Classification of triple-negative breast cancers based on Immunogenomic profiling

A lactate-responsive gene signature predicts the prognosis and immunotherapeutic response of patients with triple-negative breast cancer

Background

Increased glycolytic activity and lactate production are characteristic features of triple-negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate-responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes.

Methods

Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-d-glucose or l-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.

Results

We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.

Conclusion

We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.

Comprehensive investigation of malignant epithelial cell-related genes in clear cell renal cell carcinoma: development of a prognostic signature and exploration of tumor microenvironment interactions

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy with complex heterogeneity within epithelial cells, which plays a crucial role in tumor progression and immune regulation. Yet, the clinical importance of the malignant epithelial cell-related genes (MECRGs) in ccRCC remains insufficiently understood. This research aims to undertake a comprehensive investigation into the functions and clinical relevance of malignant epithelial cell-related genes in ccRCC, providing valuable understanding of the molecular mechanisms and offering potential targets for treatment strategies. Using data from single-cell sequencing, we successfully identified 219 MECRGs and established a prognostic model MECRGS (MECRGs' signature) by synergistically analyzing 101 machine-learning models using 10 different algorithms. Remarkably, the MECRGS demonstrated superior predictive performance compared to traditional clinical features and 92 previously published signatures across six cohorts, showcasing its independence and accuracy. Upon stratifying patients into high- and low-MECRGS subgroups using the specified cut-off threshold, we noted that patients with elevated MECRGS scores displayed characteristics of an immune suppressive tumor microenvironment (TME) and showed worse outcomes after immunotherapy. Additionally, we discovered a distinct ccRCC tumor cell subtype characterized by the high expressions of PLOD2 (procollagen-lysine,2-oxoglutarate 5-dioxygenase 2) and SAA1 (Serum Amyloid A1), which we further validated in the Renji tissue microarray (TMA) cohort. Lastly, 'Cellchat' revealed potential crosstalk patterns between these cells and other cell types, indicating their potential role in recruiting CD163 + macrophages and regulatory T cells (Tregs), thereby establishing an immunosuppressive TME. PLOD2 + SAA1 + cancer cells with intricate crosstalk patterns indeed show promise for potential therapeutic interventions.

Developing a prognostic model for skin melanoma based on the persistent tumor mutation burden and determining IL17REL as a therapeutic target

BACKGROUND

One popular and well-established marker for the immune checkpoint blockade (ICB) response is tumor mutation burden (TMB). Persistent TMB (pTMB), a subset of TMB, provides a better indicator to predict patient ICB therapy outcomes, as shown by some studies. Immune checkpoint drugs have significantly changed how melanoma is treated in recent years.

METHODS

In this study, we integrated the TCGA-SKCM database and data of pTMB of TCGA from the paper that first mentioned pTMB and analyzed mutational and Immune characteristics associated with pTMB level in SKCM. Next, the predictive DEGs were identified the subgroups of pTMB by Cox regression and LASSO analyses to construct a pTMB-related signature. Finally, the expression and Biological functions of signature genes was detected, and further validated in vitro assay.

RESULTS

In the current research, we explored the mutational and immunological features related to the level of TMB in cutaneous melanoma (CM). The high-pTMB subgroup exhibited an increasing incidence of gene changes and higher levels of immune cell infiltration. Subsequently, we established a pTMB-related signature based on the predictive DEGs and found the biological features and immune-associated variables between two distinct risk groups. Lastly, the results of the clinical sample validation demonstrated that the expression of IL17REL was down-regulated in the collected samples of individuals with CM. The in vitro assay results indicated that IL17REL effectively suppressed the proliferation, clonality, and migration of CM cells.

CONCLUSION

In conclusion, we have developed a prediction model associated with TMB and subsequently validated the potential influence of IL17REL on Overall Survival (OS) in patients diagnosed with melanoma.

Bioinformatics Analysis and Experimental Verification Define Different Angiogenesis Subtypes in Endometrial Carcinoma and Identify a Prognostic Signature

Increasing evidence indicates that peripheral blood vessels play a pivotal role in regulating tumor growth with the presence of new blood vessels facilitating tumor growth and metastasis. Nevertheless, the impact of specific molecule-mediated angiogenesis on the tumor immune microenvironment (TIME) and individual prognosis of uterine corpus endometrial carcinoma (UCEC) remains uncertain. The transcriptome information on 217 prognostic angiogenesis-related genes was integrated, and the angiogenesis patterns of 506 UCEC patients in The Cancer Genome Atlas (TCGA) cohort were comprehensively evaluated. We identified five angiogenic subtypes, namely, EC1, EC2, EC3, EC4, and EC5, which differed significantly in terms of prognosis, clinicopathological features, cancer hallmarks, genomic mutations, TIME patterns, and immunotherapy responses. Additionally, an angiogenesis-related prognostic risk score (APRS) was constructed to enable an individualized comprehensive evaluation. In multiple cohorts, APRS demonstrated a powerful predictive ability for the prognosis of UCEC patients. Likewise, APRS was confirmed to be associated with clinicopathological features, genomic mutations, cancer hallmarks, and TIME patterns in UCEC patients. The predictability of APRS for immune checkpoint inhibitor (ICI) therapy was also salient. Subsequently, the expression levels of four angiogenesis-related hub genes were verified by qRT-PCR, immunohistochemistry, and single-cell sequencing data analysis. The effects of four representative genes on angiogenesis were validated by Wound-Healing and Transwell assays, tube formation assay in vitro, and tumor xenograft model in vivo. This study proffered a new classification of UCEC patients based on angiogenesis. The established APRS may contribute to individualized prognosis prediction and immunotherapy selections that are better suited for UCEC patients.

Histamine-related genes participate in the establishment of an immunosuppressive microenvironment and impact the immunotherapy response in hepatocellular carcinoma

Chronic inflammation is pivotal in the pathogenesis of hepatocellular carcinoma (HCC). Histamine is a biologically active substance that amplifies the inflammatory and immune response and serves as a neurotransmitter. However, knowledge of histamine's role in HCC and its effects on immunotherapy remains lacking. We focused on histamine-related genes to investigate their potential role in HCC. The RNA-seq data and clinical information regarding HCC were obtained from The Cancer Genome Atlas (TCGA). After identifying the differentially expressed genes, we constructed a signature using the univariate Cox proportional hazard regression and least absolute shrinkage and selection operator (LASSO) analyses. The signature's predictive performance was evaluated using a receiver operating characteristic curve (ROC) analysis. Furthermore, drug sensitivity, immunotherapy effects, and enrichment analyses were conducted. Histamine-related gene expression in HCC was confirmed using quantitative real-time polymerase chain reaction (qRT-PCR). A histamine-related gene prognostic signature (HRGPS) was developed in TCGA. Time-dependent ROC and Kaplan-Meier survival analyses demonstrated the signature's strong predictive power. Importantly, patients in high-risk groups exhibited a higher frequency of TP53 mutations, elevated immune checkpoint-related gene expression, and increased infiltration of immunosuppressive cells-indicating a potentially favorable response to immunotherapy. In addition, drug sensitivity analysis revealed that the signature could effectively predict chemotherapy efficacy and sensitivity. qRT-PCR results validated histamine-related gene overexpression in HCC. Our findings demonstrate that inhibiting histamine-related genes and signaling pathways can impact the therapeutic effect of anti-PD-1/PD-L1. The precise predictive ability of our signature in determining the response to different therapeutic options highlights its potential clinical significance.

