Single-cell transcriptome sequencing analysis reveals intra-tumor heterogeneity in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive system that poses a significant threat to human life and health. It is crucial to thoroughly investigate the mechanisms of esophageal carcinogenesis and identify potential key molecular events in its carcinogenesis. Single-cell transcriptome sequencing is an emerging technology that has gained prominence in recent years for studying molecular mechanisms, which may help to further explore the underlying mechanisms of the ESCC tumor microenvironment in depth. The single-cell dataset was obtained from GSE160269 in the Gene Expression Omnibus database, including 60 tumor samples and four paracancer samples. The single-cell data underwent dimensional reduction clustering analysis to identify clusters and annotate expression profiles. Subcluster analysis was conducted for each cellular taxon. Copy number variation analysis of tumor cell subpopulations was performed to primarily identify malignant cells within them. A proposed chronological analysis was performed to obtain the process of cell differentiation. In addition, cell communication, transcription factor analysis, and tumor pathway analysis were also performed. Relevant risk models and key genes were established by univariate COX regression and LASSO analysis. The key genes obtained from the screen were subjected to appropriate silencing and cellular assays, including CCK-8, 5-ethynyl-2'-deoxyuridine, colony formation, and western blot. Single-cell analysis revealed that normal samples contained a large number of fibroblasts, T cells, and B cells, with fewer other cell types, whereas tumor samples exhibited a relatively balanced distribution of cell types. Subclassification analysis of immune cells, fibroblasts, endothelial cells, and epithelial cells revealed their specific spatial characteristics. The prognostic risk model, we constructed successfully, achieved accurate prognostic stratification for ESCC patients. The screened key gene, UPF3A, was found to be significantly associated with the development of ESCC by cellular assays. This process might be linked to the phosphorylation of ERK and P38. Single-cell transcriptome analysis successfully revealed the distribution of cell types and major expressed factors in ESCC patients, which could facilitate future in-depth studies on the therapeutic mechanisms of ESCC.

Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma.

Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.

Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer.

Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients. Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC). Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-ß and other pathway signals were correlated with progression. Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration.

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.

Background: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. Methods: RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). Results: We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration.

Background: N6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated. Methods: Bulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated. Results: By umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients. Conclusion: In our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer.

Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19.

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied. Methods: COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target. Result: Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment. Conclusions: Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD.

Spatial architecture of regulatory T-cells correlates with disease progression in patients with nasopharyngeal cancer.

Purpose: This study aims to investigate the prognostic value of composition and spatial architecture of tumor-infiltrating lymphocytes (TILs) as well as PDL1 expression on TILs subpopulations in nasopharyngeal carcinoma (NPC). Methods: A total of 121 patients with NPC were included and divided into two groups: favorable (n = 68) and unfavorable (n = 53). The archived tumor tissues of the included patients were retrieved, and a tissue microarray was constructed. The density and spatial distribution of TILs infiltration were analyzed using the multiplex fluorescent immunohistochemistry staining for CD3, CD4, CD8, Foxp3, cytokeratin (CK), PDL1, and 4',6-diamidino-2-phenylindole (DAPI). The infiltration density of TILs subpopulations and PDL1 expression were compared between the two groups. The Gcross function was calculated to quantify the relative proximity of any two types of cells. The Cox proportional hazards regression model was used to identify factors associated with overall survival (OS) and disease-free survival (DFS). Results: The densities of regulatory T-cells (Tregs), effector T-cells (Teffs), PDL1+ Tregs, and PDL1+ Teffs were significantly higher in patients with unfavorable outcomes. PDL1 expression on tumor cells (TCs) or overall TILs was not associated with survival. Multivariate analysis revealed that higher PDL1+ Tregs infiltration density was independently associated with inferior OS and DFS, whereas Tregs infiltration density was only a prognostic marker for DFS. Spatial analysis revealed that unfavorable group had significantly stronger Tregs and PDL1+ Tregs engagement in the proximity of TCs and cytotoxic T lymphocyte (CTLs). Gcross analysis further revealed that Tregs and PDL1+ Tregs were more likely to colocalize with CTLs. Moreover, increased GTC : Treg (Tregs engagement surrounding TCs) and GCTL : PDL1+ Treg were identified as independent factors correlated with poor outcomes. Conclusion: TILs have a diverse infiltrating pattern and spatial distribution in NPC. Increased infiltration of Tregs, particularly PDL1+ Tregs, as well as their proximity to TCs and CTLs, correlates with unfavorable outcomes, implying the significance of intercellular immune regulation in mediating disease progression.

