Identification of a Novel Tumor Microenvironment-Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma

BACKGROUND

Histone lactylation is a novel epigenetic modification that is involved in a variety of critical biological regulations. However, the role of lactylation-related genes in lung adenocarcinoma has yet to be investigated.

METHODS

RNA-seq data and clinical information of LUAD were downloaded from TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed to identify differentially expressed genes (DEGs) between the two clusters, and risk prediction models were constructed by Cox regression analysis and LASSO analysis. Kaplan-Meier (KM) survival analysis, ROC curves and nomograms were used to validate the accuracy of the models. We also explored the differences in risk scores in terms of immune cell infiltration, immune cell function, TMB, TIDE, and anticancer drug sensitivity. In addition, single-cell clustering and trajectory analysis were performed to further understand the significance of lactylation-related genes. We further analyzed lactate content and glucose uptake in lung adenocarcinoma cells and tissues. Changes in LUAD cell function after knockdown of lactate dehydrogenase (LDHA) by CCK-8, colony formation and transwell assays. Finally, we analyzed the expression of KRT81 in LUAD tissues and cell lines using qRT-PCR, WB, and IHC. Changes in KRT81 function in LUAD cells were detected by CCK-8, colony formation, wound healing, transwell, and flow cytometry. A nude mouse xenograft model and a KrasLSL-G12D in situ lung adenocarcinoma mouse model were used to elucidate the role of KRT81 in LUAD.

RESULTS

After identifying 26 lactylation-associated DEGs, we constructed 10 lactylation-associated lung adenocarcinoma prognostic models with prognostic value for LUAD patients. A high score indicates a poor prognosis. There were significant differences between the high-risk and low-risk groups in the phenotypes of immune cell infiltration rate, immune cell function, gene mutation frequency, and anticancer drug sensitivity. TMB and TIDE scores were higher in high-risk score patients than in low-risk score patients. MS4A1 was predominantly expressed in B-cell clusters and was identified to play a key role in B-cell differentiation. We further found that lactate content was abnormally elevated in lung adenocarcinoma cells and cancer tissues, and glucose uptake by lung adenocarcinoma cells was significantly increased. Down-regulation of LDHA inhibits tumor cell proliferation, migration and invasion. Finally, we verified that the model gene KRT81 is highly expressed in LUAD tissues and cell lines. Knockdown of KRT81 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. KRT81 may play a tumorigenic role in LUAD through the EMT and PI3K/AKT pathways. In vivo, KRT81 knockdown inhibited tumor growth.

CONCLUSION

We successfully constructed a new prognostic model for lactylation-related genes. Lactate content and glucose uptake are significantly higher in lung adenocarcinoma cells and cancer tissues. In addition, KRT81 was validated at cellular and animal levels as a possible new target for the treatment of LUAD, and this study provides a new perspective for the individualized treatment of LUAD.

Pan-Cancer Insights: A Study of Microbial Metabolite Receptors in Malignancy Dynamics

BACKGROUND/OBJECTIVES

The role of the gut microbiome in cancer biology has become an increasingly prominent area of research, particularly regarding the role of microbial metabolites and their receptors (MMRs). These metabolites, through the various gut-organ axes, have been proven to influence several pathogenetic mechanisms. This study conducted a comprehensive pan-cancer analysis of MMR transcriptomic profiles across twenty-three cancer types, exploring the mechanisms through which they can influence cancer development and progression.

METHODS

Utilizing both cancer cell lines from CCLE (Cancer Cell Line Encyclopedia) and human tumor samples from TCGA (The Cancer Gene Atlas), we analyzed 107 MMRs interacting with microbial metabolites such as short-chain fatty acids, bile acids, indole derivatives, and others while studying their interactions with key known cancer genes.

RESULTS

Our results revealed that certain MMRs, such as GPR84 and serotonin receptors, are consistently upregulated in various malignancies, while others, like ADRA1A, are frequently downregulated, suggesting diverse roles in cancer pathophysiology. Furthermore, we identified significant correlations between MMR expression and cancer hallmark genes and pathways, including immune evasion, proliferation, and metastasis.

CONCLUSIONS

These findings suggest that the interactions between microbial metabolites and MMRs may serve as potential biomarkers for cancer diagnosis, prognosis, and therapy, highlighting their therapeutic potential. This study underscores the significance of the microbiota-cancer axis and provides novel insights into microbiome-based strategies for cancer treatment.

Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

BACKGROUND

The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).

METHODS

After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the "NMF" R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).

RESULTS

From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.

CONCLUSIONS

We developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.

P2RX1-Negative neutrophils promote the immunosuppressive microenvironment in Non-Small cell lung cancer by Upregulating PD-L1 expression

BACKGROUND

The most abundant innate immune cells, neutrophils, contribute significantly to cancer development by stimulating immunosuppression. However, it remains unclear about its function and molecular mechanisms in the immunosuppressive microenvironment of non-small cell lung cancer (NSCLC).

METHODS

Blood samples were collected from NSCLC patients and healthy volunteers to detect the expression of P2RX1 and PD-L1 in neutrophils using qRT-PCR, western blot (WB), and flow cytometry. Neutrophils were sorted into P2RX1-positive (P2RX1+)/P2RX1-negative (P2RX1-) groups and co-cultured with CD8+ T cells. Changes in the proliferative and cytotoxic capabilities of CD8+ T cells were then detected using flow cytometry and enzyme-linked immunosorbent assay. The content of granzyme B was determined by enzyme-linked immunosorbent assay. The effects of P2RX1-deficient neutrophils on fatty acids, triglycerides, lipid droplet content and FASN expression were detected using kits, Nile red staining and WB, respectively.

RESULTS

This study revealed a deficiency in P2RX1 expression in peripheral blood neutrophils of NSCLC patients, which was negatively correlated with the expression of PD-L1. P2RX1-neutrophils inhibited T cell proliferation and granzyme B expression and promoted T cell exhaustion. Furthermore, in P2RX1-deficient neutrophils, there was a notable increase in the levels of fatty acids, triglycerides, and lipid droplet accumulation, as well as an upregulation of FASN protein expression. Mechanistically, P2RX1-neutrophils upregulated PD-L1 expression by inducing fatty acid metabolism to improve immunosuppression in NSCLC.

