Significant Gene Sets Research Articles

A new gene signature associated with pyroptosis for identifying high-risk myeloma patients

Multiple myeloma (MM) is a complicated hematologic malignancy of plasma cells. However, the existing stratification systems cannot accurately predict the prognosis of MM. This study aims to evaluate the role of pyroptosis in identifying high-risk MM patients. RNA expression profiles were obtained from the Cancer Genome Atlas-MMRF CoMMpass and GTEx databases, which were treated as MM cases and controls, respectively. By applying univariate Cox regression analysis and consensus clustering, 20 pyroptosis-related genes (PRGs) were initially identified to effectively stratify MM patients into two distinct subgroups. To identify prognostic gene signature, a stepwise LASSO regression analysis was conducted following by univariate and multivariate Cox regression analyses. We identified a set of signature genes – CASP3, CHMP2A, CHMP3, CHMP6, GZMB, CASP8, NOD2, PLCG1, and FOXO3 – that could significantly distinguish MM patients based on overall survival. We further identified that the risk score can serve as an independent prognostic indicator. The same prognostic model was also successfully constructed in the internal test cohort. Thus, a prognostic risk model for clinically predicting the survival rate of MM patients was established. Single-sample gene set enrichment analysis was employed to analyze immune infiltrating cells and immune-related pathways between two risk groups. Moreover, the mRNA expression levels of the prognostic risk signature genes were confirmed by quantitative reverse-transcription polymerase chain reaction in MM cells treated with the pyroptosis-inducing agent etoposide. In conclusion, we identified a 9-PRG signature that enables effective stratification of MM patients and serves as an independent prognostic marker. These findings underscore the need for further exploration of pyroptosis in MM therapy.

Modulation of SRC by SNTB1 activates the Hippo-YAP pathway during colon adenocarcinoma metastasis

BackgroundColon adenocarcinoma (COAD) ranks as the third most prevalent and lethal cancer in 2020, with metastasis being the primary cause of cancer-related mortality. A comprehensive understanding of the mechanism underlying distant metastasis is imperative for enhancing the prognosis and quality of life of patients with COAD.MethodsThis study employed gene set enrichment analysis (GSEA) on RNA-sequencing data from 408 patients with COAD in The Cancer Genome Atlas (TCGA) database. GSEA analysis was applied to find the significant hallmark gene set, and genes in the significant hallmark gene set was performed by univariate Cox regression to select the key gene. Then, multivariate Cox regression model was constructed. And various databases were utilized to validate and find the significant oncoprotein and hallmark gene set of the key gene. In addition, the expression of key regulators in para-carcinoma tissue, colon cancer and distant metastases samples were detected by real-time PCR, Immunohistochemistry and Western blot. Additionally, the biological functions were examined by in vitro and in vivo experiments. The scRNA-seq and CellphoneDB were performed to explore cell characters in COAD and the potential mechanism of metastasis.ResultsThe regulatory network analysis revealed SRC/YAP1 as the most significant oncoprotein and signature gene set associated with SNTB1. Moreover, significant SNTB1 overexpression in COAD metastatic tissues was observed compared to para-carcinoma and primary COAD tissues. Co-immunoprecipitation assays demonstrated the formation of a complex between SNTB1 and SRC proteins. Furthermore, the overexpression of SNTB1 enhanced the proliferation, migration and invasion capacities of COAD cell lines. Caudal vein injection of COAD cells overexpressing SNTB1 in nude mice resulted in increased tumour growth and metastasis to the lung, liver and bone. Finally, single cell RNA-seq revealed alterations in the cellular subtypes of COAD, and CellphoneDB indicated that the interaction between cancer cells exhibiting high SNTB1 expression and enterocytes promoted EMT through cellular communications involving TGF-β, accelerating metastasis in COAD.ConclusionThis study postulates that SNTB1 interacts with SRC to activate the Hippo-YAP pathway, thereby promoting COAD metastasis. Furthermore, cellular communication with enterocytes promotes EMT, facilitating metastasis. These findings propose novel therapeutic targets for preventing or treating metastatic COAD.