EZH2 Inhibition Enhances PD-L1 Protein Stability Through USP22-Mediated Deubiquitination in Colorectal Cancer

The regulation of PD-L1 is the key question, which largely determines the outcome of the immune checkpoint inhibitors (ICIs) based therapy. However, besides the transcription level, the protein stability of PD-L1 is closely correlated with its function and has drawn increasing attention. In this study, EZH2 inhibition enhances PD-L1 expression and protein stability, and the deubiquitinase ubiquitin-specific peptidase 22 (USP22) is identified as a key mediator in this process. EZH2 inhibition transcriptionally upregulates USP22 expression, and upregulated USP22 further stabilizes PD-L1. Importantly, a combination of EZH2 inhibitors with anti-PD-1 immune checkpoint blockade therapy improves the tumor microenvironment, enhances sensitivity to immunotherapy, and exerts synergistic anticancer effects. In addition, knocking down USP22 can potentially enhance the therapeutic efficacy of EZH2 inhibitors on colon cancer. These findings unveil the novel role of EZH2 inhibitors in tumor immune evasion by upregulating PD-L1, and this drawback can be compensated by combining ICI immunotherapy. Therefore, these findings provide valuable insights into the EZH2-USP22-PD-L1 regulatory axis, shedding light on the optimization of combining both immune checkpoint blockade and EZH2 inhibitor-based epigenetic therapies to achieve more efficacies and accuracy in cancer treatment.

Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, a significant proportion of gastric cancer (GC) patients do not respond to this therapy. Consequently, there is an urgent need to elucidate the mechanisms underlying resistance to ICIs and identify robust biomarkers capable of predicting the response to ICIs at treatment initiation. Methods: In this study, we collected GC tissues from 28 patients prior to the administration of anti-programmed death 1 (PD-1) immunotherapy and conducted protein quantification using high-resolution mass spectrometry (MS). Subsequently, we analyzed differences in protein expression, pathways, and the tumor microenvironment (TME) between responders and non-responders. Furthermore, we explored the potential of these differences as predictive indicators. Finally, using machine learning algorithms, we screened for biomarkers and constructed a predictive model. Results: Our proteomics-based analysis revealed that low activity in the complement and coagulation cascades pathway (CCCP) and a high abundance of activated CD8 T cells are positive signals corresponding to ICIs. By using machine learning, we successfully identified a set of 10 protein biomarkers, and the constructed model demonstrated excellent performance in predicting the response in an independent validation set (N = 14; area under the curve [AUC] = 0.959). Conclusion: In summary, our proteomic analyses unveiled unique potential biomarkers for predicting the response to PD-1 inhibitor immunotherapy in GC patients, which may provide the impetus for precision immunotherapy.

Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

Targeting PRKDC activates the efficacy of antitumor immunity while sensitizing to chemotherapy and targeted therapy in liver hepatocellular carcinoma

BACKGROUND

Liver hepatocellular carcinoma (LIHC) ranks among the malignancies with the highest mortality rates, primarily due to chemoresistance culminating in treatment failure. Despite its impact, predictive models addressing disease progression, tumor microenvironment, and drug sensitivity remain elusive for LIHC patients. Recognizing the significant influence of various programmed cell death (PCD) modes on tumor evolution, this study investigates PCD genes to elucidate their implications on the prognosis and immune landscape of LIHC.

METHODS

To develop the classification and model, we employed a total of 17 genes associated with PCD patterns. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database, specifically from LIHC patients. Moreover, we obtained spatial transcriptome data and additional bulk datasets from the Gene Expression Omnibus (GEO) database to conduct further analysis. Various experiments were conducted to validate the role of the PCD gene PRKDC in proliferation, migration, invasion, EMT, cell cycle, therapeutic sensitivity, and antitumor immunity.

RESULTS

A novel LIHC classification based on these genes divided patients into two clusters, C1 and C2. The C2 cluster exhibited characteristics indicative of poor prognosis and an immune-activated microenvironment. This group showed greater response potential to immune checkpoint inhibitors, displaying higher levels of certain immune signatures and receptors. A programmed cell death index (PCDI) was constructed using 17 selected PCD genes. This index could effectively predict patient prognosis, with higher PCDI indicating poorer survival rates and a more pro-tumor microenvironment. Immune landscapes revealed varying interactions with PCDI, suggesting therapeutic targets and insights into treatment resistance. Moreover, experiments results suggested that PRKDC can augment the invasive nature and growth of malignant cells and it can serve as a potential target for therapy, offering hope for ameliorating the prognosis of LIHC patients.

CONCLUSIONS

The study uncovers the insights of programmed cell death in the prognosis and potential therapeutic interventions. And we found that PRKDC can serve as a target for enhancing the efficacy of antitumor immunity while sensitizing chemotherapy and targeted therapy in liver hepatocellular carcinoma.

Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states

OBJECTIVES

This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission.

METHODS

We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks.

RESULTS

Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse.

CONCLUSION

Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

CXCR6 defines therapeutic subtypes of CD4+ cytotoxic T cell lineage for adoptive cell transfer therapy in pediatric B cell acute lymphoblastic leukemia

The potential of cytotoxic CD4+ T cells and tissue resident memory T cells (Trm) in achieving adult leukemia remission have been highlighted [1,2]. We hypothesized that CXCR6 could serve as a marker for cytotoxic CD4+ Trm cells in the bone marrow (BM) of pediatric B-ALL patients. Flow cytometry (FCM) and published single cell RNA sequencing (scRNA-seq) datasets were employed to characterize CXCR6+CD4+ T cells in the BM and peripheral blood (PB) of pediatric B-ALL patients and healthy donors. FCM, scRNA-seq and co-culture were utilized to explore the cytotoxicity of CXCR6+CD4+ T cells in vitro based on in vitro induction of CXCR6+CD4+ T cells using tumor antigens and peripheral blood mononuclear cells (PBMCs). The ssGSEA based on the cell markers identified according to the in vivo scRNA-seq data, the TARGET-ALL-P2 datasets, and integrated machine learning algorithm were employed to figure out the key cells with prognostic values, followed by simulation of adoptive cell transfer therapy (ACT). Integrated machine learning identified the high-risk cells for disease free survival, and overall survival, while simulation of ACT therapy using CXCR6+CD4+T cells indicated that CXCR6+CD4+ T cells could remodel the bone marrow microenvironments towards anti-tumor. Based on the expression of genes involved in formation of resident memory T cells, CXCR6 is not a marker of resident memory CD4+T cells but defines therapeutic subtypes of CD4+ cytotoxic T cell lineage for pediatric B-ALL.

Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis

BACKGROUND

Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets.

METHODS

Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms.

RESULTS

The Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo-keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity.

CONCLUSION

These three genes are pivotal mitochondrial genes implicated in NASH progression.

