TCGA Pan-cancer Research Articles

Lung adenocarcinomas with mucinous histology: clinical, genomic, and immune microenvironment characterization and outcomes to immunotherapy-based treatments and KRASG12C inhibitors

BackgroundApproximately 10% of lung adenocarcinomas (LUAD) have mucinous histology (LUADMuc), which is associated to a light/absent smoking history and a high prevalence of KRAS mutations. We sought to characterize LUADMuc by comparing it to LUAD without mucinous histology (LUADnon-muc) and determine the relative benefit of current treatments. Patients and MethodsPatients with LUAD from five institutions and TCGA PanCancer Atlas classified as LUADMuc or LUADnon-muc were included. Clinicopathologic, genomic, immunophenotypic, transcriptional features, and treatment outcomes were compared between LUADMuc and LUADnon-muc. ResultsOf 4,082 patients with LUAD, 9.9% had LUADMuc. Compared to LUADnon-muc, patients with LUADMuc had a lighter smoking history (median: 15 vs 20 pack-years, P=0.008), lower PD-L1 TPS (median: 0% vs 5%, P<0.0001), and lower tumor mutation burden (median: 6.8 vs 8.5 mutations/megabase, P<0.0001). Mutations in KRAS, NKX2-1 (TTF-1), STK11, SMARCA4, GNAS, and ALK rearrangements were enriched in LUADMuc, while TP53, EGFR, BRAF, and MET mutations were enriched in LUADnon-muc. At stage IV diagnosis, LUADMuc was more likely to have contralateral lung metastasis (55.2% vs 36.9%, P<0.0001) and less likely to have brain metastases (23.3% vs 41.9%, P<0.0001). Compared to LUADnon-muc, LUADMuc cases showed lower intratumor CD8+, PD-1+, CD8+PD-1+, and FOXP3+ cells. Among metastatic cases receiving ICIs, compared to LUADnon-muc (N=1,511), LUADMuc (N=112) had lower objective response rate (ORR, 8.4% vs 25.9%, P<0.0001), and shorter median progression-free survival (mPFS, 2.6 vs 3.9 months, P<0.0001) and overall survival (mOS, 9.9 vs 17.2 months, P<0.0001). Similarly, patients with LUADMuc had worse outcomes to chemo-immunotherapy. LUADMuc (N=18) and LUADnon-muc (N=150) had similar ORR (16.7% vs 34.9%, P=0.12) and mPFS (4.6 vs 5.6 months, P=0.17) to treatment with KRASG12C inhibitors, but LUADMuc had shorter mOS (6.8 vs 10.8 months, P=0.018). ConclusionLUADMuc represents a distinct LUAD subpopulation with unique clinicopathologic, genomic, immunophenotypic, and transcriptional features, achieving worse outcomes to standard immunotherapy-based treatments.

Pan-cancer TCGA analysis reveals the potential involvement of B-type lamins in dysregulating chromosome segregation in human cancer.

Lamins play a crucial role in maintaining nuclear structure and function. Our study investigates the expression patterns and clinical implications of B-type lamins with a special focus on lamin B2 across various cancer types using comprehensive RNA sequencing datasets. We found that high expression levels of lamin B1 and lamin B2 are associated with decreased overall and disease-free survival in cancer. This association is further pronounced when both lamins are co-expressed, indicating a compounded negative impact on patient prognosis. Additionally, we highlighted the relationship between B-type lamins and the tumor microenvironment. Lamin B1 mRNA expression shows a strong positive correlation with activated CD4+ T-cells and type 2 T-helper cells (Th2), suggesting its role in immune cell infiltration and response within tumors. Lamin B2 expression also correlates moderately with these immune cells, indicating a potential but lesser role in modulating the immune landscape. Notably, the epigenetic state of lamin B1 significantly affects the tumor microenvironment, suggesting a dual role in structural integrity and immune modulation. We have identified 9 lamin B2 interacting proteins that are co-expressed with B-type lamins in cancerous conditions and modulate cytokinesis and cell division pathways during tumorigenesis. Furthermore, we have identified specific molecular targets of B-type lamins that co-express with them in a range of human cancers and are potentially involved in dysregulating chromosome segregation and mRNA binding. Overexpression of these targets alongside B-type lamins correlates with poor prognosis in multiple cancers. These findings underscore the potential of B-type lamins as biomarkers for poor prognosis and as targets for cancer therapies.