CONCLUSION

The mechanism by which P2RX1-deficient neutrophils contributed to immunosuppressive effects in NSCLC was clarified by our findings, indicating that P2RX1 could be one potential target for counteracting the immunosuppressive effects of neutrophils.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Immune Features of Tumor Microenvironment: A Genetic Spotlight

A tumor represents a highly intricate tissue entity, characterized by an exceptionally complex microenvironment that starkly contrasts with the typical physiological surroundings of healthy tissues. Within this tumor microenvironment (TME), every component and factor assume paramount importance in the progression of malignancy and exerts a pivotal influence on a patient's clinical outcome. One of the remarkable aspects of the TME is its remarkable heterogeneity, not only across different types of cancers but even within the same histological category of tumors. In-depth research has illuminated the intricate interplay between specific immune cells and molecules and the dynamic characteristics of the TME. Recent investigations have yielded compelling evidence that several mutations harbored by tumor cells possess the capacity to instigate substantial alterations in the TME. These mutations, often acting as drivers of tumorigenesis, can orchestrate a cascade of events that remodel the TME, thereby influencing crucial aspects of cancer behavior, including its invasiveness, immune evasion, and response to therapies. It is within this nuanced context that the present study endeavors to provide a concise yet comprehensive summary of how specific mutations, within the genetic landscape of cancer cells, can instigate profound changes in TME features. By elucidating the intricate relationship between genetic mutations and the TME, this research aims to contribute to a deeper understanding of cancer biology. Ultimately, the knowledge gained from this study holds the potential to inform the development of more targeted and effective treatments, thereby offering new hope to patients grappling with the complexities of cancer.

A Novel Gene Signature based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients

BACKGROUND

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes.

METHODS

For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions.

RESULTS

Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells.

CONCLUSION

We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

KLRB1 is a novel prognostic biomarker in endometrial cancer and is associated with immune infiltration

Background

Endometrial cancer (EC) has the characteristics of high mortality and poor prognosis in the advanced stage, which seriously threatens women's health. Killer cell lectin-like receptor B1 (KLRB1) is a promising immune checkpoint of which the expression level can regulate the killing effect on tumor cells of the immune system, thereby affecting the survival and prognosis of tumor patients. However, it is still unclear whether KLRB1 is associated with survival and prognosis in patients with EC. Therefore, our study focused on the relationship between KLRB1 and immune cells to explore the role of KLRB1 on the immune microenvironment, and to further explore its feasibility as a prognostic marker in EC.

Methods

In this study, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analyze the messenger RNA (mRNA) expression level of KLRB1 in normal endometrial and EC tissues. The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) database was used to determine the correlation between KLRB1 mRNA expression and clinical features among the EC patients. KLRB1 expression levels were investigated in the Tumor IMmune Estimation Resource (TIMER) database to reveal its relationship with immune cell infiltration of EC. Finally, using the R package clusterProfiler, enrichment analysis was performed on KLRB1 to study its potential function.

Results

The results suggested that KLRB1 expression varied in different tumor tissues, and the EC group had lower mRNA expression levels than did the control group. It was also found that patients with high expression of KLRB1 had a better prognosis. According to further enrichment and immune infiltration analyses, KLRB1 expression had a closed relationship with the level of infiltration of some immune cell types, such as B cells memory, eosinophils, and Tregs, among others.

Conclusions

KLRB1 expression is associated with the infiltration of immune cells and can be used as a prognostic biomarker in EC.

Systematic Multiomic Analysis of PKHD1L1 Gene Expression and Its Role as a Predicting Biomarker for Immune Cell Infiltration in Skin Cutaneous Melanoma and Lung Adenocarcinoma

The identification of genetic factors that regulate the cancer immune microenvironment is important for understanding the mechanism of tumor progression and establishing an effective treatment strategy. Polycystic kidney and hepatic disease 1-like protein 1 (PKHD1L1) is a large transmembrane protein that is highly expressed in immune cells; however, its association with tumor progression remains unclear. Here, we systematically analyzed the clinical relevance of PKHD1L1 in the tumor microenvironment in multiple cancer types using various bioinformatic tools. We found that the PKHD1L1 mRNA expression levels were significantly lower in skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD) than in normal tissues. The decreased expression of PKHD1L1 was significantly associated with unfavorable overall survival (OS) in SKCM and LUAD. Additionally, PKHD1L1 expression was positively correlated with the levels of infiltrating B cells, cluster of differentiation (CD)-8+ T cells, and natural killer (NK) cells, suggesting that the infiltration of immune cells could be associated with a good prognosis due to increased PKHD1L1 expression. Gene ontology (GO) analysis also revealed the relationship between PKHD1L1-co-altered genes and the activation of lymphocytes, including B and T cells. Collectively, this study shows that PKHD1L1 expression is positively correlated with a good prognosis via the induction of immune infiltration, suggesting that PKHD1L1 has potential prognostic value in SKCM and LUAD.

Identification of Molecular Markers Predicting the Outcome of Anti-thrombotic Therapy After Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome and Atrial fibrillation: Evidence from a Meta-analysis and Experimental Study

Acute coronary syndrome (ACS) and atrial fibrillation (AF) often coexist in clinical practice, and patients with these conditions often have a critical illness with high risk of both ischemia and bleeding. This study aims to report potential molecular markers for predicting the efficacy based on a meta-analysis of microarray data from the GEO database. In 40 patients with acute coronary syndrome (ACS) and atrial fibrillation (AF) treated with PCI, P2RX1's effects on platelet aggregation, medication resistance, and predictive value were examined. Twenty up-regulated genes in peripheral blood samples of ACS and AF patients were down-regulated after PCI, while 7 down-regulated genes were up-regulated. ACS affected eight potential genes. P2RX1, one of the four LASSO analysis-retrieved disease characteristic genes, accurately predicted AF patients' thrombosis risk and PCI's anti-thrombotic impact. Therefore, P2RX1 may be a molecular marker to predict the effect of anti-thrombotic therapy in patients with ACS and AF after PCI.

ACSM5 inhibits ligamentum flavum hypertrophy by regulating lipid accumulation mediated by FABP4/PPAR signaling pathway

BACKGROUND

Ligamentum flavum (LF) hypertrophy is the main cause of lumbar spinal canal stenosis (LSCS). Previous studies have shown that LF hypertrophy tissue exhibits abnormal lipid accumulation, but the regulatory mechanism remains unclear. The objective of this study was to explore the function and potential mechanism of ACSM5 in LF lipid accumulation.

METHODS

To assess the ACSM5 expression levels, lipid accumulation and triglyceride (TG) level in LF hypertrophy and normal tissue, we utilized RT-qPCR, western blot, oil red O staining, and TG assay kit. The pearson correlation coefficient assay was used to analyze the correlation between ACSM5 levels and lipid accumulation or TG levels in LF hypertrophy tissue. The role of ACSM5 in free fatty acids (FFA)-induced lipid accumulation in LF cells was assessed in vitro, and the role of ACSM5 in LF hypertrophy in mice was verified in vivo. To investigate the underlying mechanisms of ACSM5 regulating lipid accumulation in LF, we conducted the mRNA sequencing, bioinformatics analysis, and rescue experiments.

RESULTS

In this study, we found that ACSM5, which was significantly down-regulated in LF tissues, correlated with lipid accumulation. In vitro cell experiments demonstrated that overexpression of ACSM5 significantly inhibited FFA-induced lipid accumulation and fibrosis in LF cells. In vivo animal experiments further confirmed that overexpression of ACSM5 inhibited LF thickening, lipid accumulation, and fibrosis. Mechanistically, ACSM5 inhibited lipid accumulation of LF cells by inhibiting FABP4-mediated PPARγ signaling pathway, thereby improving hypertrophy and fibrosis of LF.