Mutation of neurotrophic tyrosine receptor kinase can promote pan-cancer immunity and the efficacy of immunotherapy

The Neurotrophic tyrosine receptor kinase (NTRK) family plays important roles in tumor progression and is involved in tumor immunogenicity. Here, we conducted a comprehensive bioinformatic and clinical analysis to investigate the characteristics of NTRK mutations and their association with the outcomes in pan-cancer immunotherapy. In 3888 patients across 12 cancer types, patients with NTRK-mutant tumors showed more benefit from immunotherapy in terms of objective response rate (ORR; 41.7% vs. 27.5%; P < 0.001), progress-free survival (PFS; HR = 0.80; 95% CI, 0.68-0.96; P = 0.01), and overall survival (OS; HR = 0.71; 95% CI, 0.61-0.82; P < 0.001). We further constructed and validated a nomogram to estimate survival probabilities after the initiation of immunotherapy. Multi-omics analysis on intrinsic and extrinsic immune landscapes indicated that NTRK mutation was associated with enhanced tumor immunogenicity, enriched infiltration of immune cells, and improved immune responses. In summary, NTRK mutation may promote cancer immunity and indicate favorable outcomes in immunotherapy. Our results have implications for treatment decision-making and developing immunotherapy for personalized care.

Unraveling the interplay of ferroptosis and immune dysregulation in diabetic kidney disease: a comprehensive molecular analysis

BACKGROUND

Diabetic kidney disease (DKD) is a primary microvascular complication of diabetes with limited therapeutic effects. Delving into the pathogenic mechanisms of DKD and identifying new therapeutic targets is crucial. Emerging studies reveal the implication of ferroptosis and immune dysregulation in the pathogenesis of DKD, however, the precise relationship between them remains not fully elucidated. Investigating their interplay is pivotal to unraveling the pathogenesis of diabetic kidney disease, offering insights crucial for targeted interventions and improved patient outcomes.

METHODS

Integrated analysis, Consensus clustering, Machine learning including Generalized Linear Models (GLM), RandomForest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (xGB), Artificial neural network (ANN) methods of DKD glomerular mRNA sequencing were performed to screen DKD-related ferroptosis genes.CIBERSORT, ESTIMATE and ssGSEA algorithm were used to assess the infiltration of immune cells between DKD and control groups and in two distinct ferroptosis phenotypes. The ferroptosis hub genes were verified in patients with DKD and in the db/db spontaneous type 2 diabetes mouse model via immunohistochemical and Western blotting analyses in mouse podocyte MPC5 and mesangial SV40-MES-13 cells under high-glucose (HG) conditions.

RESULTS

We obtained 16 differentially expressed ferroptosis related genes and patients with DKD were clustered into two subgroups by consensus clustering. Five ferroptosis genes (DUSP1,ZFP36,PDK4,CD44 and RGS4) were identified to construct a diagnostic model with a good diagnosis performance in external validation. Analysis of immune infiltration revealed immune heterogeneity between DKD patients and controls.Moreover, a notable differentiation in immune landscape, comprised of Immune cells, ESTIMATE Score, Immune Score and Stromal Score was observed between two FRG clusters. GSVA analysis indicated that autophagy, apoptosis and complement activation can participate in the regulation of ferroptosis phenotypes. Experiment results showed that ZFP36 was significantly overexpressed in both tissue and cells while CD44 was on the contrary.Meanwhile,spearman analysis showed both ZFP36 and CD44 has a strong correlation with different immune cells,especially macrophage.

CONCLUSION

The regulation of the immune landscape in DKD is significantly influenced by the focal point on ferroptosis. Newly identified ferroptosis markers, CD44 and ZFP36, are poised to play essential roles through their interactions with macrophages, adding substantial value to this regulatory landscape.

Integration of single-cell RNA-seq and bulk RNA-seq to construct liver hepatocellular carcinoma stem cell signatures to explore their impact on patient prognosis and treatment

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.

METHODS

The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.

RESULTS

A total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.

CONCLUSION

The current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Bulk and single-cell transcriptome reveal the immuno-prognostic subtypes and tumour microenvironment heterogeneity in HCC

BACKGROUND & AIMS

Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single-cell transcriptome is helpful to recognize immune subtypes and TME in HCC.

METHODS

Based on the relative expression ordering (REO) of 1259 immune-related genes, an immuno-prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in-house samples. The machine learning models based on subtype-specific gene pairs with stable REOs were constructed to jointly predict immuno-prognostic subtypes in single-cell RNA-seq data and validated in another single-cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed.

RESULTS

An immune-related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single-cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid-derived suppressor cells (MDSCs), Mø_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single-cell data revealed the crosstalk of SPP1-related L-R pairs between cancer and immune cells, especially SPP1-CD44, might lead to immunosuppression in C1.

CONCLUSIONS

The REO-based immuno-prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC. This study paved the way for understanding TME heterogeneity between immuno-prognostic subtypes of HCC.

Cold to Hot: Tumor Immunotherapy by Promoting Vascular Normalization Based on PDGFB Nanocomposites

Immunotherapy is a promising cancer therapeutic strategy. However, the "cold" tumor immune microenvironment (TIME), characterized by insufficient immune cell infiltration and immunosuppressive status, limits the efficacy of immunotherapy. Tumor vascular abnormalities due to defective pericyte coverage are gradually recognized as a profound determinant in "cold" TIME establishment by hindering immune cell trafficking. Recently, several vascular normalization strategies by improving pericyte coverage have been reported, whereas have unsatisfactory efficacy and high rates of resistance. Herein, a combinatorial strategy to induce tumor vasculature-targeted pericyte recruitment and zinc ion-mediated immune activation with a platelet-derived growth factor B (PDGFB)-loaded, cyclo (Arg-Gly-Asp-D-Phe-Lys)-modified zeolitic imidazolate framework 8 (PDGFB@ZIF8-RGD) nanoplatform is proposed. PDGFB@ZIF8-RGD effectively induced tumor vascular normalization, which facilitated trafficking and infiltration of immune effector cells, including natural killer (NK) cells, M1-like macrophages and CD8+ T cells, into tumor microenvironment. Simultaneously, vascular normalization promoted the accumulation of zinc ions inside tumors to trigger effector cell immune activation and effector molecule production. The synergy between these two effects endowed PDGFB@ZIF8-RGD with superior capabilities in reprogramming the "cold" TIME to a "hot" TIME, thereby initiating robust antitumor immunity and suppressing tumor growth. This combinatorial strategy for improving immune effector cell infiltration and activation is a promising paradigm for solid tumor immunotherapy.

