Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma

The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. This study aims to investigate the relationship between gene expression and mitophagy in LUAD, with an emphasis on identifying key biomarkers and elucidating their roles in tumorigenesis and immune cell infiltration.

Methods

We utilized datasets GSE151101 and GSE203609 from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with lung cancer and mitophagy. DEGs were identified using GEO2R, filtered based on criteria of P < 0.05 and log2 fold change ≥ 1. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to classify DEGs into modules. Functional annotation of these modules was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Gene Set Enrichment Analysis (GSEA) was applied to the most relevant module, designated as the greenyellow module. To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. Validation of the findings was conducted using The Cancer Genome Atlas (TCGA) database, Human Protein Atlas (HPA), quantitative PCR (qPCR), and immune cell infiltration analysis via CIBERSORTx.

Results

Our analysis identified 11,012 overlapping DEGs between the two datasets. WGCNA revealed 11 modules, with the green-yellow module exhibiting the highest correlation. Functional enrichment analysis highlighted significant associations with FOXM1 signaling pathways and retinoblastoma in cancer. Machine learning algorithms identified COASY, FTSJ1, and MOGS as pivotal genes. These findings were validated using TCGA data, qPCR experiments, which demonstrated high expression levels in LUAD samples. Immunohistochemistry from HPA confirmed consistency between protein levels and RNA-seq data. Furthermore, pan-cancer analysis indicated that these genes are highly expressed across various cancer types. Immune infiltration analysis suggested significant correlations between these genes and specific immune cell populations.

Conclusion

COASY, FTSJ1 and MOGS have emerged as critical biomarkers in LUAD, potentially influencing tumorigenesis through mitophagy-related mechanisms and immune modulation. These findings provide promising avenues for future research into targeted therapies and diagnostic tools, thereby enhancing LUAD management.

A disulfidptosis-related glucose metabolism and immune response prognostic model revealing the immune microenvironment in lung adenocarcinoma

Background

Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis.

Methods

Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS.

Results

We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05).

Conclusion

The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.

Identification of subgroups and development of prognostic risk models along the glycolysis-cholesterol synthesis axis in lung adenocarcinoma

Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.

EVI2B may be a novel prognostic marker for lung adenocarcinoma

Objective: This study intended to unravel the relationship of EVI2B expression with lung adenocarcinoma (LUAD). Methods: TIMER1.0, Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases, as well as the University of Alabama at Birmingham Cancer website, were used to analyze the expression of EVI2B and its relationship with clinical features. The relationship between survival curve analysis and prognosis was analyzed. The role of EVI2B in LUAD was verified by wet experiments. Results: EVI2B was markedly downregulated in LUAD. There was a relationship between the expression of EVI2B and clinical features. Low EVI2B level was substantially implicated in low survival in LUAD. EVI2B overexpression constrained LUAD cell viability, migration and invasion. Conclusion: EVI2B was related to prognosis and immune microenvironment in LUAD, suggesting that EVI2B may be a novel prognostic marker for LUAD.

Identification of Basement Membrane-Related Signatures in Gastric Cancer

BACKGROUND

The basement membrane (BM) serves as a major barrier to impede tumor cell invasion and extravasation during metastasis. However, the associations between BM-related genes and GC remain unclear.

METHODS

RNA expression data and corresponding clinical information of STAD samples were downloaded from the TCGA database. We identified BM-related subtypes and constructed a BM-related gene prognostic model using lasso-Cox regression analysis. We also investigated the single-cell properties of prognostic-related genes and the TME characteristic, TMB status, and chemotherapy response in high- and low-risk groups. Finally, we verified our results in the GEPIA database and human tissue specimens.

RESULTS

A six-gene lasso Cox regression model (APOD, CAPN6, GPC3, PDK4, SLC7A2, SVEP1) was developed. Activated CD4+ T cells and follicular T cells were shown to infiltrate more widely in the low-risk group. The low-risk group harbored significantly higher TMB and better prognosis, favoring immunotherapy.

CONCLUSIONS

We constructed a six-gene BM-related prognostic model for predicting GC prognosis, immune cell infiltration, TMB status, and chemotherapy response. This research provides new ideas for developing more effective individualized treatment of GC patients.

Comprehensively Analyze the Prognosis Significance and Immune Implication of PTPRO in Lung Adenocarcinoma

Immunotherapy for lung adenocarcinoma (LUAD) is considered to be a promising treatment option, but only a minority of patients benefit from it. Therefore, it is essential to clarify the regulation mechanism of the tumor immune microenvironment (TIM) of the LUAD. Receptor-type protein tyrosine phosphatase (PTPRO) has been shown to be a tumor suppressor in a variety of tumor; however, its role in LUAD has never been reported. In this study, we first found that PTPRO was lowly expressed in LUAD and positively correlated with patient prognosis. Next, we investigated the relationship between PTPRO and clinical characteristics, and the results showed that gender, age, T, and stage were closely related to the expression level of PTPRO. Moreover, we performed univariate and multivariate analyses, and the results revealed that PTPRO was a protective factor for LUAD. By constructing a nomogram based on the expression level of PTPRO and various clinical characteristics, it was proved that the nomogram has a good predictive capacity. Furthermore, we analyzed the coexpression network of PTPRO through multiple databases and performed GO and KEGG enrichment analyses. The results demonstrated that PTPRO was involved in the regulation of multiple immune pathways. In addition, we analyzed whether PTPRO expression of LUAD regulate immune cell infiltration and the results demonstrated that PTPRO was closely related to the infiltration of various immune cells. Finally, we predicted LUAD sensitivity to chemotherapeutics and response to immunotherapy by PTPRO expression levels. The results showed that PTPRO expression level affect the sensitivity of various chemotherapeutic drugs and may be involved in the efficacy of immunotherapy. These results we obtained suggested that PTPRO is closely related to the prognosis and TIM of LUAD, which may be a potential immunotherapeutic target for LUAD.