CONCLUSIONS

our findings elucidated the important role of ACSM5 in the regulation of LF lipid accumulation and provide insight into potential therapeutic interventions for the treatment of LF hypertrophy. This study further suggested that therapeutic strategies targeting lipid deposition may be an effective potential approach to treat LF hypertrophy-induced LSCS.

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma

PURPOSE

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC.

METHODS

TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways.

RESULTS

This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response.

CONCLUSION

Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Identification of a coagulation-related signature correlated with immune infiltration and their prognostic implications in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a fatal form of lung cancer with a poor prognosis. Coagulation system had been confirmed closely related to tumor progression and the hypercoagulable state encouraged the immune infiltration and development of tumor cells, leading to a poor prognosis in cancer patients. However, the use of the coagulation-related genes (CRGs) for prognosis in LUAD has yet to be determined. In this study, we constructed an immune-related signature (CRRS) and identified a potential coagulation-related biomarker (P2RX1).

METHODS

We obtained a total of 209 CRGs based on two coagulation-related KEGG pathways, then developed the CRRS signature by using the TCGA-LUAD RNA-seq data via the procedure of LASSO-Cox regression, stepwise-Cox regression, univariate and multivariate Cox regression. Grouped by the CRRS, Kaplan-Meier survival curves and receiver operating characteristic curves were drawn for the training and validation sets, respectively. In addition, single-sample gene set enrichment analysis was exploited to explore immune infiltration level. Moreover, immunophenotypes and immunotherapy grouped by CRRS were further analyzed.

RESULTS

We developed an immune-related signature (CRRS) composed of COL1A2, F2, PLAUR, C4BPA, and P2RX1 in LUAD. CRRS was an independent risk factor for overall survival and displayed stable and powerful performance. Additionally, CRRS possessed distinctly superior accuracy than traditional clinical variables and molecular features. Functional analysis indicated that the differentially high expressed genes in the low-risk group significantly enriched in T cell and B cell receptor signaling pathways. The low-risk group was sensitive to anti-PD-1/PD-L1 immunotherapy and displayed abundant immune infiltration and immune checkpoint gene expression. Finally, we identified an independent prognostic gene P2RX1. Low expression of P2RX1 associated with poor overall survival and decreased immune infiltration.

CONCLUSIONS

Our study revealed a significant correlation between CRRS and immune infiltration. CRRS could serve as a promising tool to improve the clinical outcomes for individual LUAD patients.

PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis

BACKGROUND

Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) <50%. Although single-agent pembrolizumab has also demonstrated some activity in this setting, no reliable biomarkers yet exist for selecting patients likely to respond to single-agent immunotherapy. The main purpose of the study was to identify potential new biomarkers associated with progression-free-survival (PFS) within a multiomics analysis.

METHODS

PEOPLE (NTC03447678) was a prospective phase II trial evaluating first-line pembrolizumab in patients with advanced EGFR and ALK wild type treatment-naïve NSCLC with PD-L1 <50%. Circulating immune profiling was performed by determination of absolute cell counts with multiparametric flow cytometry on freshly isolated whole blood samples at baseline and at first radiological evaluation. Gene expression profiling was performed using nCounter PanCancer IO 360 Panel (NanoString) on baseline tissue. Gut bacterial taxonomic abundance was obtained by shotgun metagenomic sequencing of stool samples at baseline. Omics data were analyzed with sequential univariate Cox proportional hazards regression predicting PFS, with Benjamini-Hochberg multiple comparisons correction. Biological features significant with univariate analysis were analyzed with multivariate least absolute shrinkage and selection operator (LASSO).

RESULTS

From May 2018 to October 2020, 65 patients were enrolled. Median follow-up and PFS were 26.4 and 2.9 months, respectively. LASSO integration analysis, with an optimal lambda of 0.28, showed that peripheral blood natural killer cells/CD56dimCD16+ (HR 0.56, 0.41-0.76, p=0.006) abundance at baseline and non-classical CD14dimCD16+monocytes (HR 0.52, 0.36-0.75, p=0.004), eosinophils (CD15+CD16-) (HR 0.62, 0.44-0.89, p=0.03) and lymphocytes (HR 0.32, 0.19-0.56, p=0.001) after first radiologic evaluation correlated with favorable PFS as well as high baseline expression levels of CD244 (HR 0.74, 0.62-0.87, p=0.05) protein tyrosine phosphatase receptor type C (HR 0.55, 0.38-0.81, p=0.098) and killer cell lectin like receptor B1 (HR 0.76, 0.66-0.89, p=0.05). Interferon-responsive factor 9 and cartilage oligomeric matrix protein genes correlated with unfavorable PFS (HR 3.03, 1.52-6.02, p 0.08 and HR 1.22, 1.08-1.37, p=0.06, corrected). No microbiome features were selected.

CONCLUSIONS

This multiomics approach was able to identify immune cell subsets and expression levels of genes associated to PFS in patients with PD-L1 <50% NSCLC treated with first-line pembrolizumab. These preliminary data will be confirmed in the larger multicentric international I3LUNG trial (NCT05537922).

TRIAL REGISTRATION NUMBER

2017-002841-31.

Diagnostic and prognostic value of STAP1 and AHNAK methylation in peripheral blood immune cells for HBV-related hepatopathy

Introduction

Although we had identified that the methylation of AHNAK was a good diagnostic marker for hepatopathy, here we speculate that there was also another marker, STAP1, whose methylation also involved in the detection of hepatopathy.

Methods

We investigated the methylation levels of the AHNAK and STAP1 in peripheral blood mononuclear cells of chronic hepatitis B (CHB) patients, compensatory liver cirrhosis (CLC) patients, decompensated liver cirrhosis (DCLC) patients, hepatocellular carcinoma (HCC) patients and healthy controls by methylation-specific PCR. We also evaluated the differences and changes of methylation and expression of AHNAK and STAP1 at different stages of liver disease using the TCGA and GEO public datasets.

Results

Methylation level of STAP1 in PBMC was positively correlated with the course of liver cancer. The combination of AHNAK and STAP1 methylation was able to predict differrent HBV related hepatopathy. The GEO datasets also supported that the methylation of AHNAK and STAP1 was associated with different types of hepatopathy. The TCGA data showed that the levels of methylation and expression of STAP1 were down-regulated in HCC. We also found the STAP1 methylation level in PBMC and T cells was associated with age, gender, alcohol drinking and anti-HBe. Hyper-methylation of STAP1 was correlated with the poor prognosis of patients but its expression had no association.

Conclusion

We concluded that combination of AHNAK and STAP1 methylation in peripheral blood immune cells can be used as a diagnostic marker for HBV related hepatopathy and STAP1 methylation may be a potential prognostic marker for HBV related HCC. Our clinical study registration number was ChiCTR2000039860.

Gene expression profiles in mesenchymal stromal cells from bone marrow, adipose tissue and lung tissue of COPD patients and controls

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is characterized by irreversible lung tissue damage. Novel regenerative strategies are urgently awaited. Cultured mesenchymal stem/stromal cells (MSCs) have shown promising results in experimental models of COPD, but differences between sources may impact on their potential use in therapeutic strategies in patients.