Identification of HDAC9 and ARRDC4 as potential biomarkers and targets for treatment of type 2 diabetes

We aimed to identify the key potential insulin resistance (IR)-related genes and investigate their correlation with immune cell infiltration in type 2 diabetes (T2D). The GSE78721 dataset (68 diabetic patients and 62 controls) was downloaded from the Gene Expression Omnibus database and utilized for single-sample gene set enrichment analysis. IR-related genes were obtained from the Comparative Toxicology Genetics Database, and the final IR-differentially expressed genes (DEGs) were screened by intersecting with the DEGs obtained from the GSE78721 datasets. Functional enrichment analysis was performed, and the networks of the target gene with microRNA, transcription factor, and drug were constructed. Hub genes were identified based on a protein-protein interaction network. Least absolute shrinkage and selection operator regression and Random Forest and Boruta analysis were combined to screen diagnostic biomarkers in T2D, which were validated using the GSE76894 (19 diabetic patients and 84 controls) and GSE9006 (12 diabetic patients and 24 controls) datasets. Quantitative real-time polymerase chain reaction was performed to validate the biomarker expression in IR mice and control mice. In addition, infiltration of immune cells in T2D and their correlation with the identified markers were computed using CIBERSORT. We identified differential immune gene set regulatory T-cells in the GSE78721 dataset, and T2D samples were assigned into three clusters based on immune infiltration. A total of 2094 IR-DEGs were primarily enriched in response to endoplasmic reticulum stress. Importantly, HDAC9 and ARRDC4 were identified as markers of T2D and associated with different levels of immune cell infiltration. HDAC9 mRNA level were higher in the IR mice than in control mice, while ARRDC4 showed the opposite trend. In summary, we discovered potential vital biomarkers that contribute to immune cell infiltration associated with IR, which offers a new sight of immunotherapy for T2D.

WTAP regulates the production of reactive oxygen species, promotes malignant progression, and is closely related to the tumor microenvironment in glioblastoma

RNA modifications have been substantiated to regulate the majority of physiological activities in the organism, including the metabolism of reactive oxygen species (ROS), which plays an important role in cells. As for the effect of RNA modification genes on ROS metabolism in glioblastoma (GBM), it has not been studied yet. Therefore, this study aims to screen the RNA modification genes that are most related to ROS metabolism and explore their effects on the biological behavior of GBM in vitro. Here, an association between WTAP and ROS metabolism was identified by bioinformatics analysis, and WTAP was highly expressed in GBM tissue compared with normal brain tissue, which was confirmed by western blotting and immunohistochemical staining. When using a ROS inducer to stimulate GBM cells in the WTAP overexpression group, the ROS level increased more significantly and the expression levels of superoxide dismutase 1 (SOD1) and catalase (CAT) also increased. Next, colony formation assay, wound healing assay, and transwell assay were performed to investigate the proliferation, migration, and invasion of GBM cells. The results showed that WTAP, as an oncogene, promoted the malignant progression of GBM cells. Functional enrichment analysis predicted that WTAP was involved in the regulation of tumor/immune-related functional pathways. Western blotting was used to identify that WTAP had a regulatory effect on the phosphorylation of PI3K/Akt signaling. Finally, based on functional enrichment analysis, we further performed immune-related analysis on WTAP. In conclusion, this study analyzed WTAP from three aspects, which provided new ideas for the treatment of GBM.

Analyzing the associations between tertiary lymphoid structures and postoperative prognosis, along with immunotherapy response in gastric cancer: findings from pooled cohort studies

BACKGROUND

The clinical significance of tertiary lymphoid structure (TLS) in gastric cancer (GC) was uncertain.

METHODS

A systematic search was performed in public databases for eligible studies as of April 2, 2023. Meta-analyses were performed to interrogate the associations between TLS levels and prognosis and immunotherapy response of GC. Bioinformatic analyses based on the nine-gene signature of TLS were further conducted to capture the biological underpinnings.

RESULTS

Eleven studies containing 4224 GC cases were enrolled in the meta-analysis. TLS levels positively correlated with smaller tumor size, earlier T stage and N stage. Moreover, higher TLS levels were detected in diffuse and mix subtypes of GC (P < 0.001). Higher TLS levels strongly predicted favorable postoperative overall survival of GC, with HR of 0.36 (95%CI 0.26-0.50, P < 0.001) and 0.55 (95%CI 0.45-0.68, P < 0.001) of univariate and multivariate Cox analysis, respectively. Higher TLS levels were also in favor of the treatment response of anti-PD-1 inhibitors as later-line therapy of GC. TLS levels positively correlated with immune effector cells infiltration, diversity and richness of T cell receptor and B cell receptor repertoire, immune checkpoint genes expression, and immune-related genes mutation of GC in the TCGA-STAD cohort, representing higher immunogenicity and immunoactivity. Moreover, moderate accuracy of TLS levels in predicting benefit from anti-PD-1 inhibitors in the PRJEB25780 cohort was also validated (AUC 0.758, 95%CI 0.583-0.933), higher than the microsatellite instability-score and Epstein-Barr virus status.

CONCLUSIONS

TLS levels demonstrated potential in predicting the postoperative prognosis and immunotherapy response of GC.

Establishment of the molecular subtypes and a risk model for stomach adenocarcinoma based on genes related to reactive oxygen species

Background

Oxidative stress promotes the development of stomach adenocarcinoma (STAD) and resistance of STAD patients to chemotherapy. This study developed a risk classification and prognostic model for STAD based on genes related to oxidative stress.

Methods

Univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression analysis were performed using transcriptome data of STAD from The Cancer Genome Atlas (TCGA) and reactive oxygen species (ROS)-related genes from Gene Set Enrichment Analysis (GSEA) website to develop a risk model. Genetic landscape, pathway characteristics and immune characteristics between the two risk groups were assessed to evaluate patients' response to anti-tumor therapy. Further, a nomogram was created to evaluate the clinical outcomes of STAD patients. The mRNA levels of genes were detected by reverse transcription quantitative PCR (RT-qPCR).

Results

Two ROS-related molecular subtypes (subtype C1 and C2) were classified, with subtype C2 having unfavorable prognosis, higher immune score, and greater infiltration of macrophages, myeloid-derived suppressor cells (MDSCs), mast cells, regulatory T cells, and C-C chemokine receptor (CCR). Five ROS-related genes (ASCL2, COMP, NOX1, PEG10, and VPREB3) were screened to develop a prognostic model, the robustness of which was validated in TCGA and external cohorts. RT-qPCR analysis showed that ASCL2, COMP, NOX1, and PEG10 were upregulated, while the mRNA level of VPREB3 was downregulated in gastric cancer cells. The risk score showed a negative relation to tumor mutation burden (TMB). Low-risk patients exhibited higher mutation frequencies of TTN, SYNE1, and ARID1A, higher response rate to immunotherapy and were more sensitive to 32 traditional chemotherapeutic drugs, while high-risk patients were sensitive to 13 drugs. Calibration curve and DCA confirmed the accuracy and reliability of the nomogram.

Conclusion

These findings provided novel understanding on the mechanism of ROS in STAD. The current study developed a ROS-related signature to help predict the prognosis of patients suffering from STAD and to guide personalized treatment.

Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study

OBJECTIVES

Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.

METHODS

We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC).

RESULTS

We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002).

CONCLUSION

A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.