Construction and Validation of a Novel Immune Checkpoint-Related Model in Clear Cell Renal Cell Carcinoma

Background

With the highest mortality and metastasis rate, kidney renal clear cell carcinoma (KIRC) is one of the most common urological malignant tumors and not sensitive to chemotherapy and radiotherapy. Immunotherapy, which proves to be effective and a big progression, such as PD-1/PD-L1 inhibitors, is not sensitive to all KIRC patients. To predict prognosis and immunotherapy response, a novel immune checkpoint gene- (ICG-) related model is essential in clinics.

Methods

From the public database-downloaded dataset, a novel ICG-related model for predicting prognosis and immunotherapy response in KIRC patients was built up and verified with R packages and Cox regression analysis. The Kaplan-Meier curve was plotted.

Results

39 ICGs were identified to have different expression in KIRC patients and enriched in immune-related biological pathways and activities. Three ICGs (CTLA4, TNFSF14, and HHLA2) were screened to generate KIRC-ICG model. The KIRC-ICG model was verified to be effective. With conducting KIRC-SYS model, KIRC-ICGscore was verified to be an independent factor regardless of age, gender, stage, grade, and TNM stage. Compared to the ICG-low subgroup, the ICG-high subgroup had more immune activities. KIRC-ICGscore was significantly positively correlated with the expression of Treg markers. KIRC-ICG model could also be reliable to predict immunotherapy response.

Conclusion

The KIRC-ICG model was reliable to predict prognosis and immunotherapy response for KIRC patients and could be an independent factor regardless of clinical characteristics.

Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.

Heparin and Heparin-Based Drug Delivery Systems: Pleiotropic Molecular Effects at Multiple Drug Resistance of Osteosarcoma and Immune Cells

One of the main problems of modern health care is the growing number of oncological diseases both in the elderly and young population. Inadequately effective chemotherapy, which remains the main method of cancer control, is largely associated with the emergence of multidrug resistance in tumor cells. The search for new solutions to overcome the resistance of malignant cells to pharmacological agents is being actively pursued. Another serious problem is immunosuppression caused both by the tumor cells themselves and by antitumor drugs. Of great interest in this context is heparin, a biomolecule belonging to the class of glycosaminoglycans and possessing a broad spectrum of biological activity, including immunomodulatory and antitumor properties. In the context of the rapid development of the new field of “osteoimmunology,” which focuses on the collaboration of bone and immune cells, heparin and delivery systems based on it may be of intriguing importance for the oncotherapy of malignant bone tumors. Osteosarcoma is a rare but highly aggressive, chemoresistant malignant tumor that affects young adults and is characterized by constant recurrence and metastasis. This review describes the direct and immune-mediated regulatory effects of heparin and drug delivery systems based on it on the molecular mechanisms of (multiple) drug resistance in (onco) pathological conditions of bone tissue, especially osteosarcoma.

Expression Analysis of Ligand-Receptor Pairs Identifies Cell-to-Cell Crosstalk between Macrophages and Tumor Cells in Lung Adenocarcinoma

Background

Lung adenocarcinoma is one of the most commonly diagnosed malignancies worldwide. Macrophage plays crucial roles in the tumor microenvironment, but its autocrine network and communications with tumor cell are still unclear.

Methods

We acquired single-cell RNA sequencing (scRNA-seq) (n = 30) and bulk RNA sequencing (n = 1480) samples of lung adenocarcinoma patients from previous literatures and publicly available databases. Various cell subtypes were identified, including macrophages. Differentially expressed ligand-receptor gene pairs were obtained to explore cell-to-cell communications between macrophages and tumor cells. Furthermore, a machine-learning predictive model based on ligand-receptor interactions was built and validated.

Results

A total of 159,219 single cells (18,248 tumor cells and 29,520 macrophages) were selected in this study. We identified significantly correlated autocrine ligand-receptor gene pairs in tumor cells and macrophages, respectively. Furthermore, we explored the cell-to-cell communications between macrophages and tumor cells and detected significantly correlated ligand-receptor signaling pairs. We determined that some of the hub gene pairs were associated with patient prognosis and constructed a machine-learning model based on the intercellular interaction network.

Conclusion

We revealed significant cell-to-cell communications (both autocrine and paracrine network) within macrophages and tumor cells in lung adenocarcinoma. Hub genes with prognostic significance in the network were also identified.

