Lung Squamous Carcinoma Patients Research Articles

Plasma exosomes contain protein biomarkers valuable for the diagnosis of lung cancer

Accumulating evidence indicates that exosomal proteins are critical in diagnosing malignant tumors. To identify novel exosomal biomarkers for lung cancer diagnosis, we isolated plasma exosomes from 517 lung cancer patients and 168 healthy controls (NLs)—186 lung adenocarcinoma (LUAD) patients (screening (SN): 20, validation (VD): 166), 159 lung squamous carcinoma (LUSC) patients (SN: 20, VD: 139), 172 benign nodules (LUBN) patients (SN: 20, VD: 152) and 168 NLs (SN: 20, VD: 148)—and randomly assigned them to the SN or VD group. Proteomic analysis by LC–MS/MS and PRM were performed on all groups. The candidate humoral markers were evaluated and screened by a machine learning method. All selected biomarkers were identified in the VD groups. For LUAD, a 7-protein panel had AUCs of 97.9% and 87.6% in the training and test sets, respectively, and 89.5% for early LUAD. For LUSC, an 8-protein panel showed AUCs of 99.1% and 87.0% in the training and test sets and 92.3% for early LUSC. For LUAD + LUSC (LC), an 8-protein panel showed AUCs of 85.9% and 80.3% in the training and test sets and 87.1% for early LC diagnosis. The characteristics of the exosomal proteome make exosomes potential diagnostic tools.

Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach.

The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome's regulatory function is closely associated with the disease's pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters. This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation. Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice. The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

Blood FOLR3 methylation dysregulations and heterogeneity in non-small lung cancer highlight its strong associations with lung squamous carcinoma

BackgroundNon-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancers. Early detection is crucial to reduce lung cancer-related mortality. Aberrant DNA methylation occurs early during carcinogenesis and can be detected in blood. It is essential to investigate the dysregulated blood methylation markers for early diagnosis of NSCLC.MethodsNSCLC-associated methylation gene folate receptor gamma (FOLR3) was selected from an Illumina 850K array analysis of peripheral blood samples. Mass spectrometry was used for validation in two independent case–control studies (validation I: n = 2548; validation II: n = 3866). Patients with lung squamous carcinoma (LUSC) or lung adenocarcinoma (LUAD), normal controls (NCs) and benign pulmonary nodule (BPN) cases were included. FOLR3 methylations were compared among different populations. Their associations with NSCLC clinical features were investigated. Receiver operating characteristic analyses, Kruskal–Wallis test, Wilcoxon test, logistics regression analysis and nomogram analysis were performed.ResultsTwo CpG sites (CpG_1 and CpG_2) of FOLR3 was significantly lower methylated in NSCLC patients than NCs in the discovery round. In the two validations, both LUSC and LUAD patients presented significant FOLR3 hypomethylations. LUSC patients were highlighted to have significantly lower methylation levels of CpG_1 and CpG_2 than BPN cases and LUAD patients. Both in the two validations, CpG_1 methylation and CpG_2 methylation could discriminate LUSC from NCs well, with areas under the curve (AUCs) of 0.818 and 0.832 in validation I, and 0.789 and 0.780 in validation II. They could also differentiate LUAD from NCs, but with lower efficiency. CpG_1 and CpG_2 methylations could also discriminate LUSC from BPNs well individually in the two validations. With the combined dataset of two validations, the independent associations of age, gender, and FOLR3 methylation with LUSC and LUAD risk were shown and the age-gender-CpG_1 signature could discriminate LUSC and LUAD from NCs and BPNs, with higher efficiency for LUSC.ConclusionsBlood-based FOLR3 hypomethylation was shown in LUSC and LUAD. FOLR3 methylation heterogeneity between LUSC and LUAD highlighted its stronger associations with LUSC. FOLR3 methylation and the age-gender-CpG_1 signature might be novel diagnostic markers for the early detection of NSCLC, especially for LUSC.

Soluble galectin-3 as a microenvironment-relevant immunoregulator with prognostic and predictive value in lung adenocarcinoma.