Small molecule agents for triple negative breast cancer: Current status and future prospects

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor prognosis. The number of cases increased by 2.26 million in 2020, making it the most commonly diagnosed cancer type in the world. TNBCs lack hormone receptor (HR) and human epidermal growth factor 2 (HER2), which limits treatment options. Currently, paclitaxel-based drugs combined with other chemotherapeutics remain the main treatment for TNBC. There is currently no consensus on the best therapeutic regimen for TNBC. However, there have been successful clinical trials exploring large-molecule monoclonal antibodies, small-molecule targeted drugs, and novel antibody-drug conjugate (ADC). Although monoclonal antibodies have produced clinical success, their large molecular weight can limit therapeutic benefits. It is worth noting that in the past 30 years, the FDA has approved small molecule drugs for HER2-positive breast cancers. The lack of effective targets and the occurrence of drug resistance pose significant challenges in the treatment of TNBC. To improve the prognosis of TNBC, it is crucial to search for effective targets and to overcome drug resistance. This review examines the clinical efficacy, adverse effects, resistance mechanisms, and potential solutions of targeted small molecule drugs in both monotherapies and combination therapies. New therapeutic targets, including nuclear export protein 1 (XPO1) and hedgehog (Hh), are emerging as potential options for researchers and become integrated into clinical trials for TNBC. Additionally, there is growing interest in the potential of targeted protein degradation chimeras (PROTACs), degraders of rogue proteins, as a future therapy direction. This review provides potentially valuable insights with clinical implications.

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common types of tumors. The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies.

AIM

To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs (LMR-lncRNAs) and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC.

METHODS

Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs. Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs. Nile red staining was employed to observe intracellular lipid levels. The interaction between RP11-817I4.1, miR-3120-3p, and ATP citrate lyase (ACLY) was validated through the performance of dual-luciferase reporter gene and RIP assays.

RESULTS

Three LMR-lncRNAs (negative regulator of antiviral response, RNA transmembrane and coiled-coil domain family 1 antisense RNA 1, and RP11-817I4.1) were identified as predictive markers for HCC patients and were utilized in the construction of risk models. Additionally, proliferation, migration, and invasion were reduced by RP11-817I4.1 knockdown. An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis.

CONCLUSION

LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients, and the discovery of a novel LMR-lncRNAs, RP11-817I4.1, revealed its role in promoting lipid accumulation, thereby accelerating the onset and progression of HCC.

A novel ferroptosis-related gene signature for overall survival prediction in patients with gastric cancer

The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient's prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients.

Predictable regulation of survival by intratumoral microbe-immune crosstalk in patients with lung adenocarcinoma

Intratumoral microbiota can regulate the tumor immune microenvironment (TIME) and mediate tumor prognosis by promoting inflammatory response or inhibiting anti-tumor effects. Recent studies have elucidated the potential role of local tumor microbiota in the development and progression of lung adenocarcinoma (LUAD). However, whether intratumoral microbes are involved in the TIME that mediates the prognosis of LUAD remains unknown. Here, we obtained the matched tumor microbiome and host transcriptome and survival data of 478 patients with LUAD in The Cancer Genome Atlas (TCGA). Machine learning models based on immune cell marker genes can predict 1- to 5-year survival with relative accuracy. Patients were stratified into high- and low-survival-risk groups based on immune cell marker genes, with significant differences in intratumoral microbial communities. Specifically, patients in the high-risk group had significantly higher alpha diversity (p < 0.05) and were characterized by an enrichment of lung cancer-related genera such as Streptococcus. However, network analysis highlighted a more active pattern of dominant bacteria and immune cell crosstalk in TIME in the low-risk group compared to the high-risk group. Our study demonstrated that intratumoral microbiota-immune crosstalk was strongly associated with prognosis in LUAD patients, which would provide new targets for the development of precise therapeutic strategies.

Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes

Hepatocellular carcinoma (HCC) is the third leading cause of global cancer-related deaths. Despite immunotherapy offering hope for patients with HCC, only some respond to it. However, it remains unclear how to pre-screen eligible patients. Our study aimed to address this issue. In this study, we identified 13 prognostic genes through univariate Cox regression analysis of 87 apoptosis-related genes. Subsequently, these 13 genes were analyzed using ConsensusClusterPlus, and patients were categorized into three molecular types: C1, C2, and C3. A prognostic model and RiskScore were constructed using Lasso regression analysis of 132 significant genes identified between C1 and C3. We utilized quantitative polymerase chain reaction to confirm the model's transcript level in Huh7 and THLE2 cell lines. Both molecular subtypes and RiskScores effectively predicted patients benefiting from immunotherapy. Cox regression analysis revealed RiskScore as the most significant prognosis factor, suggesting its clinical application potential and providing a foundation for future experimental research.

The levels of IL1RN is a factor influencing the onset of rheumatoid arthritis in non-alcoholic fatty liver disease

With the improvement of global dietary conditions, non-alcoholic fatty liver disease (NAFLD) has gradually become prevalent. As the number of NAFLD patients increases, the coexistence of diseases associated with it has come into focus. In this study, based on immune phenotypes, intercellular communication activities, and clinical manifestations of NAFLD patients, IL1RN was identified as a central pro-inflammatory factor. Subsequently, potential downstream biological pathways of IL1RN in liver tissues and various cell types were enriched to describe its functions. Transcription factors Nfkb1, Jun, and Sp1, significantly associated with these functions, were also enriched. Functional studies of IL1RN suggest its potential to trigger autoimmune diseases. Given this, Mendelian randomization analysis was used to explore the causal relationship between NAFLD and various autoimmune diseases, with IL1RN considered as an intermediary introduced into Mendelian randomization studies. The results indicate that IL1RN and its partially related proteins play a certain mediating role in the process of NAFLD inducing rheumatoid arthritis (RA). Finally, additional research results suggest that intrahepatic ALT levels may influence IL1RN levels, possibly through amino acid metabolism.

Landscape of N1-methyladenosin (m1A) modification pattern in colorectal cancer

BACKGROUND

N1-methyladenosine (m1A) is a recently identified mRNA modification. However, it is still unclear that how m1A alteration affects the development of colorectal cancer (CRC).

AIMS

The landscape of m1A modification patterns regarding tumor immune microenvironment (TIME) in CRC is a lack of knowledge. Thus, this study will utilize the public database to comprehensively evaluate of multiple m1A methylation regulators in CRC.

METHODS AND RESULTS

We retrospectively analyzed 398 patients with CRC and 39 healthy people for negative control, using the The Cancer Genome Atlas (TCGA) database to evaluate m1A modification patterns regarding tumor immune microenvironment (TIME) in CRC. The m1Ascore was developed via principal component analysis. And its clinical value in prognosis of CRC was further explored. Our study revealed 12 key m1A-related DEGs including CLDN3, MUC2 and CCDC85B which are identified associated with invasion and metastasis in CRC. The most important biological processes linked to weak immune response and poor prognosis were the regulation of RNA metabolism and RNA biosynthesis. Furthermore, we found that compared to patients with low m1A scores, those with high m1A scores had higher percentage, larger tumor burdens, and worse prognosis.

CONCLUSION

Significantly diverse m1A modification patterns can be seen in CRC. Through its impact on TIME and immunological dysfunction, the heterogeneity of m1A alteration patterns influences the prognosis of CRC. This study provided novel insights into the m1A modification in CRC which might promote the development of personalized immunotherapy strategies.