AIM

To assess the transcriptome of lung-derived MSCs (LMSCs), bone marrow-derived MSCs (BM-MSC) and adipose-derived MSCs (AD-MSCs) from COPD patients and non-COPD controls.

METHODS

We studied differences in gene expression profiles between the MSC-subtypes, as well as between COPD and control using RNA sequencing (RNA-seq).

RESULTS

We show that besides heterogeneity between donors, MSCs from different sources have strongly divergent gene signatures. The growth factors FGF10 and HGF were predominantly expressed in LMSCs. MSCs from all sources displayed altered expression profiles in COPD, with most pronounced significantly up- and downregulated genes in MSCs from adipose tissue. Pathway analysis revealed that the most differentially expressed genes in COPD-derived AD-MSCs are involved in extracellular matrix (ECM) binding and expression. In LMSCs, the gene that differed most strongly between COPD and control was CSGALNACT1, an ECM modulating gene.

CONCLUSION

Autologous MSCs from COPD patients display abnormalities with respect to their transcriptome, which were surprisingly most profound in MSCs from extrapulmonary sources. LMSCs may be optimally equipped for lung tissue repair because of the expression of specific growth factor genes.

Prognosis and personalized treatment prediction in lung adenocarcinoma: An in silico and in vitro strategy adopting cuproptosis related lncRNA towards precision oncology

Background: There is a rapid increase in lung adenocarcinomas (LUAD), and studies suggest associations between cuproptosis and the occurrence of various types of tumors. However, it remains unclear whether cuproptosis plays a role in LUAD prognosis. Methods: Dataset of the TCGA-LUAD was treated as training cohort, while validation cohort consisted of the merged datasets of the GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. Ten studied cuproptosis-related genes (CRG) were used to generated CRG clusters and CRG cluster-related differential expressed gene (CRG-DEG) clusters. The differently expressed lncRNA that with prognosis ability between the CRG-DEG clusters were put into a LASSO regression for cuproptosis-related lncRNA signature (CRLncSig). Kaplan-Meier estimator, Cox model, receiver operating characteristic (ROC), time-dependent AUC (tAUC), principal component analysis (PCA), and nomogram predictor were further deployed to confirm the model's accuracy. We examined the model's connections with other forms of regulated cell death, including apoptosis, necroptosis, pyroptosis, and ferroptosis. The immunotherapy ability of the signature was demonstrated by applying eight mainstream immunoinformatic algorithms, TMB, TIDE, and immune checkpoints. We evaluated the potential drugs for high risk CRLncSig LUADs. Real-time PCR in human LUAD tissues were performed to verify the CRLncSig expression pattern, and the signature's pan-cancer's ability was also assessed. Results: A nine-lncRNA signature, CRLncSig, was built and demonstrated owning prognostic power by applied to the validation cohort. Each of the signature genes was confirmed differentially expressed in the real world by real-time PCR. The CRLncSig correlated with 2,469/3,681 (67.07%) apoptosis-related genes, 13/20 (65.00%) necroptosis-related genes, 35/50 (70.00%) pyroptosis-related genes, and 238/380 (62.63%) ferroptosis-related genes. Immunotherapy analysis suggested that CRLncSig correlated with immune status, and checkpoints, KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28, were linked closely to our signature and were potentially suitable for LUAD immunotherapy targets. For those high-risk patients, we found three agents, gemcitabine, daunorubicin, and nobiletin. Finally, we found some of the CRLncSig lncRNAs potentially play a vital role in some types of cancer and need more attention in further studies. Conclusion: The results of this study suggest our cuproptosis-related CRLncSig can help to determine the outcome of LUAD and the effectiveness of immunotherapy, as well as help to better select targets and therapeutic agents.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Hypoxia-associated prognostic markers and competing endogenous RNA coexpression networks in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common form of non-small cell lung cancer (NSCLC). Hypoxia has been found in 50-60% of locally advanced solid tumors and is associated with poor prognosis in various tumors, including NSCLC. This study focused on hypoxia-associated molecular hallmarks in LUAD. Fifteen hypoxia-related genes were selected to define the hypoxia status of LUAD by ConsensusClusterPlus based on data from The Cancer Genome Atlas (TCGA). Then, we investigated the immune status under different hypoxia statuses. Subsequently, we constructed prognostic models based on hypoxia-related differentially expressed genes (DEGs), identified hypoxia-related microRNAs, lncRNAs and mRNAs, and built a network based on the competing endogenous RNA (ceRNA) theory. Two clusters (Cluster 1 and Cluster 2) were identified with different hypoxia statuses. Cluster 1 was defined as the hypoxia subgroup, in which all 15 hypoxia-associated genes were upregulated. The infiltration of CD4+ T cells and Tfh cells was lower, while the infiltration of regulatory T (Treg) cells, the expression of PD-1/PD-L1 and TMB scores were higher in Cluster 1, indicating an immunosuppressive status. Based on the DEGs, a risk signature containing 7 genes was established. Furthermore, three differentially expressed microRNAs (hsa-miR-9, hsa-miR-31, hsa-miR-196b) associated with prognosis under different hypoxia clusters and their related mRNAs and lncRNAs were identified, and a ceRNA network was built. This study showed that hypoxia was associated with poor prognosis in LUAD and explored the potential mechanism from the perspective of the gene signature and ceRNA theory.

A more novel and robust gene signature predicts outcome in patients with esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. The latest research has displayed that tumor immune cell infiltration (ICI) is closely connected with the ESCC patients' clinical prognosis. This study was designed to construct a gene signature based on the ICI of ESCC to predict prognosis.

METHODS

Based on the selection criteria we set, the eligible ESCC cases from the GSE53625 and TCGA-ESCA datasets were chosen for the training cohort and the validation cohort, respectively. Unsupervised clustering detailed grouped ESCC cases of the training cohort based on the ICI profile. We determined the differential expression genes (DEGs) between the ICI clusters, and, subsequently, we adopted the univariate Cox analysis to recognize DEGs with prognostic potential. These screened DEGs underwent a Lasso regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS. More importantly, we listed similar studies in the most recent year and compared theirs with ours. We performed the functional annotation, immune relevant signature correlation analysis, and immune infiltrating analysis to thoroughly understand the functional mechanism of the signature and the immune cells' roles in the gene signature's predicting capacity.

RESULTS

A sixteen-gene signature (ARSD, BCAT1, BIK, CLDN11, DLEU7-AS1, GGH, IGFBP2, LINC01037, LINC01446, LINC01497, M1AP, PCSK2, PCSK5, PPP2R2A, TIGD7, and TMSB4X) was generated from the Lasso model. We then confirmed the signature as having solid and stable prognostic capacity by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GSEA uncovered the specifically mechanism of action related to the gene signature. Two immune relevant signatures, including GZMA and LAG3 were identified associating with our signature. The immune-infiltrating analysis identified crucial roles of resting mast cells, which potentially support the sixteen-gene signature's prognosis ability.