Despite the success of therapies in lung cancer, more studies of new biomarkers for patient selection are urgently needed. The present study aims to analyze the role of galectin-3 (GAL-3) in the lung tumor microenvironment (TME) using tumorspheres as a model and explore its potential role as a predictive and prognostic biomarker in non-small cell lung cancer patients. For in vitro studies, lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) primary cultures from early-stage patients and commercial cell lines were cultured, using tumorsphere-forming assays and adherent conditions for the control counterparts. We analyzed the pattern of secretion and expression of GAL-3 using reverse transcription-quantitative real-time PCR (RTqPCR), immunoblot, immunofluorescence, flow cytometry, and immunoassay analysis. Our results using three-dimensional (3D) models of lung tumor cells revealed that soluble GAL-3 (sGAL-3) is highly expressed and secreted. To more accurately mimic the TME, a co-culture of tumorspheres and fibroblasts was used, revealing that GAL-3 could be important as an immunomodulatory molecule expressed and secreted in the TME, modulating immunosuppression through regulatory T cells (TREGS ). In the translational phase, we confirmed that patients with high expression levels of GAL-3 had more TREGS , which suggests that tumors may be recruiting this population through GAL-3. Next, we evaluated levels of sGAL-3 before surgery in LUAD and LUSC patients, hypothesizing that sGAL-3 could be used as an independent prognostic biomarker for overall survival and relapse-free survival in early-stage LUAD patients. Additionally, levels of sGAL-3 at pretreatment and first response assessment from plasma to predict clinical outcomes in advanced LUAD and LUSC patients treated with first-line pembrolizumab were evaluated, further supporting that sGAL-3 has a high efficiency in predicting durable clinical response to pembrolizumab with an area under curve of 0.801 (P = 0.011). Moreover, high levels might predict decreased progression-free survival and OS to anti-PD-1 therapy, with sGAL-3 being a prognosis-independent biomarker for advanced LUAD.

The Evaluation of Prognostic Value and Immune Characteristics of Ferroptosis-Related Genes in Lung Squamous Cell Carcinoma.

Background The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC). Methods The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis. Results Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression. Conclusions The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.

At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy. In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366-0.7398) in external validation. The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

Risk stratification and prognosis prediction based on inflammation-related gene signature in lung squamous carcinoma.

Inflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients. The RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis. A two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone. The IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.

Construction of a prognostic immune-related lncRNA model and identification of the immune microenvironment in middle- or advanced-stage lung squamous carcinoma patients

BackgroundGlobally, non-small-cell lung cancer (NSCLC) has a high incidence, and NSCLC patients have poor prognoses. Lung squamous carcinoma (LUSC) is a major pathological type of NSCLC. LncRNAs play important roles in tumor progression and immune system functions. The aim of this study was to construct a predictive model with immune-related lncRNAs and to assess the immune microenvironment in middle- or advanced-stage LUSC patients. MethodsRNA sequencing data and corresponding clinical LUSC data were downloaded from The Cancer Genome Atlas. Immune genes were obtained from the Molecular Signatures Database. Immune-related lncRNAs were identified by Pearson correlation analysis in R. The model was constructed using univariate and multivariate Cox regression analyses. Finally, we validated the prognostic immune-related lncRNA model in a cohort from the Fudan University Shanghai Cancer Center. ResultsOur risk model included four immune-related lncRNAs (LINC00944, AL034550.2, AC020907.1 and AC027682.6). Survival analysis revealed that overall and disease-free survival were shorter in the high-risk group than in the low-risk group. Independent prognostic analysis showed that our model could be used as an independent prognostic predictor. The high-risk group was positively associated with CD8+ T cells, B cells, myeloid dendritic cells, macrophages, regulatory T cells (Tregs) and cancer-associated fibroblasts and high expression of PD1 and CTLA4. Additionally, a low-risk score was correlated with lower half maximal inhibitory concentrations (IC50s) of cisplatin, docetaxel, vinorelbine and paclitaxel and a higher IC50 of gemcitabine. Gene set enrichment analysis suggested that these lncRNAs may participate in tumor progression and immune processes. Validation with the clinical cancer cohort demonstrated that higher risk scores were associated with a higher, but not statistically significant, likelihood of recurrence. ConclusionWe established a risk score model including four immune-related lncRNAs. The model accurately predicts the prognosis of middle- or advanced-stage LUSC patients and provides an important reference for individualized treatment.