Identification and validation of the association of Janus kinase 2 mutations with the response to immune checkpoint inhibitor therapy

BACKGROUND

Janus kinase 2 (JAK2) mutation plays an important role in T cell immunity. However, the effect of JAK2 mutation on immunotherapy is largely uncharacterized.

METHODS

In this study, we analyzed the effect of JAK2 mutation on the efficacy and outcomes of immune checkpoint inhibitor (ICI) therapy in the discovery cohort (n = 662) and the verification cohort (n = 1423). Furthermore, we explored the association of JAK2 mutation with the tumor immune microenvironment in a multiomics cohort.

RESULTS

In the discovery cohort (n = 662), JAK2 mutant-type patients had a better objective response rate (58.8% vs. 26.7%, P = 0.010), durable clinical benefit (64.7% vs. 38.9%, P = 0.043), progression-free survival (hazard ratio [HR] = 0.431, P = 0.015), and overall survival (HR = 0.378, P = 0.025), relative to JAK2 wild-type patients. Moreover, we further verified the prognostic significance of JAK2 mutation in an independent ICI treatment cohort with a larger sample size (n = 1423). In addition, we discovered that the JAK2 mutation was remarkably related to increased immunogenicity, such as a higher TMB, higher expression of costimulatory molecules and stimulation of antigen processing mechanisms. In addition, JAK2 mutation was positively correlated with activated anticancer immunity, such as infiltration of various immune cells and higher expression of chemokines.

CONCLUSION

Our study demonstrates that JAK2 mutation is a novel marker that can be used to effectively predict prognosis and response to ICI therapy.

Identification and validation of an individualized metabolic prognostic signature for predicting the biochemical recurrence of prostate cancer based on the immune microenvironment

BACKGROUND

Prostate cancer (PCa) is the most prevalent genitourinary malignancy in men, with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. The immune microenvironment and metabolic alterations have crucial implications for the tumorigenesis and progression of PCa. Therefore, identifying metabolic genes associated with the immune microenvironment holds promise for predicting BCR and improving PCa prognosis.

METHODS

In this study, ssGSEA and hierarchical clustering analysis were first conducted to evaluate and group PCa samples, followed by the use of the ESTIMATE and CIBERSORT algorithms to characterize the immunophenotypes and tumor microenvironment. The differential metabolic genes (MTGs) between groups were utilized to develop a prognostic-related signature. The predictive performance of the signature was assessed by principal component analysis (PCA), receiver operating characteristic (ROC) curve analysis, survival analysis, and the TIDE algorithm. A miRNA-MTGs regulatory network and predictive nomogram were constructed. Moreover, the expression of prognostic MTGs in PCa was detected by RT‒qPCR.

RESULTS

PCa samples from the TCGA cohort were separated into two groups: the immune-low group and immune-high group. Forty-eight differentially expressed MTGs between the groups were identified, including 37 up-regulated and 11 down-regulated MTGs. Subsequently, CEL, CYP3A4, and PDE6G were identified as the genes most strongly associated with the BCR of PCa patients and these genes were utilized to establish the MTGs-based prognostic signatures. PCA, ROC curves analysis, Kaplan-Meier survival analysis, and the nomogram all showed the good predictive ability of the signature regardless of clinical variables. Furthermore, the MTGs-based signature was indicated as a potential predictive biomarker for immunotherapy response. Nine miRNAs involved in the regulation of prognostic MTGs were determined. In addition to the CEL gene, the PDE6G and CYP3A4 genes were expressed at higher levels in PCa samples.

CONCLUSIONS

The MTGs-based signature represents a novel approach with promising potential for predicting BCR in PCa patients.

Prognostic and immune predictive roles of a novel tricarboxylic acid cycle-based model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Since the tricarboxylic acid cycle is widely involved in tumor metabolic reprogramming and cuproptosis, investigating related genes may help to identify prognostic signature of patients with HCC. Data on patients with HCC were sourced from public datasets, and were divided into train, test, and single-cell cohorts. A variety of machine learning algorithms were used to identify different molecular subtypes and determine the prognostic risk model. Our findings revealed that the risk score (TRscore), based on the genes OGDHL, CFHR4, and SPP1, showed excellent predictive performance in different datasets. Pathways related to cell cycle and immune inflammation were enriched in the high-risk group, whereas metabolism-related pathways were significantly enriched in the low-risk group. The high-risk group was associated with a greater number of mutations of detrimental biological behavior and higher levels of immune infiltration, immune checkpoint expression, and anti-cancer immunotherapy response. Low-risk patients demonstrated greater sensitivity to erlotinib and phenformin. SPP1 was mainly involved in the interaction among tumor-associated macrophages, T cells, and malignant cells via SPP1-CD44 and SPP1-(ITGA5 + ITGB1) ligand-receptor pairs. In summary, our study established a prognostic model, which may contribute to individualized treatment and clinical management of patients with HCC.

Development of a stemness-related prognostic index to provide therapeutic strategies for bladder cancer

Bladder cancer (BC) is a heterogeneous disease with varying clinical outcomes. Recent evidence suggests that cancer progression involves the acquisition of stem-like signatures, and assessing stemness indices help uncover patterns of intra-tumor molecular heterogeneity. We used the one-class logistic regression algorithm to compute the mRNAsi for each sample in BLCA cohort. We subsequently classified BC patients into two subtypes based on 189 mRNAsi-related genes, using the unsupervised consensus clustering. Then, we identified nine hub genes to construct a stemness-related prognostic index (SRPI) using Cox regression, LASSO regression and Random Forest methods. We further validated SRPI using two independent datasets. Afterwards, we examined the molecular and immune characterized of SRPI. Finally, we conducted multiply drug screening and experimental approaches to identify and confirm the most proper agents for patients with high SRPI. Based on the mRNAsi-related genes, BC patients were classified into two stemness subtypes with distinct prognosis, functional annotations, genomic variations and immune profiles. Using the SRPI, we identified a specific subgroup of BC patients with high SRPI, who had a poor response to immunotherapy, and were less sensitive to commonly used chemotherapeutic agents, FGFR inhibitors, and EGFR inhibitors. We further identified that dasatinib was the most promising therapeutic agent for this subgroup of patients. This study provides further insights into the stemness classification of BC, and demonstrates that SRPI is a promising tool for predicting prognosis and therapeutic opportunities for BC patients.

Prognostic value and tumour microenvironment characteristics of the Glasgow Microenvironment Score in primary triple-negative breast cancer

AIMS

The Glasgow Microenvironment Score (GMS) reflects the tumour microenvironment (TME) status by combining inflammatory cell infiltration and the tumour-stroma percentage. This study aimed to investigate the prognostic value and TME characteristics of the GMS for patients with triple-negative breast cancer (TNBC).

METHODS

A total of 123 patients with stage I-III TNBC were enrolled in this study. The association between GMS and clinicopathological characteristics was examined using the Pearson's χ2 test or Fisher's exact test. Kaplan-Meier plots were used to compare survival among the three GMS groups. Cox regression analyses were conducted to test the HR. Microenvironment Cell Populations-counter algorithm was used to estimate the TME components of each case.