CONCLUSIONS

We discovered a robust sixteen-gene signature that can accurately predict ESCC prognosis. The immune relevant signatures, GZMA and LAG3, and resting mast cells infiltrating were closely linked to the sixteen-gene signature's ability.

Establishment of lung adenocarcinoma classification and risk model based on necroptosis-related genes

Lung adenocarcinoma (LUAD) is the most widely known histological subtype of lung cancer. Its classification is significant for the characteristic evaluation of patients. The aim of this research is to assess the categorization of LUAD and its risk model based on necroptosis and to investigate its potential regulatory mechanisms for diagnosing and treating LUAD. According to the expression profile data along with the clinical information related to LUAD from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we constructed a consistency matrix through consistency clustering, and used the ConsensusClusterPlus as the measurement distance to cluster and subtype the samples, and performed gene set enrichment analysis and immune infiltration analysis. Least absolute shrinkage and selection operator (Lasso) regression was utilized for obtaining prognostic significant necroptosis phenotype-related genes. Finally, we measured each patient's riskscore (RS) and build a risk model, and predicted the effect of immunotherapy for different groups of risk factors in the model. Three molecular subtypes of LUAD were obtained by cluster analysis of necroptosis-related genes in LUAD samples. Compared with C1, C3 had a better prognosis and higher immune cell infiltration. The prognosis of the C1 subtype was poor and had a high clinical grade. The proportion of Stage II, Stage III, and Stage IV was much more in comparison with that of the other two subtypes. TP53 gene had a high mutation frequency in the C1 subtype. Gene Set Enrichment Analysis (GSEA) indicated that the aberrant pathways in the C1 and C3 subtypes mainly included some cell cycle-related pathways. In addition, seven genes were identified as related genes of necroptosis phenotype affecting prognosis. High RS had a poor prognosis, while low RS had a good prognosis. The RS was verified to have a strong ability to predict survival. LUAD can be classified by the genes linked with cell necrosis and apoptosis. The difference among various types is helpful to deepen the understanding of LUAD. In addition, a risk model was constructed based. In conclusion, this study provides potential detection targets and treatment methods for LUAD from a new perspective.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.

Methods

Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.

Results

A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.

Conclusions

Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.

A universal co-expression gene network and prognostic model for hepatic-biliary-pancreatic cancers identified by integrative analyses

Hepatic, biliary and pancreatic cancers are a diverse set of malignancies with poor prognoses. It is possible that common molecular mechanisms are involved in the carcinogenesis of these cancers. Here, we identified LINC01537 and seven protein-coding genes by integrative analysis of transcriptomes of mRNAs, microRNAs and long non-coding RNAs from cholangiocarcinoma, hepatocellular carcinoma and pancreatic adenocarcinoma cohorts in TCGA. A predictive model constructed from seven biomarkers was established to successfully predict the survival rate of patients, which was then further verified in external cohorts. Additionally, patients with high-risk scores in our model were prone to epithelial-mesenchymal transition. Finally, activation of the biomarker PDE2A significantly attenuated migration and epithelial-mesenchymal transition in the HepG2 liver cancer cell line.

CD8+ T cell-associated genes MS4A1 and TNFRSF17 are prognostic markers and inhibit the progression of colon cancer

Background

Colon cancer (CC) is among the top three diseases with the highest morbidity and mortality rates worldwide. Its increasing incidence imposes a major global health burden. Immune checkpoint inhibitors, such as anti-PD-1 and anti-PD-L1, can be used for the treatment of CC; however, most patients with CC are resistant to immunotherapy. Therefore, identification of biomarkers that can predict immunotherapy sensitivity is necessary for selecting patients with CC who are eligible for immunotherapy.

Methods

Differentially expressed genes associated with the high infiltration of CD8+ T cells were identified in CC and para-cancerous samples via bioinformatic analysis. Kaplan–Meier survival analysis revealed that MS4A1 and TNFRSF17 were associated with the overall survival of patients with CC. Cellular experiments were performed for verification, and the protein expression of target genes was determined via immunohistochemical staining of CC and the adjacent healthy tissues. The proliferation, migration and invasion abilities of CC cells with high expression of target genes were determined via in vitro experiments.

Results

Differential gene expression, weighted gene co-expression and survival analyses revealed that patients with CC with high expression of MS4A1 and TNFRSF17 had longer overall survival. The expression of these two genes was lower in CC tissues than in healthy colon tissues and was remarkably associated with the infiltration of various immune cells, including CD8+ T cells, in the tumour microenvironment (TME) of CC. Patients with CC with high expression of MS4A1 and TNFRSF17 were more sensitive to immunotherapy. Quantitative reverse transcription-polymerase chain reaction, western blotting and immunohistochemical staining validated the differential expression of MS4A1 and TNFRSF17. In addition, Cell Counting Kit-8, wound healing and transwell assays revealed that the proliferation, migration and invasion abilities of CC cells were weakened after overexpression of MS4A1 and TNFRSF17.

Conclusions

The core genes MS4A1 and TNFRSF17 can be used as markers to predict the sensitivity of patients with CC to immunotherapy and have potential applications in gene therapy to inhibit CC progression.

Prognostic Modeling of Lung Adenocarcinoma Based on Hypoxia and Ferroptosis-Related Genes

Background. It is well known that hypoxia and ferroptosis are intimately connected with tumor development. The purpose of this investigation was to identify whether they have a prognostic signature. To this end, genes related to hypoxia and ferroptosis scores were investigated using bioinformatics analysis to stratify the risk of lung adenocarcinoma. Methods. Hypoxia and ferroptosis scores were estimated using The Cancer Genome Atlas (TCGA) database-derived cohort transcriptome profiles via the single sample gene set enrichment analysis (ssGSEA) algorithm. The candidate genes associated with hypoxia and ferroptosis scores were identified using weighted correlation network analysis (WGCNA) and differential expression analysis. The prognostic genes in this study were discovered using the Cox regression (CR) model in conjunction with the LASSO method, which was then utilized to create a prognostic signature. The efficacy, accuracy, and clinical value of the prognostic model were evaluated using an independent validation cohort, Receiver Operator Characteristic (ROC) curve, and nomogram. The analysis of function and immune cell infiltration was also carried out. Results. Here, we appraised 152 candidate genes expressed not the same, which were related to hypoxia and ferroptosis for prognostic modeling in The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort, and these genes were further validated in the GSE31210 cohort. We found that the 14-gene-based prognostic model, utilizing MAPK4, TNS4, WFDC2, FSTL3, ITGA2, KLK11, PHLDB2, VGLL3, SNX30, KCNQ3, SMAD9, ANGPTL4, LAMA3, and STK32A, performed well in predicting the prognosis in lung adenocarcinoma. ROC and nomogram analyses showed that risk scores based on prognostic signatures provided desirable predictive accuracy and clinical utility. Moreover, gene set variance analysis showed differential enrichment of 33 hallmark gene sets between different risk groups. Additionally, our results indicated that a higher risk score will lead to more fibroblasts and activated CD4 T  cells but fewer myeloid dendritic cells, endothelial cells, eosinophils, immature dendritic cells, and neutrophils. Conclusion. Our research found a 14-gene signature and established a nomogram that accurately predicted the prognosis in patients with lung adenocarcinoma. Clinical decision-making and therapeutic customization may benefit from these results, which may serve as a valuable reference in the future.

