with AAA remain poorly understood. This study aimed to identify key differentially expressed genes (DEGs) linked to the progression of AAA using bioinformatics analysis.

Methods: Gene expression profiles of the GSE47472 and GSE57691 datasets were acquired from the Gene Expression Omnibus (GEO) database. These datasets were merged and normalized using the “sva” R package, and DEGs were identified using the limma package in R. The functions of these DEGs were assessed using Cytoscape software. We analyzed the DEGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein–protein interaction networks were assembled using Cytoscape, and crucial genes were identified using the Cytoscape plugin, molecular complex detection. Data from GSE15729 and GSE24342 were also extracted to verify our findings.

Results: We found that 120 genes were differentially expressed in AAA. Genes associated with inflammatory responses and nuclear-transcribed mRNA catabolic process were clustered in two gene modules in AAA. The hub genes of the two modules were IL6, RPL21, and RPL7A. The expression levels of IL6 correlated positively with RPL7A and negatively with RPL21. The expression of RPL21 and RPL7A was downregulated, whereas that of IL6 was upregulated in AAA.

Conclusions: The expression of RPL21 or RPL7A combined with IL6 has a diagnostic value for AAA. The novel DEGs and pathways identified herein might provide new insights into the underlying molecular mechanisms of AAA.

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.

A metabolism-related gene signature for predicting the prognosis and therapeutic responses in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) often has an insidious onset and rapid progression. Often, when the disease is first diagnosed, the opportune time for surgical intervention has already lapsed. In addition, the effects of systemic treatment is relatively unsatisfactory. Metabolic reprogramming is one of the hallmarks of cancer. This study aimed to identify a set of genes related to metabolism to construct a predictive model for the prognosis of HCC.

Methods

The transcriptomic and clinical data of 352 HCC patients were obtained from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset and divided into a training cohort (n=212) and a testing cohort (n=140) at a ratio of 6:4. Univariate Cox regression analysis and the LASSO Cox regression model were used to identify 5 genes to establish a risk score for predicting the prognosis of HCC patients. Subsequently, the molecular characteristics of the model were assessed and the ability of the model to predict the tumor immune microenvironment and patient response to immunotherapy and chemotherapy was also examined.

Results

The risk score model was constructed based on the five genes, methyltransferase-like protein 6 (METTL6), RNA polymerase III subunit G (POLR3G), phosphoribosyl pyrophosphate amidotransferase (PPAT), SET Domain Bifurcated 2 (SETDB2), and suppressor of variegation 3-9 homolog 2 (SUV39H2). The Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves demonstrated that high-risk patients had a poorer overall survival (OS) compared to low-risk patients. he nomogram score had a better predictive ability compared to the common factors. Our results finally showed that high-risk cases were associated with cell proliferation and cell cycle related gene sets, high tumor protein P53 (TP53) mutation rate, suppressive immunity and increased sensitivity to cisplatin, gemcitabine and docetaxel. Meanwhile, low-risk cases were associated with cell cycle and immune response related pathways, low TP53 mutation rate, active immunity and more benefit from immunotherapy.

Conclusions

This study provided novel insights into the role of metabolism-related genes in HCC, and demonstrated that our model could be a promising prognostic biomarker for distinguishing the molecular and immune characteristics and inferring the potential response to chemotherapy and immunotherapy.