RESULTS

We found that higher GMS score tended to exhibit the lower nuclear grade (p=0.016), more positive lymph nodes (p=0.014) and later tumour, node, metastases stage (p=0.012). GMS was an independent prognostic factor for disease-free survival in TNBC, and GMS 2 showed the worst prognosis (HR=6.42, p=0.028). GMS 0 was more infiltrated with cytotoxic lymphocytes, including CD8+ T cells (p=0.037) and natural killer cells (p=0.005), while GMS 2 was enriched in more endothelial cells (p=0.014) and fibroblasts (p=0.008).

CONCLUSION

Our study suggested that the GMS is a prognostic indicator for patients with TNBC. As an accessible and effective index, the GMS may be a promising tool to help clinicians assess prognostic risk and TME for patients with TNBC.

Construction of an ER stress-related prognostic signature for predicting prognosis and screening the effective anti-tumor drug in osteosarcoma

BACKGROUND

Osteosarcoma is the most common malignant primary bone tumor in infants and adolescents. The lack of understanding of the molecular mechanisms underlying osteosarcoma progression and metastasis has contributed to a plateau in the development of current therapies. Endoplasmic reticulum (ER) stress has emerged as a significant contributor to the malignant progression of tumors, but its potential regulatory mechanisms in osteosarcoma progression remain unknown.

METHODS

In this study, we collected RNA sequencing and clinical data of osteosarcoma from The TCGA, GSE21257, and GSE33382 cohorts. Differentially expressed analysis and the least absolute shrinkage and selection operator regression analysis were conducted to identify prognostic genes and construct an ER stress-related prognostic signature (ERSRPS). Survival analysis and time dependent ROC analysis were performed to evaluate the predictive performance of the constructed prognostic signature. The "ESTIMATE" package and ssGSEA algorithm were utilized to evaluate the differences in immune cells infiltration between the groups. Cell-based assays, including CCK-8, colony formation, and transwell assays and co-culture system were performed to assess the effects of the target gene and small molecular drug in osteosarcoma. Animal models were employed to assess the anti-osteosarcoma effects of small molecular drug.

RESULTS

Five genes (BLC2, MAGEA3, MAP3K5, STC2, TXNDC12) were identified to construct an ERSRPS. The ER stress-related gene Stanniocalcin 2 (STC2) was identified as a risk gene in this signature. Additionally, STC2 knockdown significantly inhibited osteosarcoma cell proliferation, migration, and invasion. Furthermore, the ER stress-related gene STC2 was found to downregulate the expression of MHC-I molecules in osteosarcoma cells, and mediate immune responses through influencing the infiltration and modulating the function of CD8+ T cells. Patients categorized by risk scores showed distinct immune status, and immunotherapy response. ISOX was subsequently identified and validated as an effective anti-osteosarcoma drug through a combination of CMap database screening and in vitro and in vivo experiments.

CONCLUSION

The ERSRPS may guide personalized treatment decisions for osteosarcoma, and ISOX holds promise for repurposing in osteosarcoma treatment.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Zinc finger protein 831 promotes apoptosis and enhances chemosensitivity in breast cancer by acting as a novel transcriptional repressor targeting the STAT3/Bcl2 signaling pathway

Emerging evidence suggested that zinc finger protein 831 (ZNF831) was associated with immune activity and stem cell regulation in breast cancer. Whereas, the roles and molecular mechanisms of ZNF831 in oncogenesis remain unclear. ZNF831 expression was significantly diminished in breast cancer which was associated with promoter CpG methylation but not mutation. Ectopic over-expression of ZNF831 suppressed breast cancer cell proliferation and colony formation and promoted apoptosis in vitro, while knockdown of ZNF831 resulted in an opposite phenotype. Anti-proliferation effect of ZNF831 was verified in vivo. Bioinformatic analysis of public databases and transcriptome sequencing both showed that ZNF831 could enhance apoptosis through transcriptional regulation of the JAK/STAT pathway. ChIP and luciferase report assays demonstrated that ZNF831 could directly bind to one specific region of STAT3 promoter and induce the transcriptional inhibition of STAT3. As a result, the attenuation of STAT3 led to a restraint of the transcription of Bcl2 and thus accelerated the apoptotic progression. Augmentation of STAT3 diminished the apoptosis-promoting effect of ZNF831 in breast cancer cell lines. Furthermore, ZNF831 could ameliorate the anti-proliferation effect of capecitabine and gemcitabine in breast cancer cell lines. Our findings demonstrate for the first time that ZNF831 is a novel transcriptional suppressor through inhibiting the expression of STAT3/Bcl2 and promoting the apoptosis process in breast cancer, suggesting ZNF831 as a novel biomarker and potential therapeutic target for breast cancer patients.

An immune cell infiltration landscape classification to predict prognosis and immunotherapy effect in oral squamous cell carcinoma

Tumor immune cell infiltration (ICI) is associated with the prognosis of oral squamous cell carcinoma (OSCC) patients and the effect of immunotherapy. The combat algorithm was used to merge the data from three databases and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to quantify the amount of infiltrated immune cells. Unsupervised consistent cluster analysis was used to determine ICI subtypes, and differentially expressed genes (DEGs) were determined according to these subtypes. The DEGs were then clustered again to obtain the ICI gene subtypes. The principal component analysis (PCA) and the Boruta algorithm were used to construct the ICI scores. Three different ICI clusters and gene clusters with a prognosis of significant difference were found and the ICI score was constructed. Patients with higher ICI scores have a better prognosis following internal and external verification. Besides, the proportion of patients with effective immunotherapy was higher than those with low scores in two external datasets with immunotherapy. This study shows that the ICI score is an effective prognostic biomarker and a predictor of immunotherapy.

A novel endoplasmic reticulum stress-related lncRNA signature for prognosis prediction and immune response evaluation in Stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.

METHODS

We collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.

RESULTS

The 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.

CONCLUSION

Our study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

STAT4 facilitates PD-L1 level via IL-12R/JAK2/STAT3 axis and predicts immunotherapy response in breast cancer

Signal transducer and activator of transcription 4 (STAT4) is a critical transcription factor for T helper cell differentiation and tumor cells. Although its prognostic role and gene function have been reported in several carcinomas, the role of STAT4 in vitro and in vivo in breast cancer remains poorly understood. The effect of STAT4 in immunotherapy is also unclear. Therefore, we integrated bulk transcriptomics, experiments, and single-cell transcriptomics to systematically analyze its function in prognosis and signaling pathway. Several clinical breast cancer cohorts confirmed STAT4 as a T-cell relevant prognostic biomarker. Overexpressed STAT4 increased programmed cell death ligand 1 (PD-L1) and major histocompatibility complex class II levels in breast cancer cells. In molecular mechanism, transcriptional synergy between STAT4 and STAT3 transactivated interleukin (IL)-12R and involved a positive feedback loop: STAT4/IL-12R/JAK2-STAT3-STAT4, which contributed to the upregulation of PD-L1 expression. The above signaling axis was defined as the STAT4-related pathway and its score was used to predict T-cell expansion and anti-PD1 treatment response. These findings highlight a novel molecular mechanism indirectly regulating PD-L1 through the STAT4-related pathway: IL-12R/JAK2-STAT3-STAT4/PD-L1, and it has potential application in predicting anti-PD-1 immunotherapy response, which may pave the way for stratified immunotherapy in breast cancer.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer

PURPOSE

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Molecular targets and therapeutic strategies for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is known for its heterogeneous complexity and is often difficult to treat. TNBC lacks the expression of major hormonal receptors like estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 and is further subdivided into androgen receptor (AR) positive and AR negative. In contrast, AR negative is also known as quadruple-negative breast cancer (QNBC). Compared to AR-positive TNBC, QNBC has a great scarcity of prognostic biomarkers and therapeutic targets. QNBC shows excessive cellular growth and proliferation of tumor cells due to increased expression of growth factors like EGF and various surface proteins. This study briefly reviews the limited data available as protein biomarkers that can be used as molecular targets in treating TNBC as well as QNBC. Targeted therapy and immune checkpoint inhibitors have recently changed cancer treatment. Many studies in medicinal chemistry continue to focus on the synthesis of novel compounds to discover new antiproliferative medicines capable of treating TNBC despite the abundance of treatments currently on the market. Drug repurposing is one of the therapeutic methods for TNBC that has been examined. Moreover, some additional micronutrients, nutraceuticals, and functional foods may be able to lower cancer risk or slow the spread of malignant diseases that have already been diagnosed with cancer. Finally, nanomedicines, or applications of nanotechnology in medicine, introduce nanoparticles with variable chemistry and architecture for the treatment of cancer. This review emphasizes the most recent research on nutraceuticals, medication repositioning, and novel therapeutic strategies for the treatment of TNBC.

Characterization of the immune cell function landscape in head and neck squamous carcinoma to assist in prognosis prediction and immunotherapy

BACKGROUND

The malignant characteristics of cancer depend not only on intrinsic properties of cancer cells but also on the functions of infiltrating immune cells. In this study, we aimed to investigate the functional landscape of immune cells in head and neck squamous cell carcinoma (HNSCC).

METHODS

We employed single-sample gene set enrichment analysis to examine the immunophenotypes of HNSCC based on 29 immune cell functions (ICFs) in TCGA and GSE65858 datasets. We analyzed the clinical features, immune microenvironment, molecular profiles, and biological processes. Additionally, we developed and validated an ICF-based risk score for personalized prognosis prediction. We confirmed the value of the ICF score in our cohort using qRT-PCR and immunohistochemistry. Molecular docking was used to predict potential compounds for immunotherapy.

RESULTS

Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples. The characteristics of Immune-H were consistent with a "Hot" tumor, Immune-L was similar to a "Cold" tumor, and Immune-M exhibited intermediate features. The ICF risk score was associated with immune checkpoints, infiltrating immune cells, tumor mutation burden, and sensitivities to targeted/chemotherapeutic agents. Gene set variation analysis implicated the involvement of metabolic reprogramming pathways in the high-risk group. The combination of "Tumor Immune Dysfunction and Exclusion" and "Immunophenoscore" algorithms indicated that the low-risk group had a higher likelihood of benefiting from immunotherapy. Finally, we identified Eltrombopag and other compounds that may be beneficial for HNSCC immunotherapy.

CONCLUSION

Our study provides a novel perspective on the tumor microenvironment of HNSCC, aiding in the understanding of HNSCC heterogeneity and the development of personalized/precision medicine.

Identification of Immune Subtypes and Candidate mRNA Vaccine Antigens in Small Cell Lung Cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have demonstrated promising outcomes in small cell lung cancer (SCLC), but not all patients benefit from it. Thus, developing precise treatments for SCLC is a particularly urgent need. In our study, we constructed a novel phenotype for SCLC based on immune signatures.

METHODS

We clustered patients with SCLC hierarchically in 3 publicly available datasets according to the immune signatures. ESTIMATE and CIBERSORT algorithm were used to evaluate the components of the tumor microenvironment. Moreover, we identified potential mRNA vaccine antigens for patients with SCLC, and qRT-PCR were performed to detect the gene expression.

RESULTS

We identified 2 SCLC subtypes and named Immunity High (Immunity_H) and Immunity Low (Immunity_L). Meanwhile, we obtained generally consistent results by analyzing different datasets, suggesting that this classification was reliable. Immunity_H contained the higher number of immune cells and a better prognosis compared to Immunity_L. Gene-set enrichment analysis revealed that several immune-related pathways such as cytokine-cytokine receptor interaction, programmed cell death-Ligand 1 expression and programmed cell death-1 checkpoint pathway in cancer were hyperactivated in the Immunity_H. However, most of the pathways enriched in the Immunity_L were not associated with immunity. Furthermore, we identified 5 potential mRNA vaccine antigens of SCLC (NEK2, NOL4, RALYL, SH3GL2, and ZIC2), and they were expressed higher in Immunity_L, it indicated that Immunity_L maybe more suitable for tumor vaccine development.

CONCLUSIONS

SCLC can be divided into Immunity_H and Immunity_L subtypes. Immunity_H may be more suitable for treatment with ICIs. NEK2, NOL4, RALYL, SH3GL2, and ZIC2 may be act as potential antigens for SCLC.

Identification an innovative classification and nomogram for predicting the prognosis of thyroid carcinoma patients and providing therapeutic schedules

BACKGROUND

Thyroid carcinoma (THCA) represents a prevalent form of cancer globally, with its incidence demonstrating an upward trend in recent years. Accumulating evidence has indicated that programmed cell death (PCD) patterns exert a vital influence on tumor progression. Nevertheless, the association between PCD and the prognosis of patients with papillary thyroid carcinoma remains to be elucidated. The current study endeavors to examine the link between PCD and the prognosis of thyroid cancer while concurrently developing a prognostic index based on PCD genes.

MATERIALS AND METHODS

Programmed cell death patterns were employed to construct the model and define clusters. Gene expression profile genomics and clinical data pertaining to 568 patients with thyroid cancer were sourced from the TCGA database. In addition, single-cell transcriptome data GSE184362 were procured from the Gene Expression Omnibus (GEO) database for subsequent analysis.

RESULTS

The study harnessed six machine learning algorithms to create a programmed cell death signature (PCDS). Ultimately, the model developed via SVM was chosen as the optimal model, boasting the highest C-index. Moreover, the application of non-negative matrix factorization (NMF) led to the identification of two molecular subtypes of THCA, each characterized by distinct vital biological processes and drug sensitivities. The investigation revealed that PCDS is linked to chemokines, interleukins, interferons, and checkpoint genes, as well as pivotal components of the tumor microenvironment, as determined through a comprehensive analysis of bulk and single-cell transcriptomes. Patients with THCA and elevated PCDS values are more inclined to exhibit resistance to conventional chemotherapy regimens, yet may display heightened responsiveness to targeted therapeutic agents. Finally, we established a nomogram model based on multivariable cox and logistic regression analyses to predict the overall survival of THCA patients.

CONCLUSION

This research sheds new light on the role of programmed cell death (PCD) patterns in THCA. By conducting an in-depth analysis of various cell death patterns, a novel PCD model has been devised, capable of accurately predicting the clinical prognosis and drug sensitivity of patients with THCA.