Cross-talk between necroptosis-related lncRNAs to construct a novel signature and predict the immune landscape of lung adenocarcinoma patients

Background: As a new style of cell death, necroptosis plays a crucial role in tumor immune microenvironment. LncRNAs have been identified to act as competitive RNAs to influence genes involved in necroptosis. Therefore, we aim to create a signature based on necroptosis-related lncRNAs to predict the prognosis and immune landscape of lung adenocarcinoma (LUAD) patients in this study.

Methods: TCGA database was used to acquire RNA sequencing (RNA-Seq) data and clinical information for 59 lung normal samples and 535 lung adenocarcinoma samples. The Pearson correlation analysis, univariate cox regression analysis and least absolute shrinkage and selection operator (LASSO) cox regression were performed to construct the prognostic NRlncRNAs signature. Then we used Kaplan-Meier (K-M) analysis, time-dependent ROC curves, univariate and multivariate cox regression analysis, and nomogram to validate this signature. In addition, GO, KEGG, and GSVA were analyzed to investigate the potential molecular mechanism. Moreover, we analyzed the relationship between our identified signature and immune microenvironment, TMB, and some clinical characteristics. Finally, we detected the expression of the six necroptosis-related lncRNAs in cells and tissues.

Results: We constructed a NRlncRNAs signature consisting of six lncRNAs (FRMD6-AS1, LINC01480, FAM83A-AS1, FRMD6-AS1, MED4-AS1, and LINC01415) in LUAD. LUAD patients with high risk scores had lower chance of survival with an AUC of 0.739, 0.709, and 0.733 for 1-year, 3-year, and 5-year respectively. The results based on GO, KEGG, and GSVA enrichment analysis demonstrated that NRlncRNAs signature-related genes were mainly correlated with immune pathways, metabolic-and cell growth-related pathways, cell cycle, and apoptosis. Moreover, the risk score was correlated with the immune status of LUAD patients. Patients with higher risk scores had lower ESTIMATE scores and higher TIDE scores. The risk score was positively correlated with TMB. LINC01415, FRMD6-AS1 and FAM83A-AS1 were significantly overexpressed in lung adenocarcinoma, while the expression levels of MED4-AS1 and LINC01480 were lower in lung adenocarcinoma.

Conclusion: Overall, an innovative prognostic signature based on NRlncRNAs was developed for LUAD through comprehensive bioinformatics analysis, which can act as a predictor of immunotherapy and may provide guidance for clinicians.

Comprehensive Analysis of RELL2 as a Potential Biomarker Associated with Tumor Immune Infiltrating Cells in a Pan-Cancer Analysis

Background

Receptor expressed in lymphoid tissues-like 2 (RELL2), which is a member of RELT family, is closely associated with the plasma membrane and acts as a modulator for RELT signaling. Overexpression of RELL2 induces the activation of MAPK14/p38 cascade and apoptosis. However, whether RELL2 contributes to cancers remains unclear. Here, we examined its role in cancer patient prognosis and various tumors.

Methods

We used several bioinformatics methods, specifically gene set enrichment analysis (GSEA), ScanNeo, and ESTIMATE, to analyze the CCLE dataset, GTEx dataset, and TCGA dataset. We investigated the possible association of RELL2 with the microsatellite instability (MSI) of various tumors, tumor mutational burden (TMB), immune checkpoint, immune neoantigens, immune microenvironment, and patient prognosis.

Result

RELL2 is highly expressed in cancer compared with normal tissues. RELL2 expression is linked with worse progression-free interval and overall survival in numerous cancers. In most cancers, high RELL2 expression was related to a poor prognosis. RELL2 expression was significantly associated with the tumor microenvironment, MSI, and TMB. RELL2 expression is strongly associated with phenotypes that are of major clinical significance, particularly those associated with immune neoantigens and the expression profiles of immune checkpoint genes in pan-cancer. RELL2 expression strongly linked with the expressions of methyltransferases and DNA repair genes. It also significantly correlated with multiple signaling pathways through gene set enrichment analysis.

Conclusion

RELL2 may be a prognostic biomarker in pan-cancer and may have an important function in tumorigenesis and progression.

NcRNAs-mediated P2RX1 expression correlates with clinical outcomes and immune infiltration in patients with breast invasive carcinoma

The development of novel treatments for breast invasive carcinoma (BC) has been stagnant. P2RX1, a member of the purinergic receptor family, has been found to have a prognostic impact in several tumors. Therefore, we analyzed the expression pattern of P2RX1 in pan-cancers including BC and its impact on survival and found that the expression level of P2RX1 was lower in BC compared with para-cancerous tissues, and higher P2RX1 expression indicated better prognoses. But real-time quantitative reverse transcription PCR (RT-qPCR) and Western blot detected that the P2RX1 expression in normal mammary epithelial cells was lower than that in tumor cells. Then we comprehensively analyzed the regulatory mechanism and protein-protein interaction network, and found that P2RX1 was significantly positively linked with immune cell infiltration and immune checkpoints.

Global Characterization of Metabolic Genes Regulating Survival and Immune Infiltration in Osteosarcoma

Background: The alterations in metabolic profile of tumors have been identified as one of the prognostic hallmarks of cancers, including osteosarcoma. These alterations are majorly controlled by groups of metabolically active genes. However, the regulation of metabolic gene signatures in tumor microenvironment of osteosarcoma has not been well explained.

Objectives: Thus, we investigated the sets of previously published metabolic genes in osteosarcoma patients and normal samples.

Methods: We applied computational techniques to identify metabolic genes involved in the immune function of tumor microenvironment (TME) and survival and prognosis of the osteosarcoma patients. Potential candidate gene PAICS (phosphoribosyl aminoimidazole carboxylase, phosphoribosyl aminoimidazole succino carboxamide synthetase) was chosen for further studies in osteosarcoma cell lines for its role in cell proliferation, migration and apoptosis.

Results: Our analyses identified a list of metabolic genes differentially expressed in osteosarcoma tissues. Next, we scrutinized the list of genes correlated with survival and immune cells, followed by clustering osteosarcoma patients into three categories: C1, C2, and C3. These analyses led us to choose PAICS as potential candidate gene as its expression showed association with poor survival and negative correlation with the immune cells. Furthermore, we established that loss of PAICS induced apoptosis and inhibited proliferation, migration, and wound healing in HOS and MG-63 cell lines. Finally, the results were supported by constructing and validating a prediction model for prognosis of the osteosarcoma patients.

Conclusion: Here, we conclude that metabolic genes specifically PAICS play an integral role in the immune cell infiltration in osteosarcoma TME, as well as cancer development and metastasis.