Altered expression of ACOX2 in non-small cell lung cancer

Peroxisomes are organelles that play essential roles in many metabolic processes, but also play roles in innate immunity, signal transduction, aging and cancer. One of the main functions of peroxisomes is the processing of very-long chain fatty acids into metabolites that can be directed to the mitochondria. One key family of enzymes in this process are the peroxisomal acyl-CoA oxidases (ACOX1, ACOX2 and ACOX3), the expression of which has been shown to be dysregulated in some cancers. Very little is however known about the expression of this family of oxidases in non-small cell lung cancer (NSCLC). ACOX2 has however been suggested to be elevated at the mRNA level in over 10% of NSCLC, and in the present study using both standard and bioinformatics approaches we show that expression of ACOX2 is significantly altered in NSCLC. ACOX2 mRNA expression is linked to a number of mutated genes, and associations between ACOX2 expression and tumour mutational burden and immune cell infiltration were explored. Links between ACOX2 expression and candidate therapies for oncogenic driver mutations such as KRAS were also identified. Furthermore, levels of acyl-CoA oxidases and other associated peroxisomal genes were explored to identify further links between the peroxisomal pathway and NSCLC. The results of this biomarker driven study suggest that ACOX2 may have potential clinical utility in the diagnosis, prognosis and stratification of patients into various therapeutically targetable options.

Comprehensive Analysis of Gene Signatures of m6ARNA Methylation Regulators in Lung Adenocarcinoma and Development of a Risk Scoring System

The recent application of targeted immunotherapy has greatly improved the clinical outcomes of patients with lung adenocarcinoma (LUAD), but drug resistance continues to emerge, and to evaluate and to improve patient prognosis are arduous. The diagnostic and prognostic value of N6-methyladenosine (M6A) in LUAD has attracted increasing attention. We systematically studied correlations among important M6A methylation regulators, tumor mutational burden (TMB), and immune infiltration in clinical and sequencing data from the LUAD cohort of the cancer genome map (TCGA). The molecular subtype clusters 1 and 2 were identified by the consensus clustering of 16 M6A regulatory factors. Clinical prognosis, M6A regulatory factor expression, TMB, pathway enrichment, and immune cell infiltration significantly differed between clusters 1 and 2. Compared with other clinical traits, a prognostic risk score system constructed using the M6A regulatory factors HNRNPA2B1 and HNRNPC can serve as an independent prognostic method for LUAD, with higher predictive sensitivity and specificity. Risk scores were significantly higher for cluster 2 than 1, which was consistent with the trend towards a better prognosis in cluster 1. Overall, our findings revealed an important role of M6A methylation regulators in LUAD, and our risk scoring system involving these regulators might help to screen groups at high risk for LUAD and provide important theoretical bioinformatic support for evaluating the prognosis of such patients.

Comprehensive analysis of the prognostic and role in immune cell infiltration of MSR1 expression in lower-grade gliomas

BACKGROUND

The therapeutic effects of conventional treatment on gliomas are not promising. The tumor microenvironment (TME) has a close association with the invasiveness of multiple types of tumors, including low-grade gliomas (LGG). This study aims to validate the prognostic and immune-related role of macrophage scavenger receptor 1 (MSR1) in LGG patients.

METHODS

Data in this study were obtained from public databases. The differential expression of MSR1 was analyzed in LGG patients with different clinicopathological characteristics. Kaplan-Meier survival analysis, a time-dependent receiver operating characteristic (ROC) curve, and Cox regression analysis were used to assess the prognostic value of MSR1. Differentially expressed genes (DEGs) were screened between the high and low expression groups of MSR1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to annotate the function of these DEGs. Hallmark gene sets were identified based on MSR1 by Gene Set Enrichment Analysis (GSEA). Difference analysis and correlation analysis were used to study the relationship between MSR1 and TME-related scores, tumor-infiltrating immune cells (TIICs), immune-related gene sets, and immune checkpoints (ICPs). The single-cell sequencing data were processed to identify the cell types expressing MSR1. The quantification of TIICs in TME was calculated by single-sample gene set enrichment analysis (ssGSEA). The differential expression of MSR1 in LGG and control brain tissues was verified by experiments.

RESULTS

There were significant differences in the expression level of MSR1 in different types of tissues and cells. MSR1 has a high prognostic value in LGG patients and can be used as an independent prognostic factor. MSR1 is closely related to TME and may play an important role in the immunotherapy of LGG patients.

CONCLUSIONS

The result of our study demonstrated that MSR1 is an independent prognostic biomarker in LGG patients and may play an important role in the TME of LGGs.

Characteristics of immunophenotypes and immunological in tumor microenvironment and analysis of immune implication of CXCR4 in gastric cancer

The formation of gastric cancer (GC) is a complicated process involving multiple factors and multiple steps. The tumor–immune microenvironment is essential for the growth of GC and affects the prognosis of patients. We performed multiple machine learning algorithms to identify immunophenotypes and immunological characteristics in GC patients’ information from the TCGA database and extracted immune genes relevance of the GC immune microenvironment. C-X-C motif chemokine receptor 4 (CXCR4), belongs to the C-X-C chemokine receptor family, which can promote the invasion and migration of tumor cells. CXCR4 expression is significantly correlated to metastasis and the worse prognosis. In this work, we assessed the condition of immune cells and identified the connection between CXCR4 and GC immune microenvironment, as well as the signaling pathways that mediate the immune responses involved in CXCR4. The work showed the risk scores generated by CXCR4-related immunomodulators could distinguish risk groups consisting of differential expression genes and could use for the personalized prognosis prediction. The findings suggested that CXCR4 is involved in tumor immunity of GC, and CXCR4 is considered as a potential prognostic biomarker and immunotherapy target of GC. The prognostic immune markers from CXCR4-associated immunomodulators can independently predict the overall survival of GC.