CNSC-52. OLIGODENDROGLIOMA SPATIAL TRANSCRIPTOMIC SEQUENCING REVEALS THERAPY-DEPENDENT EVOLUTION OVER TIME

Abstract Oligodendroglioma evolution is incompletely understood. Here we analyze serial oligodendroglioma samples to define genomic and cellular mechanisms underlying treatment resistance. RNA sequencing was performed on 53,406 spatial transcriptomes from 31 oligodendrogliomas (IDH mutant, 1p/19q-codeleted) that were resected from 16 patients. There were 15 CNS WHO grade 2 and 16 grade 3 oligodendrogliomas, which were stratified for temporal progression across grades (n=5 grade 2 to 2, n=5 grade 2 to 3, n=6 grade 3 to 3) and treatments between serial resections (n=2 TMZ/RT, n=2 TMZ, n=1 mTOR inhibition, n=1 IDH inhibition, n=10 surgical monotherapy). Spatial transcriptomic clusters were defined using cell signature gene sets, cell cycle analysis, differentially expressed marker genes, immunohistochemistry, spatial copy number alterations (CNAs), and deconvolution of 18,628 oligodendroglioma single-cell transcriptomes from 16 independent patients. Specificity was tested using immunofluorescence on independent oligodendrogliomas that did not recur after surgical monotherapy (n=8 samples, median follow-up 8.4 years) and spatial transcriptomic sequencing on 8 matched-paired astrocytomas from 4 patients. Integrated uniform manifold approximation and projection revealed a proneural/NPC-like cluster that was enriched in grade 3 oligodendroglioma samples and represented the starting point in oligodendroglioma lineage differentiation using RNA velocity, Monocle, and subclonal spatiotemporal CNA evolution. Comparison across treatment regimens showed (1) enrichment of the proneural/NPC-like cluster in recurrent versus primary tumors that were treated with surgical monotherapy, (2) enrichment of a proneural/OPC-like cluster and depletion of an oligodendrocyte-like cluster after TMZ/RT, (3) enrichment of proneural/NPC-like and immediate-early response clusters after TMZ/RT, (4) enrichment of the oligodendrocyte-like cluster following mTOR inhibition, and (5) enrichment of cancer associated fibroblasts (CAFs) following IDH inhibition. These data show the spatiotemporal landscape of oligodendroglioma is associated with branched lineage differentiation that is modified by postoperative therapy, and suggest that therapy-dependent druggable dependencies may exist to treat oligodendrogliomas that are resistant to standard interventions.

CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

Macrophages on the run: Exercise balances macrophage polarization for improved health

ObjectiveExercise plays a crucial role in maintaining and improving human health. However, the precise molecular mechanisms that govern the body’s response to exercise or/compared to periods of inactivity remain elusive. Current evidence appears to suggest that exercise exerts a seemingly dual influence on macrophage polarization states, inducing both pro-immune response M1 activation and cell-repair-focused M2 activation. To reconcile this apparent paradox, we leveraged a comprehensive meta-analysis of 75 diverse exercise and immobilization published datasets (7000+ samples), encompassing various exercise modalities, sampling techniques, and species. Methods75 exercise and immobilization expression datasets were identified and processed for analysis. The data was analyzed using boolean relationships which uses binary gene expression relationships in order to increase the signal to noise achieved from the data, allowing for the use of comparison across such a diverse set of datasets. We utilized a boolean relationship-aided macrophage gene model [1], to model the macrophage polarization state in pre and post exercise samples in both immediate exercise and long term training. ResultsOur modeling uncovered a key temporal dynamic: exercise triggers an immediate M1 surge, while long term training transitions to sustained M2 activation. These patterns were consistent across different species (human vs mouse), sampling methods (blood vs muscle biopsy), and exercise type (resistance vs endurance), and routinely showed statistically significant results. Immobilization was shown to have the opposite effect of exercise by triggering an immediate M2 activation. Individual characteristics like gender, exercise intensity and age were found to impact the degree of polarization without changing the overall patterns. To model macrophages within the specific context of muscle tissue, we identified a focused gene set signature of muscle resident macrophage polarization, allowing for the precise measurement of macrophage activity in response to exercise within the muscle. ConclusionsThese consistent patterns across all 75 examined studies suggest that the long term health benefits of exercise stem from its ability to orchestrate a balanced and temporally-regulated interplay between pro-immune response (M1) and reparative macrophage activity (M2). Similarly, it suggests that an imbalance between pro-immune and cell repair responses could facilitate disease development. Our findings shed light on the intricate molecular choreography behind exercise-induced health benefits with a particular insight on its effect on the macrophages within the muscle.