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

Identification of Novel Subtypes in Lung Adenocarcinoma: Evidence from Gene Set Variation Analysis in Tumor and Adjacent Nontumor Samples

In patients with lung adenocarcinoma (LUAD), the prognostic role of adjacent nontumor tissues is still unknown. Alterations in the activity of immunologic and hallmark gene sets in adjacent nontumor tissues may have a potential influence on cell proliferation of normal lung cell after pulmonary lobectomy. We sought to discover LUAD subgroups and prognostic gene sets based on changes in gene set activity in tumor and adjacent nontumor tissues. Firstly, we used gene set variation analysis (GSVA) to characterize the activity changes of 4922 gene sets in LUAD and nontumor samples. Luckily, we identified three novel LUAD subtypes using the nonnegative matrix factorization (NMF) algorithm. In detailed, patients with subtype-3 had a favorable prognosis, but subtypes 1 and 2 had a bad prognosis. In addition, patients with subtype-3 in the validation cohort also lived longer. Meanwhile, using the LASSO-Cox algorithm, we discovered 15 prognostic gene sets in tumors (T gene sets) and two prognostic gene sets in adjacent nontumors (N gene sets). Interestingly, genes from N gene sets were related with immune response in nontumor tissues, but genes from T gene sets were correlated with DNA damaging and repairing in tumor tissues. These findings highlighted the possibility of a stronger immune response in nearby nontumor tissues. In conclusion, our study established a theoretical foundation for selecting therapy strategy for LUAD patients that should be guided by changes in activity in tumor and adjacent nontumor tissues, particularly after pulmonary lobectomy.

Systematic Pan-Cancer Analysis of KLRB1 with Prognostic Value and Immunological Activity across Human Tumors

Introduction

KLRB1 is a gene encoding CD161 expressed in NK cells and some T cell subsets. At present, KLRB1 is believed to affect tumorigenesis and development by regulating the cytotoxicity of NK cells in several cancers. However, there is a lack of systematic reviews of KLRB1 in a variety of malignancies.

Objectives

Hence, our research is aimed at providing a relatively comprehensive understanding of the role of KLRB1 in different types of cancer, paving the way for further research on the molecular mechanism and immunotherapy potential of KLRB1.

Methods

In this study, we used relevant public databases, including TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), CCLE (Cancer Cell Line Encyclopedia), GTEx (Genotype Tissue-Expression), and HPA (Human Protein Atlas), to perform a pan-cancer analysis of KLRB1 across 33 types of cancer. We explored the potential molecular mechanism of KLRB1 in clinical prognosis and tumor immunity from the aspects of gene expression, survival status, clinical phenotype, immune infiltration, immunotherapy response, and chemotherapeutic drug sensitivity.

Results

KLRB1 was downregulated in 13 cancers while upregulated in kidney cancer. Patients with high expression of KLRB1 have a better prognosis in most types of cancer. Moreover, the KLRB1 expression level is related to TMB and MSI and related to various immune signatures of tumor. The expression of KLRB1 can affect tumor immune cell infiltration. KLRB1 expression level can also affect the sensitivity of chemotherapy drugs.

Conclusions

KLRB1 may be a prognostic and immunological biomarker across tumors. At the same time, KLRB1 expression can reflect the sensitivity of cancer patients to chemotherapy drugs. KLRB1 may become a new target for immunotherapy.

Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

Genome-wide identification of m6A-associated functional SNPs as potential functional variants for thyroid cancer

m6A methylation has been demonstrated to be one of the most important epigenetic regulation mechanisms in cell differentiation and cancer development especially m6A derived diagnostic and prognostic biomarkers have been identified in the past several years. However, systemic investigation to the interaction between germline single-nucleotide polymorphisms (SNPs) and m6A has not been conducted yet. In this study, we collected previous identified significant thyroid cancer associated SNPs from UKB cohort (358 cases and 407,399 controls) and ICR cohort (3,001 patients and 287,550 controls) and thyroid eQTL (sample size = 574 from GTEx project) and m6A-SNP (N = 1,678,126) were applied to prioritize the candidate SNPs. Finally, five candidate genes (PLEKHA8, SMUG1, CDC123, RMI2, ACSM5) were identified to be thyroid cancer associated m6A-related genetic susceptibility. Loss and gain function studies of m6A writer proteins confirm that ACSM5 is regulated by m6A methylation of mRNA. Moreover, ACSM5 is downregulated in thyroid cancer and inversely correlated with PTC malignancy and patient survival. Together, our study highlight mRNA-seq and m6A-seq double analysis provided a novel approach to identify cancer biomarkers and understanding the heterogeneity of human cancers.

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes

Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups (P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.

Integrative analysis of genome-wide DNA methylation and single-nucleotide polymorphism identified ACSM5 as a suppressor of lumbar ligamentum flavum hypertrophy

BACKGROUND

Hypertrophy of ligamentum flavum (HLF) is a common lumbar degeneration disease (LDD) with typical symptoms of low back pain and limb numbness owing to an abnormal pressure on spinal nerves. Previous studies revealed HLF might be caused by fibrosis, inflammatory, and other bio-pathways. However, a global analysis of HLF is needed severely.

METHODS

A genome-wide DNA methylation and single-nucleotide polymorphism analysis were performed from five LDD patients with HLF and five LDD patients without HLF. Comprehensive integrated analysis was performed using bioinformatics analysis and the validated experiments including Sanger sequencing, methylation-specific PCR, qPCR and ROC analysis. Furthermore, the function of novel genes in ligamentum flavum cells (LFCs) was detected to explore the molecular mechanism in HLF through knock down experiment, overexpression experiment, CCK8 assay, apoptosis assay, and so on.

RESULTS

We identified 69 SNP genes and 735 661 differentially methylated sites that were enriched in extracellular matrix, inflammatory, and cell proliferation. A comprehensive analysis demonstrated key genes in regulating the development of HLF including ACSM5. Furthermore, the hypermethylation of ACSM5 that was mediated by DNMT1 led to downregulation of ACSM5 expression, promoted the proliferation and fibrosis, and inhibited the apoptosis of LFCs.

CONCLUSION

This study revealed that DNMT1/ACSM5 signaling could enhance HLF properties in vitro as a potential therapeutic strategy for HLF.

A Robust Seven-Gene Signature Associated With Tumor Microenvironment to Predict Survival Outcomes of Patients With Stage III-IV Lung Adenocarcinoma

Background

Due to the relatively insidious early symptoms of lung adenocarcinoma (LUAD), most LUAD patients are at an advanced stage at the time of diagnosis and lose the best chance of surgical resection. Mounting evidence suggested that the tumor microenvironment (TME) was highly correlated with tumor occurrence, progress, and prognosis. However, TME in advanced LUAD remained to be studied and reliable prognostic signatures based on TME in advanced LUAD also had not been well-established. This study aimed to understand the cell composition and function of TME and construct a gene signature associated with TME in advanced LUAD.

Methods

The immune, stromal, and ESTIMATE scores of each sample from The Cancer Genome Atlas (TCGA) database were, respectively, calculated using an ESTIMATE algorithm. The LASSO and Cox regression model were applied to select prognostic genes and to construct a gene signature associated with TME. Two independent datasets from the Gene Expression Omnibus (GEO) were used for external validation. Twenty-two subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the CIBERSORT algorithm.