Systemic Analyses of the Expression of TPI1 and Its Associations with Tumor Microenvironment in Lung Adenocarcinoma and Squamous Cell Carcinoma

Background. Recent studies have shown that the expression level of triosephosphate isomerase 1 (TPI1) may be associated with the occurrence and metastasis of tumors, but the expression level of TPI1 and its effect on lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are not yet clear. Methods. We comprehensively explored and validated the TPI1 expression in lung adenocarcinoma and lung squamous cell carcinoma in public datasets. The associations of TPI1 expression with clinicopathological characteristics and prognosis were also studied in both histological types. Moreover, we analyzed the potential relations of TPI1 with immunomodulators and immune cell infiltrations in the tumor microenvironment based on previous literatures and bioinformatic tools. Results. We found that TPI1 was significantly overexpressed in LUAD and LUSC. Significant associations of TPI1 expression were observed regarding age, gender, and pathological stages in LUAD. However, similar trend was only found with respect to age in LUSC. The high expression of TPI1 was significantly associated with worse survival in LUAD, but not in LUSC. Furthermore, we explored the potential distribution and changes of TPI1 expression in tumor microenvironment. Pathway enrichment analyses were performed to identify possible roles of TPI1 in both lung cancers. Conclusions. TPI1 was overexpressed in both LUAD and LUSC. Increased TPI1 expression was correlated with poor prognosis in LUAD and changed immune cell infiltrating in various degrees in both histological types. Our study provides insights in understanding the potential roles of TPI1 in tumor progression and immune microenvironment.

A scoping review of artificial intelligence applications in thoracic surgery

OBJECTIVES

Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions.

METHODS

A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric.

RESULTS

ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility.

CONCLUSIONS

There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.

Impacts of Cigarette Smoking on the Tumor Immune Microenvironment in Esophageal Squamous Cell Carcinoma

Objective: Cigarette smoking is a carcinogenic factor for esophageal cancer and evidence also indicates its effects on tumor microenvironment in patients with esophageal squamous cell carcinoma (ESCC). Materials and Methods: In our study, we demonstrated nine immune infiltrating cells and markers in non-smokers and smokers of 189 non-drinking ESCC patients with multiplex fluorescent immunohistochemistry (mflHC) staining and multispectral imaging. The impacts of cigarette smoking on tumor microenvironment and patient prognosis were also analyzed. Results: Among 189 ESCC patients of non-drinker, 86 patients was current smokers, while 34 males and 59 females were non-smokers and 10 former-smokers. Among 34 male non-smokers and 83 smokers, distinct immune infiltrating cells, with increased DCs in stromal regions (P=0.033), elevated infiltration of Treg cells in intraepithelial regions (P=0.010) and reduced activate cytotoxic T lymphocytes (aCTLs) in both intraepithelial (P=0.021) and stromal regions (P=0.017), were observed in tumor specimens of smoking males, implying an immune suppressed response during cigarette smoke exposure. For smoking characters, the level of stromal tumor-associated macrophages (TAMs) infiltration was correlated with smoking year after age adjusted (r_s =0.352, P=0.002). Though cigarette smoking did not alter the expression of programmed death ligand 1 (PD-L1) in epithelial cells or TAMs in tumor specimens, higher expression of PD-L1 predicted a worse survival in non-smokers but not smokers. Conclusions: Our findings indicated smoking may impair T cell-mediated immune response and supported the possible impacts of cigarette smoking in PD-L1 related research and therapy of ESCC.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas

AIM

This study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms.

METHODS

The data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358).

RESULTS

A total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells.

CONCLUSION

The proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.

TYK2 is a prognostic biomarker and associated with immune infiltration in the lung adenocarcinoma microenvironment

BACKGROUND

Lung adenocarcinoma (LUAD) remains a major disease with high morbidity and mortality. The Janus kinases (JAKs) play a significant part in cellular biological process, inflammation and immunity. The role of JAK family in LUAD is still ambiguous.

METHODS

Various bioinformatics web portals were applied to explore the prognostic value of JAK family and their correlation with immune infiltration in LUAD.

RESULTS

JAK1/2 was downregulated, whereas JAK3/TYK2 was upregulated in patients with LUAD compared with the healthy controls in subgroup analyses based on gender, age, smoking habits, cancer stage, TP53 mutation status, and nodal metastasis status. Drug sensitivity indicated that low expression of JAK3 and TYK2 were resistant to most of the small molecules or drugs. High TYK2 expression was associated with favorable overall survival and relapse free survival in LUAD. Moreover, univariate and multivariate analysis revealed that clinical stage, lymphatic node metastasis and TYK2 expression were the independent factors affecting the prognosis of LUAD patients. TYK2 expression in LUAD patients was positively associated with the abundance of immune cells (B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells) and immune biomarker sets. Moreover, TYK2 was mainly involved in RNA binding, transcriptional mis-regulation in cancer and cell cycles. We also identified several TYK2-associated miRNA or transcription factor targets in LUAD.