Retrospective study assessing the role of theandrogen receptor in clear cell renal cell cancer patients treated with VEGFR inhibitors in monotherapy.

Despite that incorporating antiangiogenic in combination with immune-checkpoint inhibitors as the standard first-line treatment for advanced clear cell renal cell cancer (ccRCC) yields promising outcomes, these regimens often lead to significant toxicity. However, a subgroup of patients has shown responsiveness to VEGFR tyrosine-kinase inhibitors (TKIs) in monotherapy, leading tothe question ofwhether employing combination therapies can significantly enhance overall survival in all patients over monotherapy. Thus, we aim to identify gene expression signatures that can predict TKI response within subpopulations that might benefit from single-agent therapies, to minimize unnecessary exposure to combination therapies and their associated toxicities, as well as to discover new potential therapeutic targets to improve ccRCC treatment. Based on prior data, the androgen receptor (AR) might meet both conditions. We evaluated the association between AR expression, assessed through NanoString® technology-derived mRNA counts, and the clinical outcomes of 98 ccRCC patients treated with first-line antiangiogenics and determined its association with other genes implicated in ccRCC tumorigenesis. Higher AR-expression correlates significantly with better prognosis and survival based on the MSKCC risk score, and longer PFS. Furthermore, we have identified a gene set signature associated with AR-overexpression and several genes involved in angiogenesis and transcriptional targets of the hypoxia-inducible factor, a cornerstone of ccRCC. AR-overexpression and its association with other genes could favor a transcriptomic signature set to aidin identifying patients suitable for TKI in monotherapy, rather than aggressive combinations, enhancing thus, precision and personalized therapeutic decisions.

Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study.

Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes.

Identification and validation of a novel five-gene signature in high-risk MYCN-not-amplified neuroblastoma

ObjectiveHigh-risk neuroblastoma patients often have poor outcomes despite multi-treatment options. The risk stratification of high-risk MYCN-not-amplified (HR-MYCN-NA) patients remains difficult. This study aims to identify a gene set signature that can help further stratify HR-MYCN-NA patients for a potential personalized therapeutic strategy.MethodsThree microarrays and one single-cell RNA sequence dataset were acquired and analyzed. Firstly, the prognostic-related genes (PRGs) in HR-MYCN-NA tumor cells were identified using TARGET-NB and GSE137804 datasets. Then, the prognostic model was established by LASSO-Cox regression, and verified in external cohort (GSE49710, GSE45547). Moreover, a time-dependent receiver operating characteristic curve (ROC) and area under the ROC (AUC) was used to assess survival prediction. A nomogram was established to predict the 1-, 3- and 5-year overall survival (OS) of HR-MYCN-NA patients.ResultsIn the training set, a five-PRGs signature, which include GAL, GFRA3, MARCKS, PSMD13, and ZNHIT3 genes, was identified and successfully stratified HR-MYCN-NA patients into ultra-high risk (UHR) and high-risk (HR) subtypes (HR = 4.29, P < 0.001). ROC curve analysis confirmed its predictive power (AUC = 0.74–0.82), suggesting a good predictive efficacy. Consistently, high-risk scores also predicted worse OS (HR = 2, P = 0.033) in the external validation dataset (AUC = 0.67–0.71). Moreover, the overall C-index of the nomogram was 0.75 (P < 0.001), which indicated good agreement between the observed and predicted survival rates. Further integrating the five PRGs signature with clinical factors, these 5 gene signature (HR = 4.45, P < 0.001) and tumor grade (HR = 4.15, P = 0.02) were found to be independent prognostic factors for HR-MYCN-NA patients.ConclusionThe novel five PRGs signature could well predict the survival of HR-MYCN-NA patients, which may provide constructive information for these subsets.