Results

Favorable overall survival (OS) and progression-free survival (PFS) were found in patients with high immune score (p = 0.048 and p = 0.028; respectively) and stromal score (p = 0.024 and p = 0.025; respectively). Based on the immune and stromal scores, 453 differentially expressed genes (DEGs) were identified. Using the LASSO and Cox regression model, a seven-gene signature containing AFAP1L2, CAMK1D, LOXL2, PIK3CG, PLEKHG1, RARRES2, and SPP1 was identified to construct a risk stratification model. The OS and PFS of the high-risk group were significantly worse than that of the low-risk group (p < 0.001 and p < 0.001; respectively). The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the seven-gene signature. Similar findings were validated in two independent cohorts. In addition, the proportion of macrophages M2 and Tregs was higher in high-risk patients (p = 0.041 and p = 0.022, respectively).

Conclusion

Our study established and validated a seven-gene signature associated with TME, which might serve as a prognosis stratification tool to predict survival outcomes of advanced LUAD patients. In addition, macrophages M2 polarization may lead to worse prognosis in patients with advanced LUAD.

Identification of a Novel Prognostic Gene Signature From the Immune Cell Infiltration Landscape of Osteosarcoma

Background: The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. Latestresearch hasdisplayed that tumor immune cell infiltration (ICI) is associated with the clinical outcome of patients with osteosarcoma (OS). This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes.

Methods: The TARGET-OS dataset was used for model training, while the GSE21257 dataset was taken forvalidation. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan–Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO regression analysis and produced a gene signature, which was next assessed with the Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. GSEAswere deployed to further study the functional mechanism of the signature. We conducted an analysis of 22 TICsfor identifying the role of TICs in the gene signature’s prognosis ability.

Results: Data from the training cohort were used to generate a nine-gene signature. The Kaplan–Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS validated the signature’s capacity and independence in predicting the outcomes of OS patients in the validation cohort. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis revealed the mechanism related to the gene signature specifically. The immune-infiltration analysis uncoveredkey roles for activated mast cells in the prognosis of OS.

Conclusion: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can predict OS outcome precisely and is strongly linked to activated mast cells.

FURIN correlated with immune infiltration serves as a potential biomarker in SARS-CoV-2 infection-related lung adenocarcinoma

FURIN, as a proprotein convertase, has been found to be expressed in a variety of cancers and plays an important role in cancer. In addition, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires FURIN to enter human cells. However, the role of FURIN in lung adenocarcinoma remains unclear. And the expression of SARS-CoV-2 related gene in lung adenocarcinoma has not been clarified. Therefore, in order to explore the prognostic value and mechanism of FURIN in lung adenocarcinoma, we performed bioinformatics analysis with Oncomine, Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis, human protein atlas, UALCAN, PrognoScan, Kaplan–Meier plotter, cBioPortal and LinkedOmics databases. And then we used GSE44274 in the GEO (Gene Expression Omnibus) database to analyze the expression of FURIN in LUAD patients who infected with SARS-CoV. FURIN was highly expressed in lung adenocarcinoma and was significantly associated with poor overall survival. FURIN expression was found to be correlated with six major permeable immune cells and with macrophage immune marker in LUAD patients. In addition, SARS-CoV-2 infection might affect the expression of FURIN. FURIN can be used as a promising biomarker for determining prognosis and immune infiltration in LUAD patients.

A Novel Intercellular Communication-Associated Gene Signature for Prognostic Prediction and Clinical Value in Patients With Lung Adenocarcinoma

Background

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD.

Methods

Eight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication.

Results

Eight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset.

Conclusion

The present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.

The Prognosis and Immune Checkpoint Blockade Efficacy Prediction of Tumor-Infiltrating Immune Cells in Lung Cancer

Backgrounds

The high morbidity and mortality of lung cancer are serious public health problems. The prognosis of lung cancer and whether to apply immune checkpoint blockade (ICB) are currently urgent problems to be solved.

Methods

Using R software, we performed Kaplan–Meier (K-M) analysis, Cox regression analysis, functional enrichment analysis, Spearman correlation analysis, and the single-sample gene set enrichment analysis.

Results

On the Tumor IMmune Estimation Resource (TIMER2.0) website, we calculated the abundance of tumor-infiltrating immune cells (TIICs) of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. B cell and myeloid dendritic cell (DC1) were independent prognostic factors for LUAD and LUSC patients, respectively. Enrichment analysis confirmed that genes highly related to B cell or DC1 were closely related to the immune activation of lung cancer patients. In terms of adaptive immune resistance markers, CD8A, CD8B, immunomodulators (immunostimulants, major histocompatibility complex, receptors, and chemokines), immune-related pathways, tumor microenvironment score, and TIICs, high B cell/DC1 infiltration tissue was inflamed and immune-activated and might benefit more from the ICB. Genes most related to B cell [CD19, toll-like receptor 10 (TLR10), and Fc receptor-like A (FCRLA)] and DC1 (ITGB2, LAPTM5, and SLC7A7) partially clarified the roles of B cell/DC1 in predicting ICB efficacy. Among the 186 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, there were three and four KEGG pathways, which partially explained the molecular mechanisms by which B cell and DC1 simultaneously predicted the prognosis and efficacy of immunotherapy, respectively. Among five immune subtypes, the abundance of B cell/DC1 and expression of six hub genes were higher in immune C2, C3, and C6.

Conclusion

B cell and DC1 could predict the prognosis and ICB efficacy of LUAD and LUSC patients, respectively. The six hub genes and seven KEGG pathways might be novel immunotherapy targets. Immune C2, C3, and C6 subtypes of lung cancer patients might benefit more from ICB therapy.

Predicting lung adenocarcinoma prognosis with a novel risk scoring based on platelet-related gene expression

Lung adenocarcinoma is the most common subtype of non-small cell lung cancer, and platelet receptor-related genes are related to its occurrence and progression. A new prognostic indicator based on platelet receptor-related genes was developed with multivariate COX analysis. Prognostic markers based on platelet-related risk score perform moderately in prognosis prediction. The functional annotation of this risk model in high-risk patients shows that the pathways related to cell cycle, glycolysis and platelet-derived related factors are rich. It is worth noting that somatic mutation analysis shows that TTN and MUC16 have higher mutation burdens in high-risk patients. Moreover, the differential genes of high- and low-risk groups are regulated by copy number variation and miRNA. And we provide a free online nomogram web tool based on clinical factors and the risk score (https://wsxzaq.shinyapps.io/wsxzaq_nomogram/). The score has been verified among three independent external cohorts (GSE13213, GSE68465 and GSE72094), and is still an independent risk factor for lung adenocarcinoma. In addition, among the other 6 cancers, the OS prognosis of high and low-risk groups of PRS is different (P < 0.05). Our research results have screened multiple platelet differential genes with clinical significance and constructed a meaningful prognostic risk score (PRS).