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

BackgroundRecurrence is a risk factor for the prognosis of lung squamous carcinoma (LUSC). DNA methylation levels of RNAs are also associated with LUSC prognosis. This study aimed to construct a prognostic model with high performance in predicting LUSC prognosis using the methylation levels of lncRNAs and genes.MethodsThe differentially expressed RNAs (DERs) and differentially methylated RNAs (DMRs) between the recurrent and non-recurrent LUSC tissues in The Cancer Genome Atlas (TCGA; training dataset) were identified. Weighted correlation network analysis was performed to identify co-methylation networks. Differentially methylated genes and lncRNAs with opposite expression-methylation levels were used for the screening of prognosis-associated RNAs. The prognostic model was constructed and its performance was validated in the GSE39279 dataset.ResultsA total of 664 DERs and 981 DMRs (including 972 genes) in recurrent LUSC tissues were identified. Three co-methylation modules, including 226 differentially methylated genes, were significantly associated with LUSC. Among prognosis-associated RNAs, 18 DERs/DMRs with opposite methylation-expression levels were included in the methylation prognostic risk model. LUSC patients with high risk scores had a poor prognosis compared with patients who had low risk scores (TCGA: HR = 3.856, 95% CI [2.297–6.471]; GSE39279: HR = 3.040, 95% CI [1.435–6.437]). This model had a high accuracy in predicting the prognosis (AUC = 0.903 and 0.800, respectively), equivalent to the nomogram model inclusive of clinical variables.ConclusionsReferring to the methylation levels of the 16-RNAs might help to predict the survival outcomes in LUSC.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586–0.671) in the TCGA cohort and 0.648 (95% CI: 0.535–0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.

Identification of Immune Subtypes of Lung Squamous Cell Carcinoma by Integrative Genome-Scale Analysis.

BackgroundCharacterization of the tumor microenvironment is helpful to understand the tumor immune environment of lung cancer and help predict the prognosis.MethodsFirst, immune subtypes were identified by consensus subtype among lung squamous carcinoma (LUSC) patients. Immune cell infiltration was evaluated by CIBERSORT and ESTIMATE analyses. Then, based on differentially expressed genes (DEGs) identified, a risk score model was constructed. Finally, gene FPR1 was validated by using YTMLC-90.FindingsLUSC samples were divided into four heterogeneous immune subtypes, with significantly different prognoses with subtype 4 having the poorest overall survival (OS). The immune infiltration score showed that subtype 4 was characterized as immune enriched and fibrotic, while subtype 3 was tumor enriched. DEG analysis showed that upregulated genes in subtype 4 were enriched of neutrophil and exhausted T cell-related biological processes. Based on a univariate Cox regression model, prognostic 7 immune-related genes were combined to construct a risk score model and able to predict OS rates in the validation datasets. Wound healing and transwell assay were conducted to evaluate the invasion property after activating the gene FPR1. InterpretationThe analysis of tumor immune microenvironments among LUSC subtypes may provide new insights into the strategy of immunotherapy.

Multi-omics analysis to identify lung squamous carcinoma lactate metabolism-related subtypes and establish related index to predict prognosis and guide immunotherapy

Lung squamous carcinoma (LUSC) is a malignant tumor of the respiratory system with highly heterogeneous characteristics. Lactate is the main product of aerobic glycolysis during the metabolic reprogramming of tumors. There is growing evidence that lactate metabolic processes have a broad and sophisticated impact on tumor phenotypic plasticity and tumor microenvironment (TME). However, the pattern of lactate metabolism in patients with LUSC and its impact on TME, phenotype, prognosis, and treatment have not been fully elucidated. In this study, we identified two subtypes with different lactate metabolism patterns in LUSC by non-negative matrix factorization and explored their multi-omics features. We observed that lactate metabolism levels in LUSC extensively influenced tumor immune infiltration patterns, adaptation to the hypoxia environment, and energy metabolic reprogramming. Subsequently, we constructed the lactate metabolism-related prognostic index (LMRPI) using Cox stepwise regression analysis. LMRPI showed excellent stability and accuracy, and based on the median value of LMRPI, LUAD were divided into two subgroups. The two subgroups have different patterns of immune infiltration and somatic mutations. Meanwhile, the two subgroups had different responsiveness to immune checkpoint inhibitor (ICI) therapies and different sensitivity to various chemotherapeutic and molecular targeting agents. In conclusion, we defined two subtypes with different lactate metabolism patterns in LUSC and extensively characterized their multi-omics profile. Furthermore, we developed LMRPI that predicts the prognosis of LUSC patients while also predicting their response to various adjuvant therapies, including immunotherapy, to guide their individualized treatment.