An integrative analysis to identify pancancer epigenetic biomarkers

Integrating and analyzing the pancancer data collected from different experiments is crucial for gaining insights into the common mechanisms in the molecular level underlying the development and progression of cancers. Epigenetic study of the pancancer data can provide promising results in biomarker discovery. The genes that are epigenetically dysregulated in different cancers are powerful biomarkers for drug-related studies. This paper identifies the genes having altered expression due to aberrant methylation patterns using differential analysis of TCGA pancancer data of 12 different cancers. We identified a comprehensive set of 115 epigenetic biomarker genes out of which 106 genes having pancancer properties. The correlation analysis, gene set enrichment, protein–protein interaction analysis, pancancer characteristics analysis, and diagnostic modeling were performed on these biomarkers to illustrate the power of this signature and found to be important in different molecular operations related to cancer. An accuracy of 97.56% was obtained on TCGA pancancer gene expression dataset for predicting the binary class tumor or normal. The source code and dataset of this work are available at https://github.com/panchamisuneeth/EpiPanCan.git.

9316 Modulation Of Intracellular Lipid Access By ATGL Determines Ferroptosis Sensitivity In Castration-resistant Prostate Cancer

Abstract Disclosure: P. Cao: None. D. Awad: None. J. Han: None. D.E. Frigo: Grant Recipient; Self; GTx, Inc.. Other; Self; Familial relationship with Biocity Biopharmaceuticals, Hummingbird Bioscience, Bellicum Pharmaceuticals, Maia Biotechnology, Alms Therapeutics, Hinova Pharmaceuticals and Barricade Therapeutics. Ferroptosis induction has recently been recognized as a novel therapeutic approach to alter the outcomes of drug-resistant cancer. Ferroptosis is cell death that is caused by the intracellular accumulation of toxic lipid hydroperoxides. Due to their elevated metabolism, castration-resistant prostate cancer (CRPCs) are highly prone to ferroptosis; yet, many of them manage to avert this cellular fate. We have identified a key enzyme involved in lipid metabolism, adipose triglyceride lipase (ATGL), that engenders CRPCs with the ability to resist ferroptosis. Our data has shown that ATGL deletion by CRISPR-Cas9 knockout method sensitized human CRPC cell lines such as C4-2, C4-2BLT, and PC3 cells to the ferroptosis inducer RSL3. In all cases, RSL3-mediated cell death could be blocked by the antioxidant Ferrostatin-1, confirming ferroptosis as the mechanism of cell death. Knocking out ATGL also augmented the ferroptosis inducer-mediated increase in lipid peroxides as indicated by the increased ratio of oxidized to non-oxidized C11-BODIPY 581/591. With regards to the mechanism for the sensitivity of ATGL KO to ferroptosis induction, previous research has established that the enzyme long-chain acyl-CoA synthetase 4 (ACSL4) is universally essential to facilitate ferroptosis. More specifically, ATGL(PNPLA2) was found to inversely correlate with ACSL4 in the Taylor et al., the TCGA PanCancer Atlas, and Metastatic Prostate Adenocarcinoma (SU2C/PCF Dream Team) datasets in both primary and metastatic tumors. qPCR and western blot analysis of ACSL4 levels further showed that ACSL4 expression is closely tied to the lipase activity of ATGL. Addition of arachidonic acid significantly induced ferroptosis when combined with RSL3 in a dose-dependent manner only in ATGL KO cells. This indicates that PUFAs can render ATGL KO cells even more sensitive to RSL3-induced ferroptosis while cells with functional ATGL remain highly resistant, further reinforcing that ATGL is an essential safeguard of CRPC against ferroptosis. ATGL KO cells also demonstrated sensitivity to the antiandrogen Enzalutamide. The combination of Enzalutamide and RSL3 substantially decreased cell viability in ATGL KO cells as compared to control cells, indicating a strong synergy effect particularly seen in ATGL KO cells. In summary, this work demonstrates that ATGL is an important and novel regulator of ferroptosis in CRPC and supports the use of ferroptosis inducer as a new therapeutic approach in CRPC. Presentation: 6/2/2024

TFF3 and PVRL2 co-targeting identified by multi-omics approach as an effective cancer immunosuppression strategy

