Analyzing the characteristics of immune cell infiltration in lung adenocarcinoma via bioinformatics to predict the effect of immunotherapy

Identification of an immune-related genes signature in lung adenocarcinoma to predict survival and response to immune checkpoint inhibitors

BACKGROUND

Although advances in immune checkpoint inhibitor (ICI) research have provided a new treatment approach for lung adenocarcinoma (LUAD) patients, their survival is still unsatisfactory, and there are issues in the era of response prediction to immunotherapy.

METHODS

Using bioinformatics methods, a prognostic signature was constructed, and its predictive ability was validated both in the internal and external datasets (GSE68465). We also explored the tumor-infiltrating immune cells, mutation profiles, and immunophenoscore (IPS) in the low-and high-risk groups.

RESULTS

As far as we are aware, this is the first study which introduces a novel prognostic signature model using BIRC5, CBLC, S100P, SHC3, ANOS1, VIPR1, LGR4, PGC, and IGKV4.1. According to multivariate analysis, the 9-immune-related genes (IRGs) signature provided an independent prognostic factor for the overall survival (OS). The low-risk group had better OS, and the tumor mutation burden (TMB) was significantly lower in this group. Moreover, the risk scores were negatively associated with the tumor-infiltrating immune cells, like CD8+ T cells, macrophages, dendritic cells, and NK cells. In addition, the IPS were significantly higher in the low-risk group as they had higher gene expression of immune checkpoints, suggesting that ICIs could be a promising treatment option for low-risk LUAD patients.

CONCLUSION

The combination of these 9-IRGs not only could efficiently predict overall survival of LUAD patients but also show a powerful association with the expression of immune checkpoints and response to ICIs based on IPS; hoping this model paves the way for better stratification and management of patients in clinical practice.

Targeting BMP2 for therapeutic strategies against hepatocellular carcinoma

OBJECTIVES

This study aimed to investigate the role of BMP2 in hepatocellular carcinoma (HCC) growth and metastasis using a dual approach combining single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq.

METHODS

scRNA-seq data from the GEO database and bulk RNA-seq data from the TCGA database were analyzed. Differentially expressed marker genes of endothelial cells were identified and analyzed using enrichment analysis, PPI analysis, correlation analysis, and GSEA. In vitro, experiments were conducted using the Huh-7 HCC cell line, and in vivo, models of HCC growth and metastasis were established by knocking down BMP2.

RESULTS

The scRNA-seq analysis identified BMP2 as a key marker gene in endothelial cells of HCC samples. Elevated BMP2 expression correlated with poor prognosis in HCC. In vitro experiments showed that silencing BMP2 inhibited the proliferation, migration, and invasion of liver cancer cells. In vivo studies confirmed increased BMP2 expression in HCC tissues, promoting angiogenesis and HCC growth.

CONCLUSION

This study highlights the role of BMP2 in tumor angiogenesis and HCC progression. Targeting BMP2 could be a promising therapeutic strategy against HCC.

DEP domain containing 1 as a biomarker for poor prognosis in lung adenocarcinoma

Objective

The DEP domain-containing 1 (DEPDC1) gene is essential in the development and advancement of different types of cancer. This study is to examine the levels of DEPDC1 in lung adenocarcinoma (LUAD), and to determine its relationship with clinical results and immune response. The goal is to assess its potential as a biomarker and therapeutic target for LUAD.

Methods

By comprehensively utilizing the Cancer Genome Atlas (TCGA), gene Expression Synthesis (GEO), UALCAN, cBioPortal, TISIDB databases and online platforms, we conducted a bioinformatics analysis to investigate DEPDC1 gene survival analysis, prognostic diagnosis, prognostic survival, immune cell infiltration, DNA methylation, and the correlation of genetic mutations in LUAD. The results were validated through cell assay and immunohistochemical staining.