Genetic factors and symptom dimensions associated with antidepressant treatment outcomes: clues for new potential therapeutic targets?

Treatment response and resistance in major depressive disorder (MDD) show a significant genetic component, but previous studies had limited power also due to MDD heterogeneity. This literature review focuses on the genetic factors associated with treatment outcomes in MDD, exploring their overlap with those associated with clinically relevant symptom dimensions. We searched PubMed for: (1) genome-wide association studies (GWASs) or whole exome sequencing studies (WESs) that investigated efficacy outcomes in MDD; (2) studies examining the association between MDD treatment outcomes and specific depressive symptom dimensions; and (3) GWASs of the identified symptom dimensions. We identified 13 GWASs and one WES of treatment outcomes in MDD, reporting several significant loci, genes, and gene sets involved in gene expression, immune system regulation, synaptic transmission and plasticity, neurogenesis and differentiation. Nine symptom dimensions were associated with poor treatment outcomes and studied by previous GWASs (anxiety, neuroticism, anhedonia, cognitive functioning, melancholia, suicide attempt, psychosis, sleep, sociability). Four genes were associated with both treatment outcomes and these symptom dimensions: CGREF1 (anxiety); MCHR1 (neuroticism); FTO and NRXN3 (sleep). Other overlapping signals were found when considering genes suggestively associated with treatment outcomes. Genetic studies of treatment outcomes showed convergence at the level of biological processes, despite no replication at gene or variant level. The genetic signals overlapping with symptom dimensions of interest may point to shared biological mechanisms and potential targets for new treatments tailored to the individual patient's clinical profile.

Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response.

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels. This resource integrates eight robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five distinct aspects: identifying the signature genes of cancer stemness; exploring the associations with cancer hallmarks and cellular states; exploring the associations with immune response and the communications with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding downstream functional interpretation, including immune response and cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

Cellular heterogeneity and key subsets of tissue-resident memory T cells in cervical cancer

Tissue-resident memory T cells (TRMs) play a critical role in cancer immunity by offering quick and effective immune responses. However, the cellular heterogeneity of TRMs and their significance in cervical cancer (CC) remain unknown. In this study, we generated and analyzed single-cell RNA sequencing data from 12,945 TRMs (ITGAE+CD3D+) and 25,627 non-TRMs (ITGAE−CD3D+), derived from 11 CC tissues and 5 normal cervical tissues. We found that TRMs were more immunoreactive than non-TRMs, and TRMs in CC tissues were more activated than those in normal cervical tissues. Six CD8+ TRM subclusters and one CD4+ TRM subcluster were identified. Among them, CXCL13+ CD8+ TRMs were more abundant in CC tissues than in normal cervical tissues, had both cytotoxic and inhibitory features, and were enriched in pathways related to defense responses to the virus. Meanwhile, PLAC8+ CD8+ TRMs were less abundant in CC tissues than in normal cervical tissues but had highly cytotoxic features. The signature gene set scores of both cell subclusters were positively correlated with the overall survival and progression-free survival of patients with CC following radiotherapy. Of note, the association between HLA-E and NKG2A, either alone or in a complex with CD94, was enriched in CXCL13+ CD8+ TRMs interacting with epithelial cells at CC tissues. The in-depth characterization of TRMs heterogeneity in the microenvironment of CC could have important implications for advancing treatment and improving the prognosis of patients with CC.

Single-cell RNA sequencing uncovers cellular heterogeneity and the progression of heterotopic ossification of the elbow.