BackgroundThe immunosuppressive tumour microenvironment (TME) plays a critical role in cancer progression and relapse by significantly influencing cancer pathogenesis through autocrine and paracrine signalling. Trefoil factor 3 (TFF3), a secreted protein, has been implicated in modulating the TME to promote cancer advancement. Herein, we investigated the potential association between TFF3 and key immunosuppressive TME components to distinguish a co-targetable oncotherapeutic strategy. MethodsThe TFF3-PVRL2 association were identified and investigated by integrating multiple bioinformatic-tools. The virtual compound screening for PVRL2 inhibitors was done with EasyVS. The TFF3-PVRL2 protein-level correlation was validated by immunoblotting, and the effectiveness of co-inhibiting TFF3 and PVRL2 was assessed using siRNA and AMPC (a TFF3 inhibitor). ResultsAnalysis of the TISIDB database revealed a positive correlation between TFF3 and PVRL2 mRNA levels across multiple cancer types. This correlation was confirmed at the protein level through immunoblot analysis. Further evaluation using TCGA pan-cancer datasets demonstrated that TFF3 and PVRL2 interact to establish an immunosuppressive TME, promoting cancer progression in BRCA, LUAD, PAAD, PRAD, and STAD. Enrichment analyses of positively correlated genes, PPI network hub proteins, and ceRNA networks involving TFF3 and PVRL2, conducted using LinkedOmics, STRING, and Cytoscape, provided insights into their potential co-functions in cancer. A cell-based assay was performed to evaluate the combined therapeutic efficacy of targeting both, TFF3 and PVRL2 and virtual screening identified potential drugs for inhibiting PVRL2. ConclusionPVRL2 has emerged as a promising immunoinhibitory target with significant associations with TFF3 and represents a key co-targetable molecule for effective oncotherapeutic strategies.

Comprehensive analysis to identify IL7R as a immunotherapy biomarker from pan-cancer analysis to in vitro validation

BackgroundImmunotherapy faces a major challenge in treatment resistance, highlighting the need for efficacy biomarkers identification. The tumor microenvironment (TME) significantly influences treatment outcomes, necessitating molecular TME exploration to address immunotherapy resistance.MethodsThe study initially pinpointed IL7R as a pivotal TME gene and then examined its impact on TME’s CD8 + T cells at the single-cell level. Bulk-RNA analysis investigated IL7R function, immune cell infiltration related to IL7R in TCGA pan-cancer samples with its expression verified in clinical samples through immunohistochemistry. Genome instability and immune-related molecular expression associated with IL7R were also assessed. Furthermore, the clinical efficacy of IL7R was evaluated in various immunotherapy treatment cohorts.ResultsOur single-cell analyses and cell-cased experiment revealed that T cells with high IL7R expression tended to be non-terminal and correlated with favorable immunotherapy responses. High IL7R expression corresponded to increased immune and stromal cell signiture, immune pathway enrichment, and an immune-inflamed environment in Bulk-RNA analysis and immunohistochemistry verification. These patients exhibited higher proportions of memory T cells and M1 cells within the TME, along with frequent genome instability and immune molecular upregulation. While IL7R had varied prognostic impact across the TCGA dataset, patients with high IL7R expression showed extended survival under immunotherapy.ConclusionIL7R plays a critical role in shaping TME diversity across cancer types and holds promise as a relevant biomarker for predicting immunotherapy benefits.

Comprehensive pan-cancer analysis and experiments revealed R3HDM1 as a novel predictive biomarker for prognosis and immune therapy response.