Results

DEPDC1 shows high levels of expression in the majority of tumors, with its expression being notably elevated in LUAD compablue to normal tissues. The expression of DEPDC1 varies based on the clinical characteristics of patients with LUAD. DEPDC1 expression affects the survival prognosis and prognostic model construction of LUAD patients. In addition, the presence of DEPDC1 is linked to immune infiltration. Various chemokines and chemokine receptors, immunoinhibitors and immune-stimulators in LUAD are significantly correlated with DEPDC1 methylation levels. Cell experiments confirmed through qPCR that the mRNA expression of DEPDC1 in LUAD was markedly elevated in comparison to the normal population, and immunohistochemistry showed positive DEPDC1 expression in LUAD pathological sections.

Conclusion

Systematic analysis and experiments have verified that DEPDC1 serves as a biomarker for detecting early, prediction of survival, and evaluation of immune cell infiltration in LUAD.

Multi-omics and immunogenomics analysis revealed PFKFB3 as a targetable hallmark and mediates sunitinib resistance in papillary renal cell carcinoma: in silico study with laboratory verification

Glycolysis-related metabolic reprogramming is a central hallmark of human cancers, especially in renal cell carcinoma. However, the regulatory function of glycolytic signature in papillary RCC has not been well elucidated. In the present study, the glycolysis-immune predictive signature was constructed and validated using WGCNA, glycolysis-immune clustering analysis. PPI network of DEGs was constructed and visualized. Functional enrichments and patients' overall survival were analyzed. QRT-PCR experiments were performed to detect hub genes' expression and distribution, siRNA technology was used to silence targeted genes; cell proliferation and migration assays were applied to evaluate the biological function. Glucose concentration, lactate secretion, and ATP production were measured. Glycolysis-Immune Related Prognostic Index (GIRPI) was constructed and combined analyzed with single-cell RNA-seq. High-GIRPI signature predicted significantly poorer outcomes and relevant clinical features of pRCC patients. Moreover, GIRPI also participated in several pathways, which affected tumor immune microenvironment and provided potential therapeutic strategy. As a key glycolysis regulator, PFKFB3 could promote renal cancer cell proliferation and migration in vitro. Blocking of PFKFB3 by selective inhibitor PFK-015 or glycolytic inhibitor 2-DG significantly restrained renal cancer cells' neoplastic potential. PFK-015 and sunitinib could synergistically inhibit pRCC cells proliferation. Glycolysis-Immune Risk Signature is closely associated with pRCC prognosis, progression, immune infiltration, and therapeutic response. PFKFB3 may serve as a pivotal glycolysis regulator and mediates Sunitinib resistance in pRCC patients.

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

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

Identification of molecular subtypes and prognostic model to reveal immune infiltration and predict prognosis based on immunogenic cell death-related genes in lung adenocarcinoma

Immunogenic cell death (ICD) has been increasingly indicated to be related to caners. However, ICD's role in Lung adenocarcinoma (LUAD) is still not well investigated. Clinical data along with associated mRNA expression profiles from LUAD cases were collected in TCGA and GEO databases. 13 ICD-related genes were identified. Relations of ICD-related genes expression with prognosis of patients, tumor immune microenvironment (TIME) was analyzed. Then, candidate genes were identified and the prognostic signature were constructed. Afterwards, one nomogram incorporating those chosen clinical data together with risk scores were built. Finally, the effect of HSP90AA1, one gene of the prognostic signature, on LUAD cell were analyzed. Two clusters were identified, which were designated as the ICD-high or -low subtype according to ICD-related genes levels. ICD-high subgroup showed good prognosis, high immune cell infiltration degrees, and enhanced immune response signaling activity compared with ICD-low subtype. Moreover, we established and verified the risk signature based on ICD-related genes. High risk group predicted poor prognosis of LUAD independently and presented negative association with immune score and immune status. Furthermore, nomogram contributed to the accurate prediction of LUAD prognostic outcome. Finally, HSP90AA1 levels were remarkably elevated within tumor cells in comparison with healthy pulmonary epithelial cells. HSP90α, HSP90AA1 protein product, promoted growth, migration, and invasion of LUAD cells. Molecular subtypes and prognostic model were identified by incorporating ICD-related genes, and it was related to TIME and might be adopted for the accurate prediction of LUAD prognosis.