Heterotopic ossification of the elbow (HOE) is a complicated pathologic process characterized by extra bone formation in the elbow. Bone formation is a complex developmental process involving the differentiation of mesenchymal stem cells into osteoblasts. The aim of this study was to explore the cellular origin and progression of HOE by single-cell RNA sequencing. We identified 13 clusters of cells in HOE and further analyzed the subclusters for 4 of the main cell types. Six subclusters of osteoblasts, nine subclusters of chondrocytes, six subclusters of fibroblasts, and five subclusters of mononuclear phagocytes (MPs) were identified and analyzed. The new findings on osterix (OSX) and SOX9 expression in osteoblast subclusters and chondrocyte subclusters indicate that HOE is mediated through endochondral ossification. Further identification of the corresponding signature gene sets of distinct subclusters indicated that subclusters of osteoblasts_3, osteoblasts_4, osteoblasts_5, and osteoblasts_6 are relatively more mature during the osteoblastic progression of HOE. The trajectory analysis of the osteoblasts demonstrated that some genes were gradually downregulated, such as CRYAB, CCL3, SFRP4, WIF1, and IGFBP3, while other critical genes were upregulated, such as VCAN, IGFBP4, FSTL1, POSTN, MDK, THBS2, and ALPL, suggesting that these factors may participate in HOE progression. Cell-cell communication networks revealed extensive molecular interactions among the 13 HOE clusters. Ligand-receptor pairs for IL6, COL24A1, COL22A1, VWF, FZD6, FGF2, and NOTCH1 were identified, suggesting that multiple signaling pathways may be involved in HOE. In conclusion, this study provided the cellular atlas for HOE. We have established a greater extent of the heterogeneity of HOE cells than previously known through transcriptomic analysis at the single-cell level. We have observed gradual patterns of signature gene expression during the differentiation and maturation progression of osteoblasts from stem cells in HOE with higher resolution. The cell heterogeneity of HOE deserves further investigation to pave the way for identification of potential targets for HOE early diagnosis and therapeutic treatment.

Identification of hub genes associated with stripe rust disease in wheat through integrative transcriptome and gene-based association study

Stripe rust poses a severe menace to wheat cultivation worldwide, occurring as the most devastating disease threatening global wheat industry. The use of resistance genes has been considered as an effective method for management of rust diseases. However, for most of yellow rust (Yr) genes, the molecular mechanisms and pathways responsible for resistance response against Puccinia striiformis (Pst) infection are not well understood. In this study, transcriptome analysis was conducted in two cultivars viz. PBW343 (susceptible) and FLW30 (resistant) to the pathotype 46S119 of Pst in India. The inoculation with Pst pathotype 46S119 was followed by leaf sample collection at different hours per inoculation (hpi) viz.12, 48, and 72. The transcriptome analysis revealed a total of 18,482 differentially expressed genes (DEGs) across different time points. However, a set of 159 genes were identified to exhibit a genome-wide association with P ≤ 0.05 level of significance, which can be represented as Nominally Significant GWAS Geneset (NSGG). By utilizing Weighted Gene Co-expression Network Analysis (WGCNA), we identified 924 and 79 genes from the DEGs and NSGG dataset belonging to different modules. Functional validation of genes was done to confirm the transcriptional expression of four potential genes, including two hub genes associated with the defence mechanism against rust infection.

Development of a novel prognostic signature for colorectal cancer based on angiogenesis-related genes

BackgroundColorectal cancer (CRC) is the third most common malignant tumor worldwide. Angiogenesis is closely related to tumor metastasis, which is the main cause of cancer death. Although several angiogenesis signatures have been proposed in some cancer types, no angiogenic signature has been developed to predict the prognosis and efficacy of antiangiogenic bevacizumab in CRC patients. MethodsWe developed a novel CRC angiogenic signature by refining seven publicly available angiogenic gene sets using least absolute shrinkage and selection operator (LASSO). Immune and stromal cells within the tumor microenvironment were compared between the high- and low-risk groups in more than 1000 CRC samples classified by calculating the risk score based on the customized angiogenic signature. The correlation of this new gene set with the efficacy of bevacizumab was also compared. ResultsA new prognostic-associated angiogenesis signature gene set was constructed that can divide CRC patients into two high- and low-risk groups. The high-risk angiogenic group was significantly associated with extracellular matrix organization, epithelial-mesenchymal transition (EMT), and myogenesis. In addition, the high-risk group had higher infiltration of stromal and immune cells and was more resistant to bevacizumab than the low-risk group. ConclusionBriefly, we constructed a novel angiogenic signature that can predict the prognosis of CRC patients and the efficacy of bevacizumab in treating CRC. Our results provide new insights into the relationships among angiogenesis, metastasis, and medication for CRC.