R3HDM1, an RNA binding protein with one R3H domain, remains uncharacterized in terms of its association with tumor progression, malignant cell regulation, and the tumor immune microenvironment. This paper aims to fill this gap by analyzing the potential of R3HDM1 in diagnosis, prognosis, chemotherapy, and immune function across various cancers. Data was collected from the Firehost database (http://gdac.broadinstitute.org) to obtain the TCGA pan-cancer queue containing tumor and normal samples. Additional data on miRNA, TCPA, mutations, and clinical information were gathered from the UCSC Xena database (https://xenabrowser.net/datapages/). The mutation frequency and locus of R3HDM1 in the TCGA database were examined using the cBioPortal. External validation through GEO data was conducted to assess the differential expression of R3HDM1 in different cancers. Protein expression levels were evaluated using the Clinical Proteomics Tumor Analysis Alliance (CPTAC). The differential expression of R3HDM1 was verified in lung adenocarcinoma cell lines and normal lung glandular epithelial cells via RT-qPCR. Cell migration and proliferation experiments were conducted by knocking down the expression of R3HDM1 in two lung adenocarcinoma cell lines using small interfering RNA. The biological role of R3HDM1 in pan-cancer was explored using the GSEA method. Multiple immune infiltration algorithms from the TIMER2.0 database was employed to investigate the correlation between R3HDM1 expression and the tumor immune microenvironment. Validation of transcriptome immune infiltration was based on 140 single-cell datasets from the TISCH database. The study also characterized a pan-cancer survival profile and analyzed the differential expression of R3HDM1 in different molecular subtypes. The relationship between R3HDM1 and drug resistance was investigated using four chemotherapy data sources: CellMiner, GDSC, CTRP and PRISM. The impact of chemicals on the expression of R3HDM1 was explored through the CTD database. The study revealed differential expression of R3HDM1 in various tumors, indicating its potential as an early diagnostic marker. Changes in somatic copy number (SCNA) and DNA methylation were identified as factors contributing to abnormal expression levels. Additionally, the study found that R3HDM1 expression is associated with clinical features, metabolic pathways, and important pathways related to metastasis and the immune system. High expression of R3HDM1 was linked to poor prognosis across different tumors and altered drug sensitivity. Furthermore, the expression of R3HDM1 showed significant correlations with immune modulatory molecules and biomarkers of lymphocyte subpopulation infiltration. Finally, the study highlighted four chemicals that could influence the expression of R3HDM1. Overall, this study proposes that R3HDM1 expression is a promising biomarker for predicting the prognosis of cancer, especially lung adenocarcinoma, and the efficacy of immunotherapy, demonstrating the rationale for further exploration in the development of anti-tumor therapies.

A comprehensive landscape analysis of autophagy in cancer development and drug resistance.

Autophagy plays important roles in cancer progression and therapeutic resistance, and the autophagy underlying the tumor pathogenesis and further mechanisms of chemoresistance emergence remains unknown. In this study, via the single-sample gene set enrichment analysis (ssGSEA) method, an autophagy 45-gene list was identified to evaluate samples' autophagy activity, verified through six GEO datasets with a confirmed autophagy phenotype. It was further utilized to distinguish tumors into autophagy score-high and score-low subtypes, and analyze their transcriptome landscapes, including survival analysis, correlation analysis of autophagy- and resistance-related genes, biological functional enrichment, and immune- and hypoxia-related and genomic heterogeneity comparison, in TCGA pan-cancer datasets. Furthermore, we performed an analysis of autophagy status in breast cancer chemoresistance combined with multiple GEO datasets and in vitro experiments to validate the mechanisms of potential anticancer drugs for reversing chemoresistance, including CCK-8 cell viability assays, RT-qPCR, and immunofluorescence. The 45-gene list was used to identify autophagy score-high and score-low subtypes and further analyze their multi-dimensional features. We demonstrated that cancer autophagy status correlated with significantly different prognoses, molecular alterations, biological process activations, immunocyte infiltrations, hypoxia statuses, and specific mutational processes. The autophagy score-low subtype displayed a more favorable prognosis compared with the score-high subtype, associated with their immune-activated features, manifested as high immunocyte infiltration, including high CD8+T, Tfh, Treg, NK cells, and tumor-associated macrophages M1/M2. The autophagy score-low subtype also showed a high hypoxia score, and hypoxic tumors showed a significantly differential prognosis in different autophagy statuses. Therefore, "double-edged" cell fates triggered by autophagy might be closely correlated with the immune microenvironment and hypoxia induction. Results demonstrated that dysregulated autophagy was involved in many cancers and their therapeutic resistance and that the autophagy was induced by the resistance-reversing drug response, in five breast cancer GEO datasets and validated by in vitro experiments. In vitro, dihydroartemisinin and artesunate could reverse breast cancer doxorubicin resistance, through inducing autophagy via upregulating LC3B and ATG7. Our study provided a comprehensive landscape of the autophagy-related molecular and tumor microenvironment patterns for cancer progression and resistance, and highlighted the promising potential of drug-induced autophagy in the activation of drug sensitivity and reversal of resistance.