Secondary primary lung cancer after esophageal cancer: a population-based study of 44,172 patients

Background The present study aimed to assess the survival, incidence, and characteristics of secondary primary lung cancer (SPLC) after esophageal cancer (EC-LC). Methods The patients with esophageal cancer (EC) who developed SPLC and patients with first primary lung cancer (LC-1) were retrospectively reviewed in the Surveillance, Epidemiology, and End Results 18 registries covering 2000–2016. Overall survival and characteristics were compared between patients with EC-LC and patients with LC-1. The independent relation between a history of EC and death was evaluated by calculating hazard ratios in multivariate Cox regression analysis propensity score-matching analysis, and multiple imputation for cases with missing information. Results In comparison with the general population, the patients with EC had a higher risk for developing secondary primary lung cancer (SIR =1.86, 95% confidence interval (CI): 1.69–2.05). A history of EC was found to be an independent risk factor of death for lung squamous carcinoma (LUSC) and lung adenocarcinoma (LUAD) patients in localized stage based on multivariate Cox regression analysis, propensity score-matching analysis and multiple imputation. Conclusions There is a significantly increased risk of secondary primary lung cancer in EC survivors and a history of EC adversely affects overall survival in individuals who subsequently develop localized LUSC and LUAD. Clinicians should moderately strengthen lung tissue protection during the management of EC patients.

Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Antitumor Immunosuppression of EGFR Wild Type Lung Cancers With Low PD-L1 Expression.

PurposeWith the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), non-small cell lung cancer (NSCLC) patients have achieved remarkable survival benefits in recent years. However, epidermal growth factor receptor (EGFR) wild-type and low expression of programmed death-ligand 1 (PD-L1) NSCLCs remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a metabolic gene signature that could successfully identify high-risk patients and reveal its underlying molecular immunology characteristics.MethodsBy identifying the bottom 50% PD-L1 expression level as PD-L1 low expression and removing EGFR mutant samples, a total of 640 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) tumor samples and 93 adjacent non-tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). We identified differentially expressed metabolic genes (DEMGs) by R package limma and the prognostic genes by Univariate Cox proportional hazards regression analyses. The intersect genes between DEMGs and prognostic genes were put into the least absolute shrinkage and selection operator (LASSO) penalty Cox regression analysis. The metabolic gene signature contained 18 metabolic genes generated and successfully stratified LUAD and LUSC patients into the high-risk and low-risk groups, which was also validated by the Gene Expression Omnibus (GEO) database. Its accuracy was proved by the time-dependent Receiver Operating Characteristic (ROC) curve, Principal Components Analysis (PCA), and nomogram. Furthermore, the Single-sample Gene Set Enrichment Analysis (ssGSEA) and diverse acknowledged methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT revealed its underlying antitumor immunosuppressive status. Besides, its relationship with somatic copy number alterations (SCNAs) and tumor mutational burden (TMB) was also discussed.ResultsIt is noteworthy that metabolism reprogramming is associated with the survival of the double-negative LUAD and LUSC patients. The SCNAs and TMB of critical metabolic genes can inhibit the antitumor immune process, which might be a promising therapeutic target.