Pretreatment systemic immune-inflammation index predicts survival for non-metastatic nasopharyngeal carcinoma: two independent institutional studies.

Objective: This study aimed to evaluate the prognostic value of the pretreatment systemic immune-inflammation index (SII) in non-metastatic nasopharyngeal carcinoma (NPC). Methods: We retrospectively analyzed the data of 839 patients with non-metastatic NPC recruited from two independent institutions. The training-set cohort and the external validation-set cohort was comprised of 459 and 380 patients from each institution, respectively. The optimal cut-off value of SII was determined, and a prognostic risk stratification model was developed based on the training cohort and further assessed in the validation cohort. The propensity score matching (PSM) method was applied to minimize the confounding effects of unbalanced covariables. Results: The optimal cut-off value of the SII in the training cohort was 686, which was confirmed using the validation cohort. Multivariate analysis showed that both before and after PSM, SII values > 686 were independently associated with worse progression-free survival (PFS) ratio in both cohorts (before PSM, P = 0.008 and P = 0.008; after PSM, P = 0.008 and P = 0.007, respectively). Based on the analysis of independent prognostic factors of SII and N stage, we developed a categorical risk stratification model, which achieved significant discrimination among risk indexes associated with PFS and distant metastasis-free survival (DMFS) in the training cohort. There was no significant difference in PFS between RT alone and combined therapies within the low- and intermediate-risk groups (5-year PFS, 77.5% vs. 75.3%, P = 0.275). Patients in the high-risk group who received concurrent chemoradiotherapy experienced superior PFS compared with those who received other therapies (5-year PFS, 64.9% vs. 40.3%, P = 0.003). Conclusion: Pretreatment SII predicts PFS of patients with non-metastatic NPC. Prognostic risk stratification incorporating SII is instructive for selecting individualized treatment.

The Role of Postoperative Radiotherapy and Prognostic Model in Primary Squamous Cell Carcinoma of Parotid Gland.

BACKGROUND: Primary squamous cell carcinoma of parotid gland (parotid SCC) is a high malignant histologic subtype of parotid cancers with aggressive clinical presentation. However, the clinical features and survival benefit of postoperative radiotherapy (PORT) for primary parotid SCC are not well known. METHODS: A retrospective population-based study was performed to identify the role of PORT in parotid SCC patients diagnosed between 1975 and 2016 from SEER database. A prognostic risk model was established based on patient clinical features, including age, tumor stage, and node involvement status. Patients were stratified into high, intermediate, and low risk according to this model. The survival benefit of radiotherapy was compared in the whole cohort and different risk groups. RESULTS: Nine hundred thirty-one parotid SCC patients were extracted from SEER database, 634 (68.1%) in the RT group and 286 (30.7%) in the non-RT group. Overall, 503 (54.0%) deaths occurred, with a median follow-up of 84 months, the 5-year OS was 43.6% in the whole cohort, 47.7 vs 35.9% in patients with/without PORT (P = 0.005), and 58.9 vs. 38.8 vs. 27.1% in low-, intermediate-, and high-risk group (P < 0.001). Compared with surgery alone, PORT significantly improved the OS of patients with medium risk (47.5 vs. 20.6, P < 0.001), whereas not in the low risk (61 vs. 54%, P = 0.710) and high (25.6 vs. 28.7%, P = 0.524). CONCLUSION: This prognostic model can separate the patients with parotid squamous cell carcinoma into different risk. PORT significantly improved the OS of patients with intermediate risk, whereas high-risk group may need more intensive treatment strategies.