Development of a Hallmark Pathway-Related Gene Signature Associated with Immune Response for Lower Grade Gliomas

Long non-coding RNAs in ferroptosis and cuproptosis impact on prognosis and treatment in hepatocellular carcinoma

Ferroptosis and cuproptosis are recently discovered forms of cell death that have gained interest as potential cancer treatments, particularly for hepatocellular carcinoma. Long non-coding RNAs (lncRNAs) influence cancer cell activity by interacting with various nucleic acids and proteins. However, the role of ferroptosis and cuproptosis-related lncRNAs (FCRLs) in cancer remains underexplored. Ferroptosis and cuproptosis scores for each sample were assessed using Gene Set Variation Analysis (GSVA). Weighted correlation network analysis identified the FCRLs most relevant to our study. A risk model based on FCRLs was developed to categorize patients into high-risk and low-risk groups. We then compared overall survival (OS), tumor immune microenvironment, and clinical characteristics between these groups. The IPS score and ImmuCellAI webpage were used to predict the association between FCRL-related signatures and immunotherapy response. Finally, we validated the accuracy of FCRLs in hepatocellular carcinoma cell lines using induction agents (elesclomol and erastin). Patients in different risk subgroups showed significant differences in OS, immune cell infiltration, pathway activity, and clinical characteristics. Cellular assays revealed significant changes in the expression of AC019080.5, AC145207.5, MIR210HG, and LINC01063 in HCC cell lines following the addition of ferroptosis and cuproptosis inducers. We created a signature of four FCRLs that accurately predicted survival in HCC patients, laid the foundation for basic research related to ferroptosis and cuproptosis in hepatocellular carcinoma, and provided therapeutic recommendations for HCC patients.

Identification of an immune-related eRNA prognostic signature for clear cell renal cell carcinoma

BACKGROUND

Immune-related enhancer RNAs (eRNAs) have garnered significant attention in cancer metabolism research, yet their specific roles in ccRCC have remained elusive.

METHODS

We retrieved eRNA expression profiles from TCGA database and identified immune-related eRNAs (IREs) by assessing their co-expression with immune genes. Utilizing consensus clustering, we organized these IREs into two distinct clusters. The construction of an IREs signature was accomplished through the LASSO and multivariate Cox analysis. Furthermore, we performed Cell Counting Kit-8 and clonogenic assays to assess changes in the proliferative capacity of Caki-1 and 769-P cells.

RESULTS

The existence of two clusters of immune-related eRNAs in ccRCC, each with distinctive prognostic and immunological attributes. Cluster B exhibited immunosuppressive properties and displayed a positive correlation with immunosuppressive cells. Functional enrichment analysis unveiled their involvement in several tumor-promoting pathways, metabolic pathways and immune pathways. The IREs signature demonstrated its potential to accurately predict patient immune and prognostic characteristics. AC003092.1, an eRNA strongly associated with patient survival, emerged as a potential oncogene significantly linked to adverse prognosis and the presence of immunosuppressive cells and checkpoints in ccRCC patients. Notably, AC003092.1 displayed marked upregulation in ccRCC tissues and cell lines, and its knockdown substantially inhibited the proliferation of Caki-1 and 769-P cells.

CONCLUSION

We established a robust predictive model that played a vital role in determining the prognosis, clinicopathological characteristics and immune cell infiltration patterns of ccRCC patients. IRE, particularly AC003092.1, which was strongly associated with survival, hold promise as novel immunotherapeutic targets for ccRCC.

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy.

Low expression of PRRG2 in kidney renal clear cell carcinoma: an immune infiltration-associated prognostic biomarker

OBJECTIVE

This study aims to explore the prognostic significance of Proline-rich γ-carboxyglutamic acid protein 2 (PRRG2) in Kidney Renal Clear Cell Carcinoma (KIRC), a prevalent and deadly cancer, and its association with immune cell infiltration, a key strategy in developing effective biomarkers.

METHODS

The study meticulously elucidated the prognostic significance and potential role of PRRG2 in KIRC, correlating its expression with patient sex, age, metastasis, and pathological stage. Utilizing Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), the involvement of PRRG2 in immune response was investigated. The association between PRRG2 expression and immune cell infiltration was also scrutinized. Ultimately, cellular and tissue identity were confirmed via immunohistochemical staining and quantitative real-time PCR.

RESULTS

The study elucidates a notable decrease in PRRG2 expression in KIRC patients, correlating with demographic factors, metastasis, and pathological staging, and portending an unfavorable prognosis. Bioinformatic analyses underscore PRRG2's role in immune response, with its expression significantly tied to immune cell infiltration and marker expression.