Unsupervised Analysis Reveals the Involvement of Key Immune Response Genes and the Matrisome in Resistance to BRAF and MEK Inhibitors in Melanoma.

Melanoma tumors exhibit a wide range of heterogeneity in genomics even with shared mutations in the MAPK pathway, including BRAF mutations. Consistently, adaptive drug resistance to BRAF inhibitors and/or BRAF plus MEK inhibitors also exhibits a wide range of heterogeneous responses, which poses an obstacle for discovering common genes and pathways that can be used in clinic for overcoming drug resistance. This study objectively analyzed two sets of previously published tumor genomics data comparing pre-treated melanoma tumors and BRAFi- and/or MEKi-resistant tumors. Heterogeneity in response to BRAFi and BRAFi/MEKi was evident because the pre-treated tumors and resistant tumors did not exhibit a tendency of clustering together. Differentially expressed gene (DEG) analysis revealed eight genes and two related enriched signature gene sets (matrisome and matrisome-associated signature gene sets) shared by both sets of data. The matrisome was closely related to the tumor microenvironment and immune response, and five out of the eight shared genes were also related to immune response. The PLXNC1 gene links the shared gene set and the enriched signature gene sets as it presented in all analysis results. As the PLXNC1 gene was up-regulated in the resistant tumors, we validated the up-regulation of this gene in a laboratory using vemurafenib-resistant cell lines. Given its role in promoting inflammation, this study suggests that resistant tumors exhibit an inflammatory tumor microenvironment. The involvement of the matrisome and the specific set of immune genes identified in this study may provide new opportunities for developing future therapeutic methods.

STEAP2 promotes hepatocellular carcinoma progression via increased copper levels and stress-activated MAP kinase activity

Six Transmembrane Epithelial Antigen of Prostate 2 (STEAP2) belongs to a family of metalloreductases, which indirectly aid in uptake of iron and copper ions. Its role in hepatocellular carcinoma (HCC) remains to be characterized. Here, we report that STEAP2 expression was upregulated in HCC tumors compared with paired adjacent non-tumor tissues by RNA sequencing, RT-qPCR, Western blotting, and immunostaining. Public HCC datasets demonstrated upregulated STEAP2 expression in HCC and positive association with tumor grade. Transient and stable knockdown (KD) of STEAP2 in HCC cell lines abrogated their malignant phenotypes in vitro and in vivo, while STEAP2 overexpression showed opposite effects. STEAP2 KD in HCC cells led to significant alteration of genes associated with extracellular matrix organization, cell adhesion/chemotaxis, negative enrichment of an invasiveness signature gene set, and inhibition of cell migration/invasion. STEAP2 KD reduced intracellular copper levels and activation of stress-activated MAP kinases including p38 and JNK. Treatment with copper rescued the reduced HCC cell migration due to STEAP2 KD and activated p38 and JNK. Furthermore, treatment with p38 or JNK inhibitors significantly inhibited copper-mediated cell migration. Thus, STEAP2 plays a malignant-promoting role in HCC cells by driving migration/invasion via increased copper levels and MAP kinase activities. Our study uncovered a novel molecular mechanism contributing to HCC malignancy and a potential therapeutic target for HCC treatment.

Predicting epirubicin response in Danish patients with breast cancer.