CONCLUSION

Our results indicated that TYK2 was a biomarker and associated with prognosis and immune infiltration in LUAD, laying a foundation for further study about the role of TYK2 in the carcinogenesis and progression of LUAD.

Identification of an Immune-Related Signature for Predicting Prognosis in Patients With Pancreatic Ductal Adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.

METHODS

We developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.

RESULTS

Owing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.

CONCLUSIONS

This study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.

Expression Profile Analysis Identifies a Novel Seven Immune-Related Gene Signature to Improve Prognosis Prediction of Glioblastoma

Glioblastoma multiform (GBM) is a malignant central nervous system cancer with dismal prognosis despite conventional therapies. Scientists have great interest in using immunotherapy for treating GBM because it has shown remarkable potential in many solid tumors, including melanoma, non-small cell lung cancer, and renal cell carcinoma. The gene expression patterns, clinical data of GBM individuals from the Cancer Genome Atlas database (TCGA), and immune-related genes (IRGs) from ImmPort were used to identify differentially expressed IRGs through the Wilcoxon rank-sum test. The association between each IRG and overall survival (OS) of patients was investigated by the univariate Cox regression analysis. LASSO Cox regression assessment was conducted to explore the prognostic potential of the IRGs of GBM and construct a risk score formula. A Kaplan–Meier curve was created to estimate the prognostic role of IRGs. The efficiency of the model was examined according to the area under the receiver operating characteristic (ROC) curve. The TCGA internal dataset and two GEO external datasets were used for model verification. We evaluated IRG expression in GBM and generated a risk model to estimate the prognosis of GBM individuals with seven optimal prognostic expressed IRGs. A landscape of 22 types of tumor-infiltrating immune cells (TIICs) in glioblastoma was identified, and we investigated the link between the seven IRGs and the immune checkpoints. Furthermore, there was a correlation between the IRGs and the infiltration level in GBM. Our data suggested that the seven IRGs identified in this study are not only significant prognostic predictors in GBM patients but can also be utilized to investigate the developmental mechanisms of GBM and in the design of personalized treatments for them.

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

Background

The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD.

Methods

With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset.

Results

The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival.

Conclusion

We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

FAM207BP, a pseudogene-derived lncRNA, facilitates proliferation, migration and invasion of lung adenocarcinoma cells and acts as an immune-related prognostic factor

AIMS

This study aimed to characterize the functions of pseudogene-derived long non-coding RNA (lncRNA) FAM207BP in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

Through the Cancer Genome Atlas (TCGA)-Genotype Tissue Expression (GTEx) database, FAM207BP expression was detected in LUAD and normal tissues. Overall survival (OS) and disease-free survival (DFS) analysis was presented using log-rank test or univariate Cox regression analysis. The relationships between FAM207BP expression and clinical features were analyzed. FAM207BP expression was validated in LUAD tissues and cells using RT-qPCR. Cell viability of LUAD cells was evaluated after silencing or overexpressing FAM207BP. Furthermore, migrated and invasive abilities were examined by Transwell and scratch assays. The correlation between FAM207BP expression and the immune infiltration levels was analyzed. Gene Set Enrichment Analysis (GSEA) was performed for high- and low-expression of FAM207BP using C2 collection in the Molecular Signatures Database (MSigDB) database.

KEY FINDINGS

FAM207BP expression was distinctly higher in LUAD than normal tissues. Patients with its high expression indicated worse OS and DFS time. FAM207BP expression was significantly related to gender. RT-qPCR results confirmed that FAM207BP was significantly highly expressed in LUAD tissues and cells. Knockdown of FAM207BP distinctly suppressed cellular viability, migration and invasion for LUAD cells. Also, its expression was negatively related to B cell infiltration levels. GSEA results indicated that high FAM207BP expression was involved in regulation of gene expression. Its low expression was related to immune response.

SIGNIFICANCE

Pseudogene-derived lncRNA FAM207BP could induce proliferation and migration of LUAD cells, which could act as an immune-related prognostic factor.

Clinical correlates for immune checkpoint therapy: significance for CNS malignancies

Immune checkpoint inhibitors (ICIs) have revolutionized the field of cancer immunotherapy. Most commonly, inhibitors of PD-1 and CTLA4 are used having received approval for the treatment of many cancers like melanoma, non-small-cell lung carcinoma, and leukemia. In contrast, to date, clinical studies conducted in patients with CNS malignancies have not demonstrated promising results. However, patients with CNS malignancies have several underlying factors such as treatment with supportive medications like corticosteroids and cancer therapies including radiation and chemotherapy that may negatively impact response to ICIs. Although many clinical trials have been conducted with ICIs, measures that reproducibly and reliably indicate that treatment has evoked an effective immune response have not been fully developed. In this article, we will review the history of ICI therapy and the correlative biology that has been performed in the clinical trials testing these therapies in different cancers. It is our aim to help provide an overview of the assays that may be used to gauge immunologic response. This may be particularly germane for CNS tumors, where there is currently a great need for predictive biomarkers that will allow for the selection of patients with the highest likelihood of responding.