Identifying the cancer-associated fibroblast signature to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment that contribute toward the development of tumors. This study aimed to establish a new algorithm based on CAF scores to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma (LUSC). The RNA-seq data of LUSC patients were obtained from two databases and merged after removing inter-batch differences. The CAF-related data for each sample were obtained through three different algorithms. Consistency cluster analysis was performed to obtain different CAF clusters, which were analyzed to identify differentially expressed genes. These were subjected to uniform cluster analysis to obtain different gene clusters. The Boruta algorithm was used to calculate the CAF score. Three CAF clusters and two gene clusters were obtained, all of which differed in their patient prognoses and the content of infiltrating immune cells. Patients with high CAF scores exhibited worse overall survival, higher expression of biomarkers related to immune checkpoints and immune activity, and lower tumor mutation burden. The CAF score could also predict the immunotherapy response of patients. This study suggests that the CAF score can accurately predict the prognosis and immunotherapy response of LUSC patients.

MCM4 acts as a biomarker for LUAD prognosis

MCM4 forms the pre-replication complex (MCM2-7) with five other minichromosome maintenance (MCM) proteins. This complex binds to replication origins at G1 stage in cell cycle process, playing a critical role in DNA replication initiation. Recently, MCM4 is reported to have a complex interaction with multiple cancer progression, including gastric, ovarian and cervical cancer. Here, this study mainly focused on the expression of MCM4 and its values in lung adenocarcinoma (LUAD). MCM4 was highly expressed in LUAD tumours and cells, and had an important effect on the overall survival. Overexpression of MCM4 promoted the proliferation, and suppressed the apoptosis in LUAD cells. However, MCM4 silence led to the opposite results. In vivo, knockdown of MCM4 inhibited tumour volume and weight in xenograft mouse model. As a member of DNA helicase, knockdown of MCM4 caused cell cycle arrest at G1 stage through inducing the expression of P21, a CDK inhibitor. These findings indicate that MCM4 may be a possible new therapeutic target for LUAD in the future.

Genomic analysis of immunogenic cell death-related subtypes for predicting prognosis and immunotherapy outcomes in glioblastoma multiforme

Immunogenic cell death (ICD), a unique form of cancer cell death, has therapeutic potential in anti-tumour immunotherapy. The aim of this study is to explore the predictive potential of ICD in the prognosis and immunotherapy outcomes of glioblastoma multiforme (GBM). RNA sequencing data and clinical information were downloaded from three databases. Unsupervised consistency clustering analysis was used to identify ICD-related clusters and gene clusters. Additionally, the ICD scores were determined using principal component analysis and the Boruta algorithm via dimensionality reduction techniques. Subsequently, three ICD-related clusters and three gene clusters with different prognoses were identified, with differences in specific tumour immune infiltration-related lymphocytes in these clusters. Moreover, the ICD score was well differentiated among patients with GBM, and the ICD score was considered an independent prognostic factor for patients with GBM. Furthermore, two datasets were used for the external validation of ICD scores as predictors of prognosis and immunotherapy outcomes. The validation analysis suggested that patients with high ICD scores had a worse prognosis. Additionally, a higher proportion of patients with high ICD scores were non-responsive to immunotherapy. Thus, the ICD score has the potential as a biomarker to predict the prognosis and immunotherapy outcomes of patients with GBM.

Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction

Cuproptosis is a newly form of cell death. Cuproptosis related lncRNA in lung adenocarcinoma (LUAD) has also not been fully elucidated. In the present study, we aimed to construct a prognostic signature based on cuproptosis-related lncRNA in LUAD and investigate its association with immunotherapy response. The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were obtained from TCGA database. The LASSO Cox regression was used to construct a prognostic signature. The CIBERSORT, ESTIMATE and ssGSEA algorithms were applied to assess the association between risk score and TME. TIDE score was applied to reflect the efficiency of immunotherapy response. The influence of overexpression of lncRNA TMPO-AS1 on A549 cell was also assessed by in vitro experiments. The lncRNA prognostic signature included AL606834.1, AL138778.1, AP000302.1, AC007384.1, AL161431.1, TMPO-AS1 and KIAA1671-AS1. Low-risk group exhibited much higher immune score, stromal score and ESTIMATE score, but lower tumor purity compared with high-risk groups. Also, low-risk group was associated with a much higher score of immune cells and immune related function sets, indicating an immune activation state. Low-risk patients had relative higher TIDE score and lower TMB. External validation using IMvigor210 immunotherapy cohort demonstrated that low-risk group had a better prognosis and might more easily benefit from immunotherapy. Overexpression of lncRNA TMPO-AS1 promoted the proliferation, migration and invasion of A549 cell line. The novel cuproptosis-related lncRNA signature could predict the prognosis of LUAD patients, and helped clinicians stratify patients appropriate for immunotherapy and determine individual therapeutic strategies.

RASAL3 predicts overall survival and CD8+ T lymphocyte infiltration in lung adenocarcinoma

RAS-activating protein-like 3 (RASAL3) is a synaptic Ras GTPase-activating protein (SynGAP) and a potential novel biomarker of CD8+ T cell infiltration in lung adenocarcinoma (LUAD). This study explored RASAL3 expression in LUAD, the prognostic impact of RASAL3 and the relationship with immune cell infiltration. RASAL3 expression in LUAD tissues was considerably low, with high RASAL3 expression associated with better overall survival, whereas the low expression was linked to advanced T, N, M classifications, TNM stage and lower grade. Furthermore, RASAL3 expression positively correlated with CD8+ T lymphocyte infiltration. In conclusion, RASAL3 expression is a potential prognostic and immunological biomarker of LUAD.

Investigating the evolution process of lung adenocarcinoma via random walk and dynamic network analysis

Lung adenocarcinoma (LUAD) is a typical disease regarded as having multi-stage progression. However, many existing methods often ignore the critical differences among these stages, thereby limiting their effectiveness for discovering key biological molecules and biological functions as signals at each stage. In this study, we propose a method to discover the evolution between biological molecules and biological functions by investigating the multi-stage biological molecules of LUAD. The method is based on the random walk algorithm and the Monte Carlo method to generate clusters as the modules, which were used as subgraphs of the differentiated biological molecules network in each stage. The connection between modules of adjacent stages is based on the measurement of the Jaccard coefficient. The online gene set enrichment analysis tool (DAVID) was used to obtain biological functions corresponding to the individual important modules. The core evolution network was constructed by combining the aforementioned two networks. Since the networks here are all dynamic, we also propose a strategy to visualize the dynamic information together in one network. Eventually, 12 core modules and 11 core biological functions were found through such evolutionary analyses. Among the core biological functions that we obtained, six functions are related to the disease, the biological function of neutrophil chemotaxis is not directly associated with LUAD but can serve as a predictor, two functions may serve as a predictive signal, and two functions need to be verified through more biological evidence. Compared with two alternative design methods, the method proposed in this study performed more efficiently.

Construction of a 5-methylcytosine-Related Molecular Signature to Inform the Prognosis and Immunotherapy of Lung Squamous Cell Carcinoma

BACKGROUND

Methylation of cytosine residues resulting in 5-methylcytosine (5-mC) is an important epigenetic modification associated with tumorigenesis. The present study explored the relationship between methylation, prognosis, and immunotherapy of patients suffering from lung squamous cell carcinoma (LUSC).