CONCLUSION

PRRG2 may potentially impact prognosis in KIRC patients by regulating immune infiltration, thus rendering PRRG2 a promising candidate prognostic biomarker for KIRC-associated immune infiltration.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

Identification of Molecular Subtypes and Prognostic Characteristics of Adrenocortical Carcinoma Based on Unsupervised Clustering

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with a poor prognosis. Increasing evidence highlights the significant role of immune-related genes (IRGs) in ACC progression and immunotherapy, but the research is still limited. Based on the Cancer Genome Atlas (TCGA) database, immune-related molecular subtypes were identified by unsupervised consensus clustering. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to further establish immune-related gene signatures (IRGS). An evaluation of immune cell infiltration, biological function, tumor mutation burden (TMB), predicted immunotherapy response, and drug sensitivity in ACC patients was conducted to elucidate the applicative efficacy of IRGS in precision therapy. ACC patients were divided into two molecular subtypes through consistent clustering. Furthermore, the 3-gene signature (including PRKCA, LTBP1, and BIRC5) based on two molecular subtypes demonstrated consistent prognostic efficacy across the TCGA and GEO datasets and emerged as an independent prognostic factor. The low-risk group exhibited heightened immune cell infiltration, TMB, and immune checkpoint inhibitors (ICIs), associated with a favorable prognosis. Pathways associated with drug metabolism, hormone regulation, and metabolism were activated in the low-risk group. In conclusion, our findings suggest IRGS can be used as an independent prognostic biomarker, providing a foundation for shaping future ACC immunotherapy strategies.

Integration analysis of single-cell and spatial transcriptomics reveal the cellular heterogeneity landscape in glioblastoma and establish a polygenic risk model

Background

Glioblastoma (GBM) is adults' most common and fatally malignant brain tumor. The heterogeneity is the leading cause of treatment failure. However, the relationship between cellular heterogeneity, tumor microenvironment, and GBM progression is still elusive.

Methods

Integrated analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) of GBM were conducted to analyze the spatial tumor microenvironment. We investigated the subpopulation heterogeneity of malignant cells through gene set enrichment analyses, cell communications analyses, and pseudotime analyses. Significantly changed genes of the pseudotime analysis were screened to create a tumor progress-related gene risk score (TPRGRS) using Cox regression algorithms in the bulkRNA-sequencing(bulkRNA-seq) dataset. We combined the TPRGRS and clinical characteristics to predict the prognosis of patients with GBM. Furthermore, functional analysis was applied to uncover the underlying mechanisms of the TPRGRS.

Results

GBM cells were accurately charted to their spatial locations and uncovered their spatial colocalization. The malignant cells were divided into five clusters with transcriptional and functional heterogeneity, including unclassified malignant cells and astrocyte-like, mesenchymal-like, oligodendrocytes-progenitor-like, and neural-progenitor-like malignant cells. Cell-cell communications analysis in scRNA-seq and stRNA-seq identified ligand-receptor pairs of the CXCL, EGF, FGF, and MIF signaling pathways as bridges implying that tumor microenvironment may cause malignant cells' transcriptomic adaptability and disease progression. Pseudotime analysis showed the differentiation trajectory of GBM cells from proneural to mesenchymal transition and identified genes or pathways that affect cell differentiation. TPRGRS could successfully divide patients with GBM in three datasets into high- and low-risk groups, which was proved to be a prognostic factor independent of routine clinicopathological characteristics. Functional analysis revealed the TPRGRS associated with growth factor binding, cytokine activity, signaling receptor activator activity functions, and oncogenic pathways. Further analysis revealed the association of the TPRGRS with gene mutations and immunity in GBM. Finally, the external datasets and qRT-PCR verified high expressions of the TPRGRS mRNAs in GBM cells.

Conclusion

Our study provides novel insights into heterogeneity in GBM based on scRNA-seq and stRNA-seq data. Moreover, our study proposed a malignant cell transition-based TPRGRS through integrated analysis of bulkRNA-seq and scRNA-seq data, combined with the routine clinicopathological evaluation of tumors, which may provide more personalized drug regimens for GBM patients.