In silico immune infiltration profiling combined with functional enrichment analysis reveals a potential role for naïve B cells as a trigger for severe immune responses in the lungs of COVID-19 patients

COVID-19, caused by SARS-CoV-2, has rapidly spread to more than 160 countries worldwide since 2020. Despite tremendous efforts and resources spent worldwide trying to explore antiviral drugs, there is still no effective clinical treatment for COVID-19 to date. Approximately 15% of COVID-19 cases progress to pneumonia, and patients with severe pneumonia may die from acute respiratory distress syndrome (ARDS). It is believed that pulmonary fibrosis from SARS-CoV-2 infection further leads to ARDS, often resulting in irreversible impairment of lung function. If the mechanisms by which SARS-CoV-2 infection primarily causes an immune response or immune cell infiltration can be identified, it may be possible to mitigate excessive immune responses by modulating the infiltration and activation of specific targets, thereby reducing or preventing severe lung damage. However, the extent to which immune cell subsets are significantly altered in the lung tissues of COVID-19 patients remains to be elucidated.

This study applied the CIBERSORT-X method to comprehensively evaluate the transcriptional estimated immune infiltration landscape in the lung tissues of COVID-19 patients and further compare it with the lung tissues of patients with idiopathic pulmonary fibrosis (IPF). We found a variety of immune cell subtypes in the COVID-19 group, especially naïve B cells were highly infiltrated. Comparison of functional transcriptomic analyses revealed that non-differentiated naïve B cells may be the main cause of the over-active humoral immune response. Using several publicly available single-cell RNA sequencing data to validate the genetic differences in B-cell populations, it was found that the B-cells collected from COVID-19 patients were inclined towards naïve B-cells, whereas those collected from IPF patients were inclined towards memory B-cells. Further differentiation of B cells between COVID-19 mild and severe patients showed that B cells from severe patients tended to be antibody-secreting cells, and gene expression showed that B cells from severe patients were similar to DN2 B cells that trigger extrafollicular response. Moreover, a higher percentage of B-cell infiltration seems associated with poorer clinical outcome. Finally, a comparison of several specific COVID-19 cases treated with targeted B-cell therapy suggests that appropriate suppression of naïve B cells might potentially be a novel strategy to alleviate the severe symptoms of COVID-19.

A Recurrence-Specific Gene-Based Prognosis Prediction Model for Lung Adenocarcinoma through Machine Learning Algorithm

Background

After curative surgical resection, about 30-75% lung adenocarcinoma (LUAD) patients suffer from recurrence with dismal survival outcomes. Identification of patients with high risk of recurrence to impose intense therapy is urgently needed.

Materials and Methods

Gene expression data of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were calculated by comparing the recurrent and primary tissues. Prognostic genes associated with the recurrence-free survival (RFS) of LUAD patients were identified using univariate analysis. LASSO Cox regression and multivariate Cox analysis were applied to extract key genes and establish the prediction model.

Results

We detected 37 DEGs between primary and recurrent LUAD tumors. Using univariate analysis, 31 DEGs were found to be significantly associated with RFS. We established the RFS prediction model including thirteen genes using the LASSO Cox regression. In the training cohort, we classified patients into high- and low-risk groups and found that patients in the high-risk group suffered from worse RFS compared to those in the low-risk group (P < 0.01). Concordant results were confirmed in the internal and external validation cohort. The efficiency of the prediction model was also confirmed under different clinical subgroups. The high-risk group was significantly identified as the risk factor of recurrence in LUAD by the multivariate Cox analysis (HR = 13.37, P = 0.01). Compared to clinicopathological features, our prediction model possessed higher accuracy to identify patients with high risk of recurrence (AUC = 96.3%). Finally, we found that the G2M checkpoint pathway was enriched both in recurrent tumors and primary tumors of high-risk patients.

Conclusions

Our recurrence-specific gene-based prognostic prediction model provides extra information about the risk of recurrence in LUAD, which is conducive for clinicians to conduct individualized therapy in clinic.

Relapse-related molecular signature in early-stage lung adenocarcinomas based on base excision repair, stimulator of interferon genes pathway and tumor-infiltrating lymphocytes

Approximately 30% of patients with early-stage non-small cell lung cancer (NSCLC) relapse within 5 years after surgery. Therefore, it is necessary to identify a robust and reliable prognostic signature for early-stage NSCLC. Immunohistochemistry data from 147 patients with stage I lung adenocarcinoma (stage I-LUAD) were analyzed for the protein expression of base excision repair (BER), stimulator of interferon genes (STING) and tumor-infiltrating lymphocytes (TIL) to explore the relationship between protein expression and prognosis. A prediction model was further established by nomogram and externally verified using The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. XRCC1 and H2AX are negative prognostic markers for relapse-free survival (RFS), while CD8, CD20 and STING are positive prognostic markers for RFS. Nomograms for RFS share common prognostic markers, including XRCC1, H2AX, STING, CD8 and CD20. The c-index was 0.724 and 0.698 in the training cohort and the internal validation cohort, respectively. It was externally verified that the nomogram model had a good prediction for recurrence of stage I-LUAD. Correlation analysis showed that APE1 and H2AX were negatively correlated with STING, while STING was positively correlated with TIL. BER, the STING pathway and TIL were associated with early recurrence and were correlated with the tissue expression of stage I-LUAD. Our nomogram model was a good predictor for recurrence of stage I-LUAD.