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

Evaluating trophinin associated protein as a biomarker of prognosis and therapy response in renal cell carcinoma

BackgroundTrophinin Associated Protein (TROAP) has been implicated in some tumors, yet its role in renal cell carcinoma (RCC) remains underexplored. This study aims to elucidate the prognostic and therapeutic implications of TROAP in RCC, encompassing different subtypes.MethodsFirstly, we identified the expression patterns of TROAP across various tumors within the TCGA pan-cancer cohort. Subsequently, the prognostic significance of TROAP was validated in three TCGA RCC cohorts and a local cohort. Finally, we conducted functional enrichment analysis, somatic mutations and copy number variations, assessed therapeutic response cohorts, and performed in vitro experiments to explore the biological characteristics of TROAP.ResultsTROAP serves as an unfavorable factor in both the TCGA RCC datasets and our local cohort. Functional enrichment analysis and in vitro experiments have demonstrated its oncogene effect in promoting tumor progression. Additionally, the relationship between TROAP expression and gene mutations in RCC appears to be limited. Furthermore, elevated TROAP expression is associated with reduced efficacy of RCC therapies, including nivolumab and everolimus.ConclusionsOur findings illustrate TROAP as a pivotal biomarker for prognosis and therapeutic response in RCC. Elevated TROAP expression is indicative of aggressive tumor behavior and resistance to conventional therapies, making it a valuable target for personalized treatment strategies in RCC management.

A pan-cancer cuproptosis signature predicting immunotherapy response and prognosis

BackgroundCuproptosis may represent a potential biomarker for predicting prognosis and immunotherapy response, but the available evidence is insufficient. MethodsThe multiple single-cell RNA sequencing (scRNA-seq) datasets were analyzed to investigate the specific occurrence of cuproptosis in distinct cell populations. Utilizing 28 scRNA-seq datasets, TCGA pan-cancer cohort, and 10 immunotherapy cohorts, we developed a cuproptosis signature (Cup.Sig). This signature was used to construct prediction models for immunotherapy response and identify potential prognostic biomarkers for pan-cancer using 11 different machine learning algorithms. ResultsMalignant cells demonstrate the higher cuproptosis scores in comparison to other cell types across diverse cancer types. The Cup.Sig exhibits significant associations with cancer hallmarks and immune cell response in multiple cancer types. Leveraging the Cup.Sig, the robust pan-cancer immunotherapy prediction model and prognostic biomarker have been established and validated using diverse datasets from various platforms. ConclusionsWe developed a pan-cancer cuproptosis signature for predicting survival and immunotherapy response.

Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets and assess their predictive power for survival and gene essentiality predictions. We demonstrate that baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pretraining are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

The role of distinct APOBEC/ADAR mRNA levels in mutational signatures linked to aging and ultraviolet radiation

The APOBEC/AID family is known for its mutator activity, and recent evidence also supports the potential impact of ADARs. Furthermore, the mutator impacts of APOBEC/ADAR mutations have not yet been investigated. Assessment of pancancer TCGA exomes identified enriched somatic variants among exomes with nonsynonymous APOBEC1, APOBEC3B, APOBEC3C, ADAR, and ADARB1 mutations, compared to exomes with synonymous ones. Principal component (PC) analysis reduced the number of potential players to eight in cancer exomes/genomes, and to five in cancer types. Multivariate regression analysis was used to assess the impact of the PCs on each COSMIC mutational signature among pancancer exomes/genomes and particular cancers, identifying several novel links, including SBS17b, SBS18, and ID7 mainly determined by APOBEC1 mRNA levels; SBS40, ID1, and ID2 by age; SBS3 and SBS16 by APOBEC3A/APOBEC3B mRNA levels; ID5 and DBS9 by DNA repair/replication (DRR) defects; and SBS7a-d, SBS38, ID4, ID8, ID13, and DBS1 by ultraviolet (UV) radiation/ADARB1 mRNA levels. APOBEC/ADAR mutations appeared to potentiate the impact of DRR defects on several mutational signatures, and some factors seemed to inversely affect certain signatures. These findings potentially implicate certain APOBEC/ADAR mutations/mRNA levels in distinct mutational signatures, particularly APOBEC1 mRNA levels in aging-related signatures and ADARB1 mRNA levels in UV radiation-related signatures.