METHODS

RNA sequencing data and corresponding clinical information were downloaded, and preprocessed, and unsupervised consistent cluster analysis was used to identify 5-mC-related clusters and gene clusters. 5-mC scores were calculated using principal component analysis, and a Boruta algorithm was used to evaluate the relationship between tumor mutation burden (TMB), immune checkpoint inhibitor response, and prognosis of individual LUSC patients.

RESULTS

: Two 5-mC clusters and three gene clusters with different prognoses were identified. Patients with higher 5-mC scores showed worse prognoses, which was confirmed in multiple cohorts. Some immune-related biological functions and pathways were enriched in the high-5-mC score subtype.

CONCLUSION

The 5-mC score is a potential biomarker independent of TMB, which can be a decisive factor regarding immune treatment responses. Further, patients with low 5-mC scores may respond better to immunotherapy. The 5-mC score can thus be used as a potential biomarker for the prognosis of LUSC patients and their response to immunotherapy.

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI.

Comprehensive analysis of the prognostic value and immune implications of the TTK gene in lung adenocarcinoma: a meta-analysis and bioinformatics analysis

Background

High expression levels of the TTK gene are closely related to tumor occurrence and poor prognosis, as confirmed by some studies. Our study explored the prognosis of lung adenocarcinoma (LUAD) patients with different TTK levels and the possible pathological mechanism of TTK in LUAD.

Methods

We extensively searched literature databases and high-throughput sequencing databases and included relevant literature or datasets in the meta-analysis according to the inclusion and exclusion criteria. Hazard ratios (HRs) and 95% confidence intervals (CIs) related to TTK expression were calculated, publication bias was assessed, and sensitivity tests were performed. We also compared the relationship between cancer immune infiltrating cells and tumor mutation burden (TMB) in patients with different TTK expression levels via bioinformatics analysis. The half maximal inhibitory concentration (IC50) of some chemotherapeutic and targeted therapy drugs were calculated. The potential biological functions or pathways associated with different TTK expression levels were determined by gene set enrichment analysis (GSEA).

Results

The meta-analysis revealed that higher TTK expression level was significantly associated with poor prognosis in LUAD patients, both in overall survival (OS) and progression-free survival (PFS). The expression level of TTK was significantly correlated with presence of some immune cells and TMB. Tumors with higher TTK expression levels were mostly enriched for the cell cycle, DNA replication and homologous recombination pathways. In addition, patients with different TTK expression levels were differently sensitive to some antitumor drugs.

Conclusion

TTK may be a promising prognostic biomarker for LUAD and is worthy of further investigation.

The Overexpression of SLC25A13 Predicts Poor Prognosis and Is Correlated with Immune Cell Infiltration in Patients with Skin Cutaneous Melanoma

Purpose

Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers with poor prognosis due to its rapid progression towards metastasis. Thus, finding clinically relevant biomarkers for early diagnosis, prognosis, and therapy prediction is essential. This study focused on the identification of SLC25A13 as a novel biomarker for SKCM and is aimed at investigating the biological functions of solute carrier family 25 member 13 (SLC25A13) in the development of SKCM.

Methods

GEPIA was used to analyze the diagnostic and prognostic values of SLC25A13 in SKCM using the TCGA dataset. PrognoScan was used to validate the prognostic value of SLC25A13 and its coexpressed genes in SKCM. TISIDB was established to reveal the relationship between the expression of SLC25A13 and immune infiltration in SKCM. The protein expression of SLC25A13 in SKCM was evaluated by the Human Protein Atlas. The signaling pathways and biological functions of SLC25A13 in SKCM were analyzed by LinkOmics. Metascape was applied to analyze the functional enrichment analysis of SLC25A13. Protein-protein interaction analysis of SLC25A13 was performed by GeneMANIA.