FGF-2 promotes angiogenesis through a SRSF1/SRSF3/SRPK1-dependent axis that controls VEGFR1 splicing in endothelial cells

BackgroundAngiogenesis is the process by which new blood vessels arise from pre-existing ones. Fibroblast growth factor-2 (FGF-2), a leading member of the FGF family of heparin-binding growth factors, contributes to normal as well as pathological angiogenesis. Pre-mRNA alternative splicing plays a key role in the regulation of cellular and tissular homeostasis and is highly controlled by splicing factors, including SRSFs. SRSFs belong to the SR protein family and are regulated by serine/threonine kinases such as SRPK1. Up to now, the role of SR proteins and their regulators in the biology of endothelial cells remains elusive, in particular upstream signals that control their expression.ResultsBy combining 2D endothelial cells cultures, 3D collagen sprouting assay, a model of angiogenesis in cellulose sponges in mice and a model of angiogenesis in zebrafish, we collectively show that FGF-2 promotes proliferation, survival, and sprouting of endothelial cells by activating a SRSF1/SRSF3/SRPK1-dependent axis. In vitro, we further demonstrate that this FGF-2-dependent signaling pathway controls VEGFR1 pre-mRNA splicing and leads to the generation of soluble VEGFR1 splice variants, in particular a sVEGFR1-ex12 which retains an alternative last exon, that contribute to FGF-2-mediated angiogenic functions. Finally, we show that sVEGFR1-ex12 mRNA level correlates with that of FGF-2/FGFR1 in squamous lung carcinoma patients and that sVEGFR1-ex12 is a poor prognosis marker in these patients.ConclusionsWe demonstrate that FGF-2 promotes angiogenesis by activating a SRSF1/SRSF3/SRPK1 network that regulates VEGFR1 alternative splicing in endothelial cells, a process that could also contribute to lung tumor progression.

Hypoxia-related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD-L1 squamous and adenocarcinoma NSCLC.

BackgroundRecently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death‐ligand 1 (PD‐L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia‐related lncRNAs (hypolncRNAs) classifier that could successfully identify the high‐risk patients and reveal its underlying molecular immunology characteristics.MethodsBy identifying the bottom 25% PD‐L1 expression level as low expression of PD‐L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non‐tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia‐related long non‐coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC (double‐negative LUAD and LUSC) patients into the high‐ and low‐risk groups, whose accuracy was proved by the time‐dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT‐ABS, CIBERSORT, and the single‐sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high‐risk patients.ConclusionsIt is noteworthy that hypolncRNAs are associated with the survival of double‐negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process.

The tumor environment immune phenotype of LUSC by genome-wide analysis

PurposeTo compare the landscape of tumor microenvironment (TME) of lung squamous carcinoma (LUSC) in different immune pattern and explore potential factors on immune therapy and prognosis. Method and MaterialsWe have obtained the LUSC data from TCGA, GEO, and our department and classified them into 2 TME clusters by random forest model based on the infiltration pattern of 24 immune cell populations. We systemically compared the genomic significance, clinical characteristics, and immune infiltration pattern in 2 TME clusters. ResultsSamples were divided into 2 TME clusters based on the relative abundance of 24 immune cells, and a random forest classifier model was constructed. TME cluster B was a higher immune infiltration group with lower mutation load, richer co-infiltrate immune cells, upregulated immune-related cytokines, immune checkpoint molecules, and higher active immune cells. TME cluster was also an independent predictor in prognosis (B vs. A, p < 0.05) in patients from TCGA, GEO, and our department. ConclusionsOur study has described the microenvironment landscape of LUSC in different immune infiltration patterns and systemically analyzed genomic and clinical characteristics with distinct immunophenotypes, thus partly revealed the interaction between tumors and the immune microenvironment, which may guide a more precise and personalized immune therapeutic strategy for LUSC patients.

The effect and mechanism of miR-607/CANT1 axis in lung squamous carcinoma.