Loss of CELF2 promotes skin tumorigenesis and increases drug resistance.

CELF2 belongs to the CELF RNA-binding protein family and exhibits antitumor activity in various tumor models. Analysis of the pan-cancer TCGA database reveals that CELF2 expression strongly correlates with favorable prognosis among cancer patients. The function of CELF2 in nonmelanoma skin cancer has not been studied. We used shRNA-mediated knockdown (KD) of CELF2 expression in human squamous cell carcinoma (SCC) cells to investigate how CELF2 impacted SCC cell proliferation, survival, and xenograft tumor growth. We determined CELF2 expression in human SCC tissues and adjacent normal skin using immunofluorescence staining. Additionally, we investigated the changes in CELF2 and its target gene expression during UV-induced and chemical-induced skin tumorigenesis by western blotting. CELF2 KD significantly increased SCC cell proliferation, colony growth, and SCC xenograft tumor growth in immunodeficient mice. CELF2 KD in SCC cells led to activation of KRT80 and GDF15, which can potentially promote cell proliferation and tumor growth. While control SCC cells were sensitive to anticancer drugs such as doxorubicin, SCC cells with CELF2 KD became resistant to drug-induced tumor growth retardation. Finally, we found CELF2 expression diminished during both UV- and chemical-induced skin tumorigenesis in mice, consistent with reduced CELF2 expression in human SCC tumors compared to adjacent normal skin. This study shows for the first time that CELF2 loss occurs during skin tumorigenesis and increases drug resistance in SCC cells, highlighting the possibility of targeting CELF2-regulated pathways in skin cancer prevention and therapies.

Overactivation of XBP1 in plasma cells implies worse survival through innate immunity in esophageal squamous cell carcinoma

To maintain protein homeostasis, X-box binding protein 1 (XBP1) undergoes splicing following the activation of the unfolded protein response (UPR) in response to endoplasmic reticulum (ER) stress. Although targeting ER stress represents a promising therapeutic strategy, a comprehensive understanding of XBP1 at the cellular level and the link between XBP1 and the innate nervous system is lacking. Here, TCGA pancancer datasets from 33 cancer types, scRNA pancancer datasets from 454 patients and bulk RNA-seq datasets from 155 paired esophageal squamous cell carcinoma (ESCC) patients were analyzed. To cope with ER stress, plasma cells tend to activate XBP1 after undergoing bacterial infection and inflammatory signaling from the innate immune system. Patients with high XBP1 expression in their plasma cells have a higher tumor grade and worse survival. However, activation of the innate immune system with increased XBP1 expression in plasma cells correlates with an increased lymphocyte ratio, indicative of a more robust immune response. Moreover, XBP1 activation appears to initiate leukocyte migration at the transcriptional level. Our study revealed that the XBP1-induced UPR could mediate the crosstalk between optimal acquired humoral immune responses and innate immunity in ESCC.

Unveiling the RKIP and EGFR Inverse Relationship in Solid Tumors: A Case Study in Cervical Cancer.

Raf Kinase Inhibitor Protein (RKIP) is recognized as a bona fide tumor suppressor gene, and its diminished expression or loss is associated with the progression and poor prognosis of various solid tumors. It exerts multifaceted roles in carcinogenesis by modulating diverse intracellular signaling pathways, including those governed by HER receptors such as MAPK. Given the significance of HER receptor overexpression in numerous tumor types, we investigated the potential oncogenic relationship between RKIP and HER receptors in solid tumors. Through a comprehensive in silico analysis of 30 TCGA PanCancer Atlas studies encompassing solid tumors (10,719 samples), we uncovered compelling evidence of an inverse correlation between RKIP and EGFR expression in solid tumors observed in 25 out of 30 studies. Conversely, a predominantly positive association was noted for the other HER receptors (ERBB2, ERBB3, and ERBB4). In particular, cervical cancer (CC) emerged as a tumor type exhibiting a robust inverse association between RKIP and EGFR expression, a finding that was further validated in a cohort of 202 patient samples. Subsequent in vitro experiments involving pharmacological and genetic modulation of EGFR and RKIP showed that RKIP depletion led to significant upregulation of EGFR mRNA levels and induction of EGFR phosphorylation. Conversely, EGFR overactivation decreased RKIP expression in CC cell lines. Additionally, we identified a common molecular signature among patients depicting low RKIP and high EGFR expression and demonstrated the prognostic value of this inverse correlation in CC patients. In conclusion, our findings reveal an inverse association between RKIP and EGFR expression across various solid tumors, shedding new light on the underlying molecular mechanisms contributing to the aggressive phenotype associated with RKIP and EGFR in cervical cancer.