Immune-Stromal Score Signature: Novel Prognostic Tool of the Tumor Microenvironment in Lung Adenocarcinoma

Background: Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. Methods: First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. Results: In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (CLEC17A, INHA, and XIRP1) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs CLEC17A, INHA, and XIRP1 were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. Conclusions: The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.

The Immune Landscape and Prognostic Immune Key Genes Potentially Involved in Modulating Synaptic Functions in Prostate Cancer

Background: Increasing evidence has indicated an association between differentially expressed genes (DEGs) in tumor-infiltrating immune cells (TIICs) and clinical outcome. The aim of this research is to investigate the influence of tumor microenvironment on the gene expression profile of TIICs and to identify their potential markers for modulating immune cell function in prostate cancer.

Methods: In our research, CIBERSORT algorithm was utilized to calculate the proportion of the TIICs in 164 tumor and 18 control samples from The Cancer Genome Atlas cohort. The differential expression analysis was conducted using R, and then the functional and the pathway enrichments of the DEGs were analyzed using Database for Annotation, Visualization, and Integrated Discovery, followed by integrated regulatory network analysis.

Results: As a result, nTreg, B cells, Th1, and DC cells were significantly increased, accompanied by largely decreased NK and NKT cells. The expressed immune-related gene correlation analysis showed that the signature gene expression extent of CD8 T cells was positively associated with CD4 memory activated T cells but negatively correlated with that of CD4 memory resting T cells. In addition, a total of 128 differentially expressed genes were identified. CytoHubba analysis obtained six hub genes, of which three prognostic-associated potential key molecules including CAV1, FLNA, and VCL were mainly involved in biological processes associated with the regulation of organic substance and synaptic connections.

Conclusions: This study provides a comprehensive understanding of the landscape of TIICs and the roles of the hub genes which may be valuable markers in prostate cancer diagnosis and immunotherapy.

Comprehensive Analysis of the Immune Implication of ACK1 Gene in Non-small Cell Lung Cancer

Activated Cdc42-associated kinase1 (ACK1), a non-receptor tyrosine kinase, has been considered as an oncogene and therapeutic target in various cancers. However, its contribution to cancer immunity remains uncertain. Here we first compared the profiles of immune cells in cancerous and normal tissues in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Next, we found that the immune cell infiltration levels were associated with the ACK1 gene copy numbers in lung cancer. Consistently, our RNA-seq data unveiled that the silencing of ACK1 upregulated several immune pathways in lung cancer cells, including the T cell receptor signaling pathway. The impacts of ACK1 on immune activity were validated by Gene Set Enrichment Analysis of RNA-seq data of 188 lung cancer cell lines from the public database. A pathway enrichment analysis of 35 ACK1-associated immunomodulators and 50 tightly correlated genes indicated the involvement of the PI3K-Akt and Ras signaling pathways. Based on ACK1-associated immunomodulators, we established multiple-gene risk prediction signatures using the Cox regression model. The resulting risk scores were an independent prognosis predictor in the TCGA lung cohorts. We also accessed the prognostic accuracy of the risk scores with a receiver operating characteristic methodology. Finally, a prognostic nomogram, accompanied by a calibration curve, was constructed to predict individuals' 3- and 5-year survival probabilities. Our findings provided evidence of ACK1's implication in tumor immunity, suggesting that ACK1 may be a potential immunotherapeutic target for non-small cell lung cancer (NSCLC). The nominated immune signature is a promising prognostic biomarker in NSCLC.

Immune cell infiltration features and related marker genes in lung cancer based on single-cell RNA-seq

PURPOSE

Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer.

METHODS

Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types.

RESULTS

Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues.

CONCLUSION

Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.

Identification of Immune Cell Landscape and Construction of a Novel Diagnostic Nomogram for Crohn's Disease

Crohn’s disease (CD) has an increasing incidence and prevalence worldwide. The etiology of CD remains unclear and there is no gold standard for diagnosis. The dysregulated immune response and different infiltration status of immune cells are critical for CD pathogenesis; therefore, it is important to provide an overview of immune-cell alterations in CD and explore a novel method for auxiliary diagnosis. Here we analyzed microarray datasets from Gene Expression Omnibus (GEO), and an extended version of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORTx) was utilized to estimate the fraction of 22 types of immune cells. Differentially expressed genes (DEGs) and a protein-protein interaction (PPI) network were identified, and we performed gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to identify differentially regulated pathways in CD. Least absolute shrinkage and selection operator (LASSO) regression was conducted to filter features, and a diagnostic nomogram based on logistic regression was built and validated in an independent validation cohort. In the derivation cohort, we found a proportion of 17 immune-cell types to be significantly altered between CD and healthy controls and a total of 150 DEGs were identified, which were mostly related to the immune response. Among the 15 hub genes based on the PPI network, C-X-C chemokine ligand 8 (CXCL8) and interleukin-1B (IL-1B) showed the highest degree of interaction. Additionally, GSEA and GSVA identified five significantly enriched pathways, among which the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway was critical in the CD development. Furthermore, six variables comprising of CXCL8, IL-1B, M1 macrophages, regulatory T cells, CD8⁺ T cells, and plasma cells were identified by LASSO regression and incorporated into a logistic regression model. The nomogram displayed a good prediction, with a 0.915 area under the receiver operating curve (AUC) and a C-index of 0.915 [95% confidence interval (CI): 0.875–0.955]. Similar results were found in the validation cohort, with an AUC of 0.884 and a 0.884 C-index (95% CI: 0.843–0.924). These results provide novel in silico insight into cellular and molecular characteristics of CD and potential biomarkers for diagnosis and targeted therapy.

Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis

PURPOSE

To comprehensively elucidate the landscape of the tumor environment (TME) of lung adenocarcinoma (LUAD), which has a profound impact on prognosis and response to immunotherapy.

METHODS AND MATERIALS

Using a large dataset of LUAD patients from The Cancer Genome Atlas, Gene Expression Omnibus database (GEO), and our institution (n = 1411), we estimated the infiltration pattern of 24 immune cell populations in each sample and systematically correlated the TME phenotypes with genomic traits and clinicopathologic characteristics.

RESULTS

The LUAD microenvironment was classified into two distinct TME clusters (A and B), and a random forest classifier model was constructed. TMEcluster A was characterized by sparse distribution of immune cell infiltration, relatively low levels of immunomodulators and slightly higher mutation load. By contrast, enrichment of both cytotoxic T cells and immunosuppressor cells was observed in TMEcluster B. Moreover, several immune-related cytokines or markers including IFN-γ, TNF-β, and several immune checkpoint molecules such as PD-L1 were also upregulated in TMEcluster B. Multivariable Cox analysis revealed that the TMEcluster was an independent prognostic factor (TMEcluster B vs. A, hazard ratio = 0.68, 95% confidence interval = 0.50-0.91, p = 0.010). These findings were all externally validated in the data from the GEO database and our institution.

CONCLUSIONS

Our findings describe a comprehensive landscape of LUAD immune infiltration pattern and integrate several previously proposed biomarkers associated with distinct immunophenotypes, thus shedding light on how tumors interact with immune microenvironment. Our results may guide a more precise immune therapeutic strategy for LUAD patients.

Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma

This work aimed to investigate tumor-infiltrating immune cells (TIICs) and immune-associated genes in the tumor microenvironment of osteosarcoma. An algorithm known as ESTIMATE was applied for immune score assessment, and osteosarcoma cases were assigned to the high and low immune score groups. Immune-associated genes between these groups were compared, and an optimal immune-related risk model was built by Cox regression analyses. The deconvolution algorithm (referred to as CIBERSORT) was applied to assess 22 TIICs for their amounts in the osteosarcoma microenvironment. Osteosarcoma cases with high immune score had significantly improved outcome (P<0.01). The proportions of naive B cells and M0 macrophages were significantly lower in high immune score tissues compared with the low immune score group (P<0.05), while the amounts of M1 macrophages, M2 macrophages, and resting dendritic cells were significantly higher (P<0.05). Important immune-associated genes were determined to generate a prognostic model by Cox regression analysis. Interestingly, cases with high risk score had poor outcome (P<0.01). The areas under the curve (AUC) for the risk model in predicting 1, 3 and 5-year survival were 0.634, 0.781, and 0.809, respectively. Gene set enrichment analysis suggested immunosuppression in high-risk osteosarcoma patients, in association with poor outcome.

A gene expression-based study on immune cell subtypes and glioma prognosis

Object

Glioma is a common malignant tumours in the central nervous system (CNS), that exhibits high morbidity, a low cure rate, and a high recurrence rate. Currently, immune cells are increasingly known to play roles in the suppression of tumourigenesis, progression and tumour growth in many tumours. Therefore, given this increasing evidence, we explored the levels of some immune cell genes for predicting the prognosis of patients with glioma.

Methods

We extracted glioma data from The Cancer Genome Atlas (TCGA). Using the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm, the relative proportions of 22 types of infiltrating immune cells were determined. In addition, the relationships between the scales of some immune cells and sex/age were also calculated by a series of analyses. A P-value was derived for the deconvolution of each sample, providing credibility for the data analysis (P < 0.05). All analyses were conducted using R version 3.5.2. Five-year overall survival (OS) also showed the effectiveness and prognostic value of each proportion of immune cells in glioma; a bar plot, correlation-based heatmap (corheatmap), and heatmap were used to represent the proportions of immune cells in each glioma sample.

Results

In total, 703 transcriptomes from a clinical dataset of glioma patients were drawn from the TCGA database. The relative proportions of 22 types of infiltrating immune cells are presented in a bar plot and heatmap. In addition, we identified the levels of immune cells related to prognosis in patients with glioma. Activated dendritic cells (DCs), eosinophils, activated mast cells, monocytes and activated natural killer (NK) cells were positively related to prognosis in the patients with glioma; however, resting NK cells, CD8⁺ T cells, T follicular helper cells, gamma delta T cells and M0 macrophages were negatively related to prognosis in the patients with glioma. Specifically, the proportions of several immune cells were significantly related to patient age and sex. Furthermore, the level of M0 macrophages was significant in regard to interactions with other immune cells, including monocytes and gamma delta T cells, in glioma tissues through sample data analysis.

Conclusion

We performed a novel gene expression-based study of the levels of immune cell subtypes and prognosis in glioma, which has potential clinical prognostic value for patients with glioma.