Identification of immune-related gene signature for predicting prognosis in uterine corpus endometrial carcinoma

The objective of this study is to develop a gene signature related to the immune system that can be used to create personalized immunotherapy for Uterine Corpus Endometrial Carcinoma (UCEC). To classify the UCEC samples into different immune clusters, we utilized consensus clustering analysis. Additionally, immune correlation algorithms were employed to investigate the tumor immune microenvironment (TIME) in diverse clusters. To explore the biological function, we conducted GSEA analysis. Next, we developed a Nomogram by integrating a prognostic model with clinical features. Finally, we performed experimental validation in vitro to verify our prognostic risk model. In our study, we classified UCEC patients into three clusters using consensus clustering. We hypothesized that cluster C1 represents the immune inflammation type, cluster C2 represents the immune rejection type, and cluster C3 represents the immune desert type. The hub genes identified in the training cohort were primarily enriched in the MAPK signaling pathway, as well as the PD-L1 expression and PD-1 checkpoint pathway in cancer, all of which are immune-related pathways. Cluster C1 may be a more suitable for immunotherapy. The prognostic risk model showed a strong predictive ability. Our constructed risk model demonstrated a high level of accuracy in predicting the prognosis of UCEC, while also effectively reflecting the state of TIME.

Molecular Basis and Advances in Targeted Immunotherapy for Cancer

Researchers have long attempted to stimulate the immune system of cancer patients as a therapeutic strategy [...].

A novel Cuprotosis-related signature predicts the prognosis and selects personal treatments for melanoma based on bioinformatics analysis

Background

Melanoma is a common and aggressive cutaneous malignancy characterized by poor prognosis and a high fatality rate. Recently, due to the application of Immune-checkpoint inhibitors (ICI) in melanoma treatment, melanoma patients' prognosis has been tremendously improved. However, the treatment effect varies quite differently from patient to patient. In this study, we aim to construct and validate a Cuproptosis-related risk model to improve outcome prediction of ICIs in melanoma and divide patients into subtypes with different Cuproptosis-related genes.

Methods

Here, according to differentially expressed genes from four melanoma datasets in GEO (Gene Expression Omnibus), and one in TCGA (The Cancer Genome Atlas) database, a novel signature was developed through LASSO and Cox regression analysis. We used 781 melanoma samples to examine the molecular subtypes associated with Cuproptosis-related genes and studied the related gene mutation and TME cell infiltration. Patients with melanoma can be divided into at least three subtypes based on gene expression profile. Survival pan-cancer analysis was also conducted for melanoma patients.

Results

The Cuproptosis risk score can predict tumor immunity, subtype, survival, and drug sensitivity for melanoma. And Cuproptosis-associated subtypes can help predict therapeutic outcomes.

Conclusion

Cuproptosis risk score is a promising potential biomarker in cancer diagnosis, molecular subtypes determination, TME cell infiltration characteristics, and therapy response prediction in melanoma patients.

A novel machine learning model based on ubiquitin-related gene pairs and clinical features to predict prognosis and treatment effect in colon adenocarcinoma

BACKGROUND

Ubiquitin and ubiquitin-like (UB/UBL) conjugations are essential post-translational modifications that contribute to cancer onset and advancement. In colon adenocarcinoma (COAD), nonetheless, the biological role, as well as the clinical value of ubiquitin-related genes (URGs), is unclear. The current study sought to design and verify a ubiquitin-related gene pairs (URGPs)-related prognostic signature for predicting COAD prognoses.

METHODS

Using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, URGP's predictive signature was discovered. Signatures differentiated high-risk and low-risk patients. ROC and Kaplan-Meier assessed URGPs' signature. Gene set enrichment analysis (GSEA) examined biological nomogram enrichment. Chemotherapy and tumor immune microenvironment were also studied.

RESULTS

The predictive signature used six URGPs. High-risk patients had a worse prognosis than low-risk patients, according to Kaplan-Meier. After adjusting for other clinical characteristics, the URGPs signature could reliably predict COAD patients. In the low-risk group, we found higher amounts of invading CD4 memory-activated T cells, follicular helper T cells, macrophages, and resting dendritic cells. Moreover, low-risk group had higher immune checkpoint-related gene expression and chemosensitivity.

CONCLUSION

Our research developed a nomogram and a URGPs prognostic signature to predict COAD prognosis, which may aid in patient risk stratification and offer an effective evaluation method of individualized treatment in clinical settings.

Prognostic costimulatory molecule-related signature risk model correlates with immunotherapy response in colon cancer

Costimulatory molecules can promote the activation and proliferation of T cells and play an essential role in immunotherapy. However, their role in the prognosis of colon adenocarcinoma remains elusive. In this study, the expression data of costimulatory molecules and clinicopathological information of 429 patients with colon adenocarcinoma were obtained from The Cancer Genome Atlas database. The patients were divided into training and verification cohorts. Correlation, Cox regression, and Lasso regression analyses were performed to identify costimulatory molecules related to prognosis. After mentioning the construction of the risk mode, a nomogram integrating the clinical characteristics and risk scores of patients was constructed to predict prognosis. Eventually, three prognostic costimulatory molecules were identified and used for constructing a risk model. High expression of these three molecules indicated a poor prognosis. The predictive accuracy of the risk model was verified in the GSE17536 dataset. Subsequently, multivariate regression analysis showed that the signature based on the three costimulatory molecules was an independent risk factor in the training cohort (HR = 2.12; 95% CI = 1.26, 3.56). Based on the risk model and clinicopathological data, the AUC values for predicting the 1-, 3-, and 5-year survival probability of patients with colon adenocarcinoma were 0.77, 0.77, and 0.71, respectively. To the best of our knowledge, this study is the first to report a risk signature constructed based on the costimulatory molecules TNFRSF10c, TNFRSF13c, and TNFRSF11a. This risk signature can serve as a prognostic biomarker for colon adenocarcinoma and is related to the immunotherapeutic response of patients.