Stem cell index-based RiskScore model for predicting prognosis in thyroid cancer and experimental verification

ObjectiveAn mRNA expression-based stemness index (mRNAsi) has been developed to characterize cancer stemness. However, the predictive value of mRNAsi-based signature in therapeutic resistance and immunotherapy in thyroid cancer (THCA) remains unclarified. This study evaluated and validated the role of mRNAsi in drug sensitivity, its relationship between mRNAsi and THCA clinical features and immunity based on bioinformatics. MethodsBased on transcriptome data of THCA patients from the Tumor Genome Atlas Project (TCGA) database, and expression data of multifunctional stem cell samples from the Progenitor Cell Biology Consortium (PCBC) databases, mRNAsi was calculated by the " one class logistic regression (OCLR)" method, Molecular subtypes of TCGA-THCA samples were identified with mRNAsi-related genes using ConsensusClusterPlus method. The gene mutation, clinical characteristics, immune characteristics, TIDE and drug sensitivity were compared among molecular subtypes. A prognostic model was designed with Lasso cox method. Modulation of malignant phenotype of THCA cell lines by model characterization genes is validated by CCK-8, flow cytometry. DNA methylation disorder in promoter region was analyzed between risk groups. The model was validated for survival in the internal Test dataset, while TCGA pan-cancer and immunotherapy datasets were further employed to validate the performance of this model. ResultsWe obtained a total of 78 stem cell samples, each containing the expression profile of 8087 mRNA genes. Based on mRNAsi, THCA was divided into 3 subtypes. Subtype C2 had the poorest prognosis and highest immune score, while subtype C3 had the best prognosis, lowest mRANsi and highest TIDE score. Patients in subtype C2 showed higher sensitivity to Cisplatin, Erlotinib, Paclitaxel, and Lapatinib. The prognostic signature was generated using 5 mRNAsi-related genes, which could predict prognosis for THCA. qRT-PCR results showed that the expression of 5 genes were various in Hth7 and KTC-1 cells, and inhibition CELSR3 expression increased percentage of apoptosis in Hth7 and KTC-1 cells. mRNAsi related DNA methylation sites were mainly enriched in tumor related pathways. Good performance of this model was validated in Test dataset, pan-cancer and immunotherapy datasets. ConclusionThis study identified three subtypes for classification and developed a prognostic model with mRNAsi-related genes, which provided great potential for prognosis and immunotherapy prediction.

Bioinformatics analysis and experimental verification of the cancer-promoting effect of DHODH in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the urinary system. To explore the potential mechanisms of DHODH in ccRCC, we analyzed its molecular characteristics using public databases. TCGA pan-cancer dataset was used to analyze DHODH expression in different cancer types and TCGA ccRCC dataset was used to assess differential expression, prognosis correlation, immune infiltration, single-gene, and functional enrichment due to DHODH. The GSCALite and CellMiner databases were employed to explore drugs and perform molecular docking analysis with DHODH. Protein–protein interaction networks and ceRNA regulatory networks of DHODH were constructed using multiple databases. The effect of DHODH on ccRCC was confirmed in vitro. DHODH was highly expressed in ccRCC. Immune infiltration analysis revealed that DHODH may be involved in regulating the infiltration of immunosuppressive cells such as Tregs. Notably, DHODH influenced ccRCC progression by forming regulatory networks with molecules, such as hsa-miR-26b-5p and UMPS and significantly enhanced the malignant characteristics of ccRCC cells. Several drugs, such as lapatinib, silmitasertib, itraconazole, and dasatinib, were sensitive to DHODH expression and exhibited strong molecular binding with it. Thus, DHODH may promote ccRCC progression and is a candidate effective therapeutic target for ccRCC.

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks.

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets.