Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress

Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer is essential in predicting survival prognosis and facilitating cancer drug treatment. The pan-cancer analysis acquired the Cancer Genome Atlas (TCGA) transcriptome sequencing data from UCSC Xena, which consisted of 33 cancer types (n = 10 440). The Random Forest, Lasso regression, and Cox regression analyses are used to construct an OXS score based on 25 OXS genes. Following this, based on the OXS signature, patients are categorized into low- and high-risk groups. The disparities between the two cohorts regarding survival prognosis, immune infiltration, and drug sensitivity are delved deeply. The expression level of genes is confirmed using immunohistochemistry. The prognosis of pan-cancer patients is adequately predicted by the OXS signature with the assistance of the machine-learning algorithm. A highly accurate nomogram is developed by combining the OXS signature and clinical features. The presence of immune cells indicated that the OXS signature can be associated with the critical pathways of immunotherapy for all types of cancer, and BCL2 showed promising results. Distinct inter-group differences are observed in the OXS signature for frequently utilized antineoplastic medications in clinical settings, including first-line drugs suggested in the guidelines. In summary, by conducting a thorough analysis of OXS genes, a new model based on OXSscore is successfully developed. This model can predict the clinical prognosis and drug sensitivity of pan-cancer with high accuracy. Potential stars in the field of cancer-related anti-OXS may include drugs that target BCL2.

Pan-cancer and single-cell analyses identify CD44 as an immunotherapy response predictor and regulating macrophage polarization and tumor progression in colorectal cancer

Introduction

Cluster of differentiation (CD) 44 is a non-kinase cell surface transmembrane glycoprotein critical for tumor maintenance and progression.

Methods

We conducted a systematic analysis of the expression profile and genomic alteration profile of CD44 in 33 types of cancer. The immune characteristics of CD44 were comprehensively explored by TIMER2.0 and CIBERSORT. In addition, the CD44 transcriptional landscape was examined at the single-cell level. Then, Pseudotime trajectory analysis of CD44 gene expression was performed using Monocle 2, and CellChat was utilized to compare the crosstalk differences between CD44+monocytes and CD44- monocytes. Tumor immune dysfunction and exclusion (TIDE) was used to evaluate the predictive ability of CD44 for immune checkpoint blockade (ICB) responses. The effects of CD44 on colorectal cancer (CRC) and macrophage polarization were investigated by knocking down the expression of CD44 in HCT-116 cell and macrophages in vitro.

Results

The expression of CD44 elevated in most cancers, predicting unfavorable prognosis. In addditon, CD44 was correlation with immune cell infiltration and key immune regulators. CD44+ monocytes had a higher information flow intensity than CD44- monocytes. CD44 had good predictive ability for immune checkpoint blockade responses. Knockdown of CD44 inhibited the proliferation, migration, and invasion of HCT-116 cell in vitro. Knockdown of CD44 inhibited M2 macrophage polarization.

Discussion

These findings suggest that CD44 is involved in regulating tumor development, macrophage polarization, and has certain predictive value for patient clinical prognosis and response to immunotherapy.

Pan-Cancer Analysis Reveals Disulfidoptosis-Associated Genes as Promising Immunotherapeutic Targets: Insights Gained from Bulk Omics and Single-Cell Sequencing Validation

Disulfidoptosis, a novel form of cell death, is distinct from other well-known cell death mechanisms. Consequently, a profound investigation into disulfidoptosis elucidates the fundamental mechanisms underlying tumorigenesis, presenting promising avenues for therapeutic intervention. Comprehensive analysis of disulfidoptosis-associated gene (DRG) expression in pan cancer utilized TCGA, GEO, and ICGC datasets, including survival and Cox-regression analyses for prognostic evaluation. We analyzed the association between DRG expression and both immune cell infiltration and immune-related gene expression using the ESTIMATE and TISDIB datasets. We obtained our single-cell RNA sequencing (scRNA-seq) data from the GEO repository. Subsequently, we assessed disulfidoptosis activity in various cell types. Evaluation of immune cell infiltration and biological functions was analyzed via single-sample gene set enrichment (ssGSEA) and gene set variation analysis (GSVA). For in vitro validation experiments, the results from real-time PCR (RT-qPCR) and Western blot were used to explore the expression of SLC7A11 in hepatocellular carcinoma (HCC) tissues and different cancer cell lines, while siRNA-mediated SLC7A11 knockdown effects on HCC cell proliferation and migration were examined. Expression levels of DRGs, especially SLC7A11, were significantly elevated in tumor samples compared to normal samples, which was associated with poorer outcomes. Except for SLC7A11, DRGs consistently exhibited high CNV and SNV rates, particularly in HCC. In various tumors, DRGs were negatively associated with DNA promoter methylation. TME analyses further illustrated a negative correlation of DRG expression with ImmuneScore and StromalScore and a positive correlation with tumor purity. Our analysis unveiled diverse cellular subgroups within HCC, particularly focusing on Treg cell populations, providing insights into the intricate interplay of immune activation and suppression within the tumor microenvironment (TME). These findings were further validated through RT-qPCR, Western blot analyses, and immunohistochemical analyses. Additionally, the knockdown of SLC7A11 induced a suppression of proliferation and migration in HCC cell lines. In conclusion, our comprehensive pan-cancer analysis research has demonstrated the significant prognostic and immunological role of disulfidoptosis across a spectrum of tumors, notably HCC, and identified SLC7A11 as a promising therapeutic target.

Systematic analysis of DNA methylation-mediated TF dysregulation on lncRNAs reveals critical roles in tumor immunity

Emerging evidence suggests that DNA methylation affects transcriptional regulation and expression perturbations of long non-coding RNAs (lncRNAs) in cancer. However, a comprehensive investigation into the transcriptional control of DNA methylation-mediated dysregulation of transcription factors (TFs) on lncRNAs has been lacking. Here, we integrated the transcriptome, methylome, and regulatome across 21 human cancers and systematically identified the transcriptional regulation of DNA methylation-mediated TF dysregulations (DMTDs) on lncRNAs. Our findings reveal that TF regulation of lncRNAs is significantly impacted by DNA methylation. Comparative analysis of DMTDs on mRNAs revealed a conserved pattern of TFs involvement. Pan-cancer Methylation TFs (MethTFs) and Methylation LncRNAs (MethLncRNAs) were identified, and were found to be closely associated with cancer hallmarks and clinical features. In-depth analysis of co-expressed mRNAs with pan-cancer MethLncRNAs unveiled frequent disruptions in cancer immunity, particularly in the context of inflammatory response. Furthermore, we identified five immune-related network modules that contribute to immune cell infiltration in cancer. Immune-related subtypes were subsequently classified, characterized by high levels of immune cell infiltration, expression of immunomodulatory genes, and relevant immune cytolytic activity score, major histocompatibility complex score, response to chemotherapy, and prognosis. Our findings provide valuable insights into cancer immunity from the epigenetic and transcriptional regulation perspective.

The diagnostic or prognostic values of FADD in cancers based on pan‑cancer analysis

Previous studies have determined that aberrant expression of the fas-associated death domain (FADD) contributes to the development of cancer. However, no pan-cancer analysis has been reported to explore the relationship between FADD and various cancers. Multiple databases were screened to identify cancer datasets for the present study and to validate the expression of FADD in various tumors. The association of FADD alteration with cancer prognosis, clinical features and tumor immunity was also evaluated. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to confirm the expression of FADD in breast, colon, liver and gastric cancer cells. Analysis of Gene Expression Omnibus database and The Cancer Genome Atlas database indicated that FADD was highly expressed in breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD) and prostate adenocarcinoma, whereas RT-qPCR results revealed that FADD was highly expressed in breast cancer and colon cancer. Further analyses demonstrated that FADD expression was significantly altered in ESCA, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma and BRCA. FADD expression was observed to be a risk factor of the overall survival in patients with HNSC, LIHC and LUAD as demonstrated by Kaplan-Meier and Cox regression analyses. The results of the present study demonstrated that FADD is highly expressed in numerous malignancies and can be utilized as a biomarker for the diagnosis of BRCA, COAD, LIHC and stomach adenocarcinoma. Moreover, FADD expression is a predictive risk factor for the development of HNSC, LIHC and LUAD and can potentially be used as a prognostic marker for these cancers.

Pan-cancer analysis of the oncogenic role of the core osteosarcoma gene VCAN in human tumors

OBJECTIVE

Versican (VCAN), a member of the multifunctional glycoprotein family, is involved in various aspects of cancer progression. However, the role of VCAN in diverse cancers remains poorly defined. This research aimed to investigate the correlation between VCAN expression and the oncogenic role, as well as visualize its prognostic landscape in pan-cancer.

METHODS

Raw data in regard to VCAN expression in cancer patients were acquired from GEO GeneChip public database in NCBI. Besides, we selected microarray data GSE16088 for analysis. We retrieved the genes associated with osteosarcoma (OS) from the OMIM database and identified their intersection with the core module. VCAN was suggested to be a potential marker gene for OS. Subsequently, we conducted Gene Set Enrichment Analysis (GSEA) to explore gene functional enrichment. Moreover, we performed pan-cancer analysis on VCAN to gain a comprehensive understanding of its implications across various cancer types.

RESULTS

The VCAN expression in the tumor tissue was higher than that in normal tissue. Elevated expression of VCAN was associated with high the tumor stage and poor long-term survival. There was a significant positive correlation between VCAN and cancer fibroblasts in all pan cancers. Moreover, FBN1 was the intersection gene of VCAN-related genes and linker genes. ANTXR1, COL5A2, CSGALNACT2, and SPARC were the target genes of VCAN genes. GSEA analysis showed that VCAN was mainly enriched in the extracellular matrix (ECM) signaling pathway.

CONCLUSION

VCAN can be used as a marker molecule for the early diagnosis of OS and holds significance as a molecule in cases of OS with distant metastasis. The ECM signaling pathway may be a core pathway in OS development and distant metastasis. These findings shed new light on therapeutics of cancers.

Identifying the Common Cell-Free DNA Biomarkers across Seven Major Cancer Types

Blood-based detection of circulating cell-free DNA (cfDNA) is a non-invasive and easily accessible method for early cancer detection. Despite the extensive utility of cfDNA, there are still many challenges to developing clinical biomarkers. For example, cfDNA with genetic alterations often composes a small portion of the DNA circulating in plasma, which can be confounded by cfDNA contributed by normal cells. Therefore, filtering out the potential false-positive cfDNA mutations from healthy populations will be important for cancer-based biomarkers. Additionally, many low-frequency genetic alterations are easily overlooked in a small number of cfDNA-based cancer tests. We hypothesize that the combination of diverse types of cancer studies on cfDNA will provide us with a new perspective on the identification of low-frequency genetic variants across cancer types for promoting early diagnosis. By building a standardized computational pipeline for 1358 cfDNA samples across seven cancer types, we prioritized 129 shard genetic variants in the major cancer types. Further functional analysis of the 129 variants found that they are mainly enriched in ribosome pathways such as cotranslational protein targeting the membrane, some of which are tumour suppressors, oncogenes, and genes related to cancer initiation. In summary, our integrative analysis revealed the important roles of ribosome proteins as common biomarkers in early cancer diagnosis.

Identification of ASF1B as a prognostic marker for liver cancer by meta-analysis and its immune value revealed by a comprehensive pan-cancer analysis of 33 human cancers

Introduction

As one of the most common malignant tumours, liver cancer is difficult to detect in the early stage, with strong metastasis and poor prognosis. Anti-silencing function protein 1 was originally discovered in yeast as a histone H3-H4 chaperone, and studies have shown that ASF1B may be a target for inhibiting the growth of hepatocellular carcinoma cells.

Aim

To evaluate the diagnostic and prognostic significance of ASF1B expression in human LIHC on the basis of TCGA data.

Material and methods

A meta-analysis revealed that high ASF1B expression was strongly associated with better overall survival. A comprehensive pan-cancer analysis of 33 human cancers revealed the immunotherapeutic value of ASF1B.

Results

In this study, we observed a significant upregulation of ASF1B expression in LIHC samples compared to non-cancer samples. Clinical analysis showed that high expression of ASF1B was associated with age, tumour status, and clinical stage. Survival analysis showed that patients with high ASF1B expression had worse overall survival and progression-free survival than patients with low ASF1B expression. The AUCs of the 1-year, 3-year, and 5-year survival-related ROC curves were 0.672, 0.590, and 0.591, respectively.

Conclusions

Our study shows that ASF1B may provide new ideas for the diagnosis and prognosis of liver cancer patients, as well as providing a new direction for the application of ASF1B in tumour immunotherapy.

A pan-cancer analysis of the oncogenic role of ATP binding cassette subfamily E member 1 (ABCE1) in human tumors: An observational study

ATP-binding-cassette subfamily E member 1 (ABCE1) has been identified as an essential component of RNA translation and cell proliferation. However, studies on its role in pan-cancer are limited. Here, we aimed to characterize ABCE1 expression and its potential biological functions in cancer. ABCE1 expression was analyzed using RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) database, and the Clinical Proteomic Tumor Analysis Consortium database. The prognostic value of ABCE1 was analyzed using clinical survival data from TCGA. We downloaded the immune cell infiltration score of TCGA samples from published articles and online databases and performed a correlation analysis between immune cell infiltration levels, chemokines/chemokine receptors, and ABCE1 expression. We further assessed the association between ABCE1-correlated genes and their functions in pancreatic adenocarcinoma (PAAD). In general, ABCE1 gene expression was upregulated in most tumors. There were significant strong correlations between ABCE1 expression and tumor-infiltrating cells in cancers. Furthermore, RNA transport and ribosome biogenesis were significantly related to ABCE1 expression in PAAD. Our study revealed that ABCE1 may serve as a potential prognostic and immunological pan-cancer biomarker. Moreover, ABCE1 may be used in the development of a novel target for PAAD.

Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures

Cancer tissue-of-origin specific biomarkers are needed for effective diagnosis, monitoring, and treatment of cancers. In this study, we analyzed transcriptomics data from 37 cancer types provided by The Cancer Genome Atlas (TCGA) to identify cancer tissue-of-origin specific gene expression signatures. We developed a deep neural network model to classify cancers based on gene expression data. The model achieved a predictive accuracy of >97% across cancer types indicating the presence of distinct cancer tissue-of-origin specific gene expression signatures. We interpreted the model using Shapley additive explanations to identify specific gene signatures that significantly contributed to cancer-type classification. We evaluated the model and the validity of gene signatures using an independent test data set from the International Cancer Genome Consortium. In conclusion, we present a robust neural network model for accurate classification of cancers based on gene expression data and also provide a list of gene signatures that are valuable for developing biomarker panels for determining cancer tissue-of-origin. These gene signatures serve as valuable biomarkers for determining tissue-of-origin for cancers of unknown primary.

A Comprehensive Pan-Cancer Analysis of 33 Human Cancers Reveals the Immunotherapeutic Value of Aryl Hydrocarbon Receptor

Background

Previous studies have reported the potential of aryl hydrocarbon receptor (AhR) in cancer immunotherapy. However, the mechanisms underpinning its therapeutic value have yet to be comprehensively investigated. Thus, this research aimed to explore the underlying association between AhR and cancer immunotherapy in 33 human cancers.

Methods

The gene expression data and clinical characteristics of 33 cancers were retrieved from The Cancer Genome Atlas database. The immunotherapeutic cohorts included GSE67501 and GSE78220 as well as IMvigor210, which were obtained from the Gene Expression Omnibus database and included in a previously published study respectively. Clinical parameters, including patient age, gender, survival, and tumor stage were analyzed to assess the prognostic value of AhR. The activity of AhR was generated by single sample gene set enrichment analysis and used to evaluate the difference between the AhR transcriptome and protein expression level. To better understand the role of AhR in cancer immunotherapy, the correlation between AhR and tumor microenvironment, as well as its relation to immune processes/elements, such as immune cell infiltration, immune inhibitors and stimulators, and the major histocompatibility complex were analyzed. The relevant underlying pathways associated with AhR signaling in cancer were also explored. Furthermore, the correlation between AhR and two immunotherapeutic biomarkers (tumor mutational burden and microsatellite instability) was investigated. Finally, the relationship between AhR and immunotherapeutic response was explored using three independent immunotherapeutic cohorts.

Results

Although AhR was not closely associated with age (5/33), gender (3/33), or tumor stage (3/21) in any of the studied human cancers, it exhibited potential prognostic value for predicting patient survival. Consistency has been observed between AhR activity and expression in some cancers (7/33). Generally, AhR presented a robust correlation with immune cell infiltration, immune modulators, and immunotherapeutic markers. Moreover, high AhR expression was significantly related to immune-relevant pathways. However, no significant correlation was observed between AhR and the immunotherapeutic response.

Conclusions

This research investigated the immunotherapeutic value of AhR in 33 human cancers, providing evidence regarding the function of AhR and its role in clinical treatment. However, considering that a bioinformatics approach was adopted, the current results are preliminary and require further validation.

Cardiac Regeneration and Tumor Growth-What Do They Have in Common?

Acute myocardial infarction is a leading cause of death. Unlike most adult mammals, zebrafish have the capability to almost fully regenerate their hearts after injury. In contrast, ischemic damage in adult human and mouse hearts usually results in scar tissue. mRNA-Sequencing (Seq) and miRNA-Seq analyses of heart regeneration in zebrafish over time showed that the process can be divided into three phases: the first phase represents dedifferentiation and proliferation of cells, the second phase is characterized by migration, and in the third phase cell signals indicate heart development and differentiation. The first two phases seem to share major similarities with tumor development and growth. To gain more insight into these similarities between cardiac regeneration and tumor development and growth, we used patient matched tumor normal (“healthy”) RNA-Seq data for several tumor entities from The Cancer Genome Atlas (TCGA). Subsequently, RNA data were processed using the same pipeline for both the zebrafish samples and tumor datasets. Functional analysis showed that multiple Gene Ontology terms (GO terms) are involved in both early stage cardiac regeneration and tumor development/growth across multiple tumor entities. These GO terms are mostly associated with cell cycle processes. Further analysis showed that orthologous genes are the same key players that regulated these changes in both diseases. We also observed that GO terms associated with heart development in the third late phase of cardiac regeneration are downregulated in the tumor entities. Taken together, our analysis illustrates similarities between cardiac remodeling and tumor progression.

LncNetP, a systematical lncRNA prioritization approach based on ceRNA and disease phenotype association assumptions

Our knowledge of lncRNA is very limited and discovering novel disease-related long non-coding RNA (lncRNA) has been a major research challenge in cancer studies. In this work, we developed an LncRNA Network-based Prioritization approach, named “LncNetP” based on the competing endogenous RNA (ceRNA) and disease phenotype association assumptions. Through application to 11 cancer types with 3089 common lncRNA and miRNA samples from the Cancer Genome Atlas (TCGA), our approach yielded an average area under the ROC curve (AUC) of 83.87%, with the highest AUC (95.22%) for renal cell carcinoma, by the leave-one-out cross validation strategy. Moreover, we demonstrated the excellent performance of our approach by evaluating the influencing factors including disease phenotype associations, known disease lncRNAs and the numbers of cancer types. Comparisons with previous methods further suggested the integrative importance of our approach. Taking hepatocellular carcinoma (LIHC) as a case study, we predicted four candidate lncRNA genes, RHPN1-AS1, AC007389.1, LINC01116 and BMS1P20 that may serve as novel disease risk factors for disease diagnosis and prognosis. In summary, our lncRNA prioritization strategy can efficiently identify disease-related lncRNAs and help researchers better understand the important roles of lncRNAs in human cancers.

Systemically identifying and prioritizing risk lncRNAs through integration of pan-cancer phenotype associations

LncRNAs have emerged as a major class of regulatory molecules involved in normal cellular physiology and disease, our knowledge of lncRNAs is very limited and it has become a major research challenge in discovering novel disease-related lncRNAs in cancers. Based on the assumption that diverse diseases with similar phenotype associations show similar molecular mechanisms, we presented a pan-cancer network-based prioritization approach to systematically identify disease-specific risk lncRNAs by integrating disease phenotype associations. We applied this strategy to approximately 2800 tumor samples from 14 cancer types for prioritizing disease risk lncRNAs. Our approach yielded an average area under the ROC curve (AUC) of 80.66%, with the highest AUC (98.14%) for medulloblastoma. When evaluated using leave-one-out cross-validation (LOOCV) for prioritization of disease candidate genes, the average AUC score of 97.16% was achieved. Moreover, we demonstrated the robustness as well as the integrative importance of this approach, including disease phenotype associations, known disease genes and the numbers of cancer types. Taking glioblastoma multiforme as a case study, we identified a candidate lncRNA gene SNHG1 as a novel disease risk factor for disease diagnosis and prognosis. In summary, we provided a novel lncRNA prioritization approach by integrating pan-cancer phenotype associations that could help researchers better understand the important roles of lncRNAs in human cancers.