Results

The mRNA and protein levels of SLC25A13 in the SKCM were much higher than those in the normal tissue. Furthermore, the overexpression of SLC25A13 predicts worse outcomes of SKCM patients. Moreover, the SLC25A13 expression was negatively correlated with the immune infiltration level of SKCM. The overexpression of SLC25A13 coexpressed genes, such as ACLY and AFG3L2, and SCL25A13 interacting genes also predicted the unfavorable prognosis of SKCM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of SLC25A13 coexpressed genes showed that these genes are enriched in ATPase activity, cell cycle, mTOR, and VEGFA-VEGFR2 signaling pathways, which were relevant to tumor development and angiogenesis. Gene set enrichment analysis (GSEA) demonstrated that the SLC25A13 expression was related to infiltrating immune cells in SKCM.

Conclusion

Our findings revealed that SLC25A13 might be a potential prognostic and therapeutic biomarker for SKCM.

Genomic Analysis Reveals the Prognostic and Immunotherapeutic Response Characteristics of Ferroptosis in Lung Squamous Cell Carcinoma

INTRODUCTION

Recent studies have reported that ferroptosis is an iron-dependent cell death process and is a potential therapeutic target in various tumours. The purpose of this study was to establish a new algorithm based on the ferroptosis score to ascertain the prognosis and response to immunotherapy of patients with lung squamous cell carcinoma (LUSC).

METHODS

The RNA-seq data of patients with LUSC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and merged after removing the inter batch differences. Based on the expression of the ferroptosis-related genes, unsupervised consistent cluster analysis was performed to obtain various ferroptosis-related subgroups. These subgroups were analysed to obtain differentially expressed genes (DEGs). Subsequently, multiple gene clusters were obtained by unsupervised consistent cluster analysis based on the expression of the DEGs. The Boruta algorithm was used to calculate the ferroptosis score.

RESULTS

There were significant differences in prognosis amongst the various ferroptosis-related and gene clusters. In addition, the gene set variation analysis revealed that the different ferroptosis-related clusters and gene clusters demonstrated differences in biological pathways. The ferroptosis scores positively correlated with the tumour mutation burden, and patients with lower scores had a better prognosis. In addition, the ferroptosis score was accurate in predicting the effectiveness of immunotherapy.

CONCLUSION

There were significant differences in the prognosis and immunotherapy response of patients with LUSC with different ferroptosis scores. Therefore, a comprehensive clinical evaluation of the ferroptosis score of each patient with LUSC is clinically significant.

Profilin 1 Induces Tumor Metastasis by Promoting Microvesicle Secretion Through the ROCK 1/p-MLC Pathway in Non-Small Cell Lung Cancer

Profilin 1 (PFN1), an actin-binding protein, plays contrasting roles in the metastasis of several cancers; however, its role in non-small cell lung cancer (NSCLC) metastasis remains unclear. Here, PFN1 expression was upregulated in metastatic NSCLC tissues. PFN1 overexpression significantly promotes NSCLC metastasis in vitro and in vivo. Proteomics analysis revealed PFN1 involvment in microvesicles (MVs) secretion. In vitro experiments confirmed that PFN1 overexpression increased secretion of MVs. MVs are important mediators of metastasis. Here, we show an increased abundance of MVs in the sera of patients with metastatic NSCLC compared to that in the sera of patients with non-metastatic NSCLC. Both in vitro and in vivo experiments revealed that PFN1 could increase MV secretion, and MVs derived from PFN1-overexpressing cells markedly promoted NSCLC metastasis. We then elucidated the mechanisms underlying PFN1-mediated regulation of MVs and found that PFN1 could interact with ROCK1 and enhance its kinase activity to promote myosin light chain (MLC) phosphorylation for MV secretion. Inhibition of ROCK1 decreased MV secretion and partially reversed the PFN1-induced promotion of NSCLC metastasis. Collectively, these findings show that PFN1 regulates MV secretion to promote NSCLC metastasis. PFN1 and MVs represent potential predictors or therapeutic targets for NSCLC metastasis.

An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma

Background

Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease.

Methods

We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort.

Results

The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set.