Lung squamous carcinoma (LUSC) is the second most frequent subtype of non-small cell lung cancer. Rarely gene alterations are identified in LUSC. Therefore, identifying LUSC-related genes to explain the relevant molecular mechanism is urgently needed. A potential biomarker, calcium-activated nucleotidase 1 (CANT1), was elevated in tissues of LUSC patients relative to normal cases based on the TCGA and/or GTEx database. CCK-8 and transwell tests were then implemented to measure the proliferative, invasive and migratory capacities, and showed that knockdown of CANT1 blocked LUSC cells proliferation. miR-607, predicted as an upstream factor for CANT1, was declined in LUSC using TargetScan analysis and luciferase activity test. Low miR-607 expression was related with unfavorable outcomes of LUSC patients. Moreover, miR-607 downregulation elevated cell viability, invasion and migration in LUSC cells, which was antagonized by si-CANT1. GEPIA website was accessed to estimate the relevance between CANT1 and epithelial-mesenchymal transition (EMT)-related positive factors. The protein levels of Fibronectin, Vimentin, Snail and β-catenin were altered due to the abnormal CANT1 and miR-607 expression. Together, these data unveiled that miR-607/CANT1 pair may exert a vital role in the progression of LUSC through mediating EMT process, which would furnish an available therapeutic therapy for LUSC.

TTN/TP53 mutation might act as the predictor for chemotherapy response in lung adenocarcinoma and lung squamous carcinoma patients.

BackgroundChemotherapy is the preferred treatment in many types of cancer including lung cancer. However, most of patients resist chemotherapy resulting in disease progressive and recurrence. Titin (TTN) mutation is proved as a beneficial role in lung squamous carcinoma (LUSC), but the predictive role on chemotherapy resistance of lung cancer is still limited and discussable.MethodsClinical information and related somatic mutation profiles were obtained from The Cancer Genome Atlas (TCGA) database and analyzed by R-Studio using R-package. Overall survival (OS) curve and the association between gene mutation and clinical features were determined by GraphPad 6.0 software.ResultsAvailable data including 563 lung adenocarcinoma (LUAD) and 505 LUSC subjects were included in this study. Among all patients, 205 out of 563 LUAD and 326 out of 505 LUSC patients displayed TTN gene mutation. When comparing the clinical features in TTN-mutated patients to patients without TTN mutation who received chemotherapy, the tumors were always located in the upper lung in LUAD patients with TTN mutation and most of TTN-mutated subjects were at low pathological stage, which was not observed in LUSC patients. However, patients with TTN-mutation, particularly missense mutation, had a higher chemosensitivity and longer OS period than that patients without TTN mutation in both LUAD and LUSC. Of note, LUAD and LUSC patients possessed favorable OS and better chemotherapy response benefiting from TTN/tumor protein 53 (TP53) double mutation compared to TTN and TP53 mutation alone, respectively. Additionally, TTN/TP53 double mutation-initiated high rate of chemotherapy response were largely concentrated within LUAD and LUSC patients whose anatomic neoplasm subdivision were located in the upper lung.ConclusionsCollectively, TTN/TP53 co-mutation is possibly served as an effective predictor for OS and chemotherapy response in lung cancer.

Localized fluorescent imaging of multiple proteins on individual extracellular vesicles using rolling circle amplification for cancer diagnosis.

Extracellular vesicles (EV) have attracted increasing attention as tumour biomarkers due to their unique biological property. However, conventional methods for EV analysis are mainly based on bulk measurements, which masks the EV‐to‐EV heterogeneity in tumour diagnosis and classification. Herein, a localized fluorescent imaging method (termed Digital Profiling of Proteins on Individual EV, DPPIE) was developed for analysis of multiple proteins on individual EV. In this assay, an anti‐CD9 antibody engineered biochip was used to capture EV from clinical plasma sample. Then the captured EV was specifically recognized by multiple DNA aptamers (CD63/EpCAM/MUC1), followed by rolling circle amplification to generate localized fluorescent signals. By‐analyzing the heterogeneity of individual EV, we found that the high‐dimensional data collected from each individual EV would provide more precise information than bulk measurement (ELISA) and the percent of CD63/EpCAM/MUC1‐triple‐positive EV in breast cancer patients was significantly higher than that of healthy donors, and this method can achieve an overall accuracy of 91%. Moreover, using DPPIE, we are able to distinguish the EV between lung adenocarcinoma and lung squamous carcinoma patients. This individual EV heterogeneity analysis strategy provides a new way for digging more information on EV to achieve multi‐cancer diagnosis and classification.