Identification and validation of a risk model and molecular subtypes based on tryptophan metabolism-related genes to predict the clinical prognosis and tumor immune microenvironment in lower-grade glioma

Background

Lower-grade glioma (LGG) is one of the most common malignant tumors in the central nervous system (CNS). Accumulating evidence have demonstrated that tryptophan metabolism is significant in tumor. Therefore, this study aims to comprehensively clarify the relationship between tryptophan metabolism-related genes (TRGs) and LGGs.

Methods

The expression level of TRGs in LGG and normal tissues was first analyzed. Next, the key TRGs with prognostic value and differential expression in LGGs were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, a risk model was constructed and Consensus clustering analysis was conducted based on the expression level of key TRGs. Then, the prognostic value, clinicopathological factors, and tumor immune microenvironment (TIME) characteristics between different risk groups and molecular subtypes were analyzed. Finally, the expression, prognosis, and TIME of each key TRGs were analyzed separately in LGG patients.

Results

A total of 510 patients with LGG from The Cancer Genome Atlas (TCGA) dataset and 1,152 normal tissues from the Genotype-Tissue Expression (GTEx) dataset were included to evaluate the expression level of TRGs. After LASSO regression analysis, we identified six key TRGs and constructed a TRGs risk model. The survival analysis revealed that the risk model was the independent predictor in LGG patients. And the nomogram containing risk scores and independent clinicopathological factors could accurately predict the prognosis of LGG patients. In addition, the results of the Consensus cluster analysis based on the expression of the six TRGs showed that it could classify the LGG patients into two distinct clusters, with significant differences in prognosis, clinicopathological factors and TIME between these two clusters. Finally, we validated the expression, prognosis and immune infiltration of six key TRGs in patients with LGG.

Conclusion

This study demonstrated that tryptophan metabolism plays an important role in the progression of LGG. In addition, the risk model and the molecular subtypes we constructed not only could be used as an indicator to predict the prognosis of LGG patients but also were closely related to the clinicopathological factors and TIME of LGG patients. Overall, our study provides theoretical support for the ultimate realization of precision treatment for patients with LGG.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework

Background

Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.

Methods

The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.

Results

In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.

Conclusion

Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

Construction and experimental validation of a B cell senescence-related gene signature to evaluate prognosis and immunotherapeutic sensitivity in bladder cancer

Senescent B cells exhibit reduced antibody production and enhanced proinflammatory cytokine and chemokine secretion, exerting non-negligible functions in antitumor immunity. This study aims to clarify the prognosis value of B cell senescence-related genes in bladder cancer (BLCA). Twelve B cell senescence-related genes were identified based on previous studies and the single-cell RNA sequencing of a BLCA sample from Gene Expression Omnibus (GEO). The Cancer Genome Atlas BLCA cohort was used as the training dataset. Three cohorts from GEO, 35 clinical samples from the local hospital, and in vitro cell experiments were used for validation. The unsupervised clustering based on the 12 genes was associated with the prognosis and the tumor immunity. Through least absolute shrinkage and selection operator regression and random forest algorithm, G protein subunit gamma 11 (GNG11) and inhibitor of DNA binding 1 (ID1) of the 12 genes were determined as significant prognosis predictors and then included in the multivariate Cox regression model. The model was a reliable and robust prognosis biomarker across multiple large-scale cohorts (pooled HR = 1.76, 95% CI = 1.41-2.20). The tight association between the model and BLCA malignant degree was demonstrated in the local cohort (P < 0.01). The model could also predict the immunotherapeutic sensitivity, which was confirmed by the tumor immune dysfunction and exclusion algorithm (P < 0.0001) and IMvigor210 cohort (P < 0.0001). At last, in vitro cell experiments in IM-9 and GM12878 B cells indicated that GNG11 and ID1 were involved in the cellular aging process. Collectively, a B cell senescence-related gene signature was constructed to evaluate the prognosis and immunotherapeutic response in BLCA, providing novel insights into the biological mechanisms.