Conclusions

Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma’s OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01902-6.

Identification of Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

CHEK1: a hub gene related to poor prognosis for lung adenocarcinoma

Aim: The study aims to pinpoint hub genes and investigate their functions in order to gain insightful understandings of lung adenocarcinoma (LUAD). Methods: Bioinformatic approaches were adopted to investigate genes in databases including Gene Expression Omnibus, WebGestalt, STRING and Cytoscape, GEPIA2, Oncomine, Human Protein Atlas, TIMER2.0, UALCAN, cBioPortal, TargetScanHuman, OncomiR, ENCORI, Kaplan-Meier plotter, UCSC Xena, European Molecular Biology Laboratory - European Bioinformatics Institute Single Cell Expression Atlas and CancerSEA. Results: Five hub genes were ascertained. CHEK1 was overexpressed in a range of cancers, including LUAD. Promoter methylation, amplification and miRNA regulation might trigger CHEK1 upregulation, signaling poor prognosis. CHEK1 with its coexpressed genes were enriched in the cell cycle pathway. Intratumor heterogeneity of CHEK1 expression could be observed. Cell clusters with CHEK1 expression were more prone to metastasis and epithelial-to-mesenchymal transition. Conclusion: CHEK1 might potentially act as a prognostic biomarker for LUAD.

A Novel Prognostic and Predictive Signature for Lung Adenocarcinoma Derived from Combined Hypoxia and Infiltrating Immune Cell-Related Genes in TCGA Patients

Background

The hypoxia and immune status of the lung adenocarcinoma (LUAD) microenvironment appear to have combined impacts on prognosis. Therefore, deriving a prognostic signature by integrating hypoxia- and immune infiltrating cell-related genes (H&IICRGs) may add value over prognostic indices derived from genes driving either process alone.

Methods

Differentially expressed H&IICRGs (DE-H&IICRGs) were identified in The Cancer Genome Atlas transcriptomic data using limma, CIBERSORT, weighted gene co-expression network analysis, and intersection analysis. A stepwise Cox regression model was constructed to identify prognostic genes and to produce a gene signature based on DE-H&IICRGs. The potential biological functions associated with the gene signature were explored using functional enrichment analysis. The prognostic signature was externally validated in a separate cohort from the Gene Expression Omnibus database.

Results

Five prognostic genes associated with overall survival in LUAD were used in the DE-H&IICRG-based prognostic signature. Patients in the high-risk group had an inferior prognosis, which was validated in an independent external cohort, and had lower expression of most immune checkpoint genes. In multivariate analysis, only risk score and T stage were independent prognostic factors. Differentially expressed genes (DEGs) associated with the risk score were enriched for pathways related to cell cycle, hypoxia regulation, and immune response. TIDE analyses showed that low-risk LUAD patients might also respond better to immunotherapy.

Conclusion

This study establishes and validates a prognostic profile for LUAD patients that combines hypoxia and immune infiltrating cell-related genes. This signature may have clinical application both for prognostication and guiding individualized immunotherapy.

Exploring the Prognostic Value, Immune Implication and Biological Function of H2AFY Gene in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is an extremely malignant cancer with poor survival. H2AFY gene encodes for a variant of H2A histone, and it has been found to be dysregulated in various tumors. However, the clinical value, biological functions and correlations with immune infiltration of H2AFY in HCC remain unclear.

Methods

We analyzed the expression and clinical significance of H2AFY in HCC using multiple databases, including Oncomine, HCCDB, TCGA, ICGC, and so on. The genetic alterations of H2AFY were analyzed by cBioPortal and COSMIC databases. Co-expression networks of H2AFY and its regulators were investigated by LinkedOmics. The correlations between H2AFY and tumor immune infiltration were explored using TIMER, TISIDB databases, and CIBERSORT method. Finally, H2AFY was knocked down with shRNA lentiviruses in HCC cell lines for functional assays in vitro.