539 Background: Anthracyclines are highly effective chemotherapy drugs. However, they are toxic and feared for their cardiotoxicity. There is a need for improved personalized prediction of treatment benefit. For example, state-of-the-art guidelines (NCCN Guidelines Version 1.2024 and meta-analysis PMID: 37061269) mark anthracyclines as optional adjuvant treatment of estrogen receptor (ER) positive, HER2 negative early high-risk breast cancer. A biomarker to predict whether a patient will benefit from anthracycline treatment could potentially lead to increased cure rate and overall reduced toxicity. However, no such test exists. Here we introduce a clinically validated, predictive test based on clinical tumor transcriptome data and the cell line panel NCI60 that can forecast epirubicin response in breast cancer patients with high accuracy. Methods: Microarray gene expression levels and growth inhibition levels (GI50) from the NCI60 cell line panel were acquired from NCBI to build a prediction algorithm. A tumor-specific variance-based filter based on &gt;6.000 patient tumors was applied to select the most relevant genes. AUC (Area Under Curve) was used to evaluate the performance of numerous configurations and combinations of parameters that led to different gene-signature sets. Response scores based on the gene expression level of all genes in each signature were generated for each sample in a published cohort (PMID: 23340299) and a final signature with 141 genes was the best-performing signature (Progressive Disease versus Response AUC 0.81 and ANOVA-test p-value &lt; 0.01). The model was validated on a cohort of Danish breast cancer patients treated with epirubicin in the metastatic setting (n=153), with time-to-progression (TTP) as endpoint (PMID: 31654283, PMID: 30099635). Finally, we used an established method (PMID: 30289602) to test if the gene signature was correlated between microarray- and RNA sequencing-based datasets. Results: The hazard ratio (HR) for the best-performing signature was 0.31 when comparing patients with a difference of at least 50 in response score (two-sided p-value = 0.012, 95% confidence interval 0.12-0.77, endpoint TTP, continuous scoring). This was consistent in a multivariate model including ER and HER2. Using the data and method provided by Pedersen et al (PMID: 30289602), we also saw a high correlation between matching microarray and RNA sequencing datasets for the genes in our model (mean Pearson’s r = 0.87). Conclusions: Here is presented a development and clinical validation of an accurate epirubicin response signature for breast cancer patients. The expression of the genes in the signature is highly correlated between microarray- and RNA sequencing data. Moving forward, we will seek to validate the model independently on RNA sequencing data. Our aim is that these models will be accepted for prospective clinical tests at local hospitals in Denmark in early 2025.

Exosome-delivered microRNAs confer adriamycin-resistance through modulating the immune and metabolism-related gene PTEN in HER2-negative breast cancer.

e12500 Background: Anthracycline is widely used in breast cancer (BCa) chemotherapy, but acquired drug resistance remains a challenge. Increasing evidence suggests that changes in the tumour immune microenvironment (TIME) consistently contribute to the development of chemoresistance. Methods: TIME scores and tumor-infiltrating immune cells (TICs) scores were used to investigate the prognosis, characteristics and gene transcriptomic profiling of HER2-negative BCa patients who received anthracycline chemotherapy. Exosomes from MCF/7-ADR (ADR-exos) and MCF/7-S cell lines were characterized. Dysregulated miRNAs were obtained by using miRNA microarray analysis on BCa cells and their corresponding exosomes. Protein profiling was performed to identify differentially expressed proteins in up- and down-regulated miR-222 cells. Ability of ADR-exos to transfer drug-resistance mediated by miR-222 was assessed by immunofluorescence assay and flow cytometry. Conditions to load miR-222 mimic or inhibitor into exosomes were optimized. Influence of miR-222 inhibitor-containing exosomes on the downstream pathway of miR-222 and the potential therapeutic effects were evaluated. Relationship between miR-222/PTEN and drug resistance in circulating exosomes from mice after doxorubicin chemotherapy was investigated. Results: Firstly, we discovered the complicated and heterogeneous TICs subtypes and their unique biological behaviors in HER2-negative BCa. Then, we identified 85 differentially expressed immunologic signature gene sets and 7202 corresponding immune-related genes in three subtypes quantified by gene set variation analysis. We identified 12 up-regulated and 13 down-regulated genes using miRNA microarray analysis in doxorubicin-resistant cells and their exosomes. Mass spectrometry found 45 differentially expressed proteins affected by miR-222. Moreover, we successfully established miRNA mimic/inhibitor-containing exosomes to explore the solo function of exosomal miR-222 in doxorubicin-resistance both in vitro and in vivo. For the first time, we detected the levels of miR-222 and its target gene PTEN in circulating exosomes from mice after doxorubicin-based chemotherapy and explored the potential clinical implications of miR-222/PTEN-containing exosomes in chemotherapy and immune suppression of BCa. Conclusions: Doxorubicin-resistant cells can transmit drug-resistance to sensitive cells via delivering miR-222 by modulating the immune- and metabolism-related gene PTEN without interference from other relevant drug-resistance factors in exosomes. Therefore, miR-222-containing exosomes and their target gene PTEN may be a promising biomarker for predicting chemotherapy efficacy and tumor immunity regulation in BCa patients receiving anthracycline chemotherapy.