Results

H2AFY expression was upregulated in the HCC tissues and cells. Kaplan-Meier and Cox regression analyses revealed that high H2AFY expression was an independent prognostic factor for poor survival in HCC patients. Functional network analysis indicated that H2AFY and its co-expressed genes regulates cell cycle, mitosis, spliceosome and chromatin assembly through pathways involving many cancer-related kinases and E2F family. Furthermore, we observed significant correlations between H2AFY expression and immune infiltration in HCC. H2AFY knockdown suppressed the cell proliferation and migration, promoted cycle arrest, and apoptosis of HCC cells in vitro.

Conclusion

Our study revealed that H2AFY is a potential biomarker for unfavorable prognosis and correlates with immune infiltration in HCC.

Comprehensive Analysis of Hexokinase 2 Immune Infiltrates and m6A Related Genes in Human Esophageal Carcinoma

Background: Hexokinase 2 not only plays a role in physiological function of human normal tissues and organs, but also plays a vital role in the process of glycolysis of tumor cells. However, there are few comprehensive studies on HK2 in esophageal carcinoma (ESCA) needs further study.

Methods: Oncomine, Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used to analyze the expression differences of HK2 in Pan-cancer and ESCA cohort, and to analyze the correlation between HK2 expression level and clinicopathological features of TCGA ESCA samples. GO/KEGG, GGI, and PPI analysis of HK2 was performed using R software, LinkedOmics, GeneMANIA and STRING online tools. The correlation between HK2 and ESCA immune infiltration was analyzed TIMER and TCGA ESCA cohort. The correlation between HK2 expression level and m6A modification of ESCA was analyzed by utilizing TCGA ESCA cohort.

Results: HK2 is highly expressed in a variety of tumors, and its high expression level in ESCA is closely related to the weight, cancer stages, tumor histology and tumor grade of ESCA. The analysis results of GO/KEGG showed that HK2 was closely related to cell adhesion molecule binding, cell-cell junction, ameboidal-type cell migration, insulin signaling pathway, hif-1 signaling pathway, and insulin resistance. GGI showed that HK2 associated genes were mainly involved in the glycolytic pathway. PPI showed that HK2 was closely related to HK1, GPI, and HK3, all of which played an important role in tumor proliferation. The analysis results of TIMER and TCGA ESCA cohort indicated that the HK2 expression level was related to the infiltration of various immune cells. TCGA ESCA cohort analyze indicated that the HK2 expression level was correlated with m6A modification genes.

Conclusion: HK2 is associated with tumor immune infiltration and m6A modification of ESCA, and can be used as a potential biological target for diagnosis and therapy of ESCA.

Systematic Analyses of a Chemokine Family-Based Risk Model Predicting Clinical Outcome and Immunotherapy Response in Lung Adenocarcinoma

Chemokines exhibited complicated functions in antitumor immunity, with their expression profile and clinical importance of lung adenocarcinoma (LUAD) patients remaining largely undetermined. This study aimed to explore the expression patterns of chemokine family in LUAD and construct a predictive chemokine family-based signature. A total of 497 samples were downloaded from the Cancer Genome Atlas (TCGA) data portal as the training set, and the combination of 4 representative Gene Expression Omnibus (GEO) datasets, including GSE30219, GSE50081, GSE37745, and GSE31210, were utilized as the validation set. A three gene-based signature was constructed using univariate and stepwise multivariate Cox regression analysis, classifying patients into high and low risk groups according to the overall survival. The independent GEO datasets were utilized to validate this signature. Another multivariate analysis revealed that this signature remained an independent prognostic factor in LUAD patients. Furthermore, patients in the low risk group featured immunoactive tumor microenvironment (TME), higher IPS scores and lower TIDE scores, and was regarded as the potential beneficiaries of immunotherapy. Finally, the role of risky CCL20 was validated by immunohistochemistry (IHC), and patients possessed higher CCL20 expression presented shorter overall survival (P = 0.011).