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach.

Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

#1290 Adverse proinflammatory responses in the kidney after FGF23 administration in three murine kidney disease models

Abstract Background and Aims The hormone FGF23 is a biomarker associated with morbidity and mortality in individuals with and without kidney disease, but the role of FGF23 excess as a pathogenic factor is incompletely understood. Here, we investigated renal FGF23 signaling in kidney disease models. Method This study used three independent disease models: i) Male C57BL/6 mice (n = 16) were treated with Freud's adjuvant and nephrotoxic serum (NTS) to induce anti-glomerular basement membrane (anti-GBM) disease. ii) Female BALB/c mice (n = 8) were treated with adriamycin (doxorubicin) 10.5 mg/kg to induce focal and segmental glomerulosclerosis (FSGS). iii) Male DBA/2J mice (n = 20) were fed a diet containing adenine 0.2%. Control mice of the respective same strain and sex received vehicle injections. Mice of the anti-GBM and FSGS models and controls received intravenous injections of recombinant FGF23 1 µg or vehicle for six consecutive days (anti-GBM) or once (FSGS), with dissection 24 h after the last injection. Mice of the adenine model were dissected after 15 weeks. Following dissection, ex vivo precision-cut kidney slices (PCKS) were obtained from n = 4 mice and maintained for 24 at 37°C, 95% O2 and 5% CO2, followed by a 1 h treatment with recombinant FGF23 200 ng/ml or vehicle. Glomerular filtration rate (GFR) or renal retention parameters in serum and albuminuria were assessed. Kidneys underwent histological assessment using hematoxylin &amp; eosin or Masson's trichrome. Renal RNA was isolated for bulk RNAseq. Differential gene expression was assessed to determine the effect of FGF23 treatment in disease-free controls, in each disease model, and the interaction between FGF23 X disease effect for the anti-GBM and FSGS groups. Gene set enrichment analysis was performed with a focus on the immune system. Results Anti-GBM mice showed a 45 ± 27% GFR decline (mean ± SD) compared to baseline, whereas controls showed an GFR increase of 11 ± 36% (p &amp;lt; 0.01). Kidney histology revealed an interstitial infiltrate. The FSGS model showed elevated serum creatinine and albuminuria, and hypercellularity in some glomeruli. Mice of the adenine model showed progressively increased blood urea nitrogen concentrations starting after 2 weeks, up to a 3-fold increase at week 14. Histology revealed interstitial fibrosis and tubular atrophy. Bulk RNAseq revealed proinflammatory transcriptional signatures after FGF23 treatment in all disease models, regardless of which timepoint or model: In the anti-GBM model, Hallmark gene sets of inflammatory response, interferon-α response, interferon-β response and allograft rejection were significantly increased after 6 days of FGF23 treatment, whereas disease-free controls showed no significantly changed immune gene sets after 6 days of FGF23. In FSGS mice, none of the Hallmark gene sets were changed after 1 FGF23 injection, but top individual gene expression affected by 1 FGF23 injection were an increased proinflammatory transcription factor Cebpb and an increased DNA damage transcript Dclre1b. Finally, the PCKS of adenine model revealed no significant Hallmark gene sets, but the two genes with differential expression were a proinflammatory macrophage/fibroblast chemokine Ccl4 (increased) and an immunoglobulin heavy variable Ighv2-2 (decreased). Conclusion FGF23-driven patterns of proinflammatory gene expression were observed in three experimental models of kidney disease, both in vivo and ex vivo assessment of FGF23 signaling, thus excluding effects of FGF23 on other organs. As these findings were limited to transcriptional analyses, the present data warrant further confirmation and exploration of FGF23 as a pathogenic agent in kidney disease.