Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

Lack of imbalance between the master regulators TTF1/NKX2-1 and ΔNp63/p40 implies adverse prognosis in non-small cell lung cancer

The transcription factors TTF1/NKX2-1 and ΔNp63/p40 are the counterposed molecular markers associated with the main Non-Small Cell Lung Cancer subtypes: TTF1 for adenocarcinoma, p40 for squamous cell carcinoma. Although they generally display a mutually exclusive expression, some exceptions exist simultaneously lacking or (very rarely) expressing both markers, either pattern being associated to poor prognosis. Hence, we quantitatively analyzed the relationship between their coordinated activity and prognosis. By analyzing the respective downstream transcriptional programs of the two genes, we defined a simple quantitative index summarizing the amount of mutual exclusivity between their activities, called Mean Absolute Activity (MAA). Systematic analysis of the MAA index in a dataset of 1018 NSCLC samples replicated on a validation dataset of 275 showed that the loss of imbalance between TTF-1 and p40 corresponds to a steady, progressive reduction in both overall and recurrence-free survival. Coherently, samples correspondent to more balanced activities were enriched for pathways related to increased malignancy and invasiveness. Importantly, multivariate analysis showed that the prognostic significance of the proposed index MAA is independent of other clinical variables including stage, sex, age and smoke exposure. These results hold irrespectively of tumor morphology across NSCLC subtypes, providing a unifying description of different expression patterns.

Alteration of serum bile acids in non-small cell lung cancer identified by a validated LC-MS/MS method

BACKGROUND

Bile acids (BA) are important metabolites and serve as signaling molecules, which are involve in multiple cancer-related signaling pathways.

METHODS

A validated LC-MS/MS approach was applied in a case-control study with 220 non-small cell lung cancer (NSCLC) patients and 244 matched healthy controls. The concentrations of seven common types of BAs in serum were determined and compared. Subgroup analyses based on demographic factor, lifestyle, pathologic types and tumor stage were conducted. Machine learning analysis was performed for NSCLC classification.

RESULTS

Serum levels of primary BAs, including cholic acid (CA), taurocholic acid (TCA) and glycocholic acid (GCA), were upregulated, while lithocholic acid (LCA), a type of secondary BA, was downregulated in NSCLC patients compared with healthy controls in overall analysis. Higher level of chenodeoxycholic acid (CDCA) and lower level of ursodeoxycholic acid (UDCA) were observed in female, elder, overweight patients, as well as patients without alcohol use in comparison with controls. CDCA and CA levels were higher only in lung adenocarcinoma (LUAD), and UDCA and DCA levels were lower only in squamous cell carcinoma (LUSC), while the concentrations of TCA, GCA, and LCA were altered prevalently in LUAD and LUSC patients. For discrimination of NSCLC from healthy people, the area under the receiver operating characteristics (ROC) curve of the models through support vector machine (SVM) approach was 0.91 (95% CI 0.88-0.94) in the training set and 0.84 (95% CI 0.78-0.91) in the validation set, respectively.

CONCLUSIONS

Serum BAs were altered in NSCLC patients compared with controls, among which primary BAs were elevated and secondary BAs were decreased. Moreover, distinct patterns of BA alterations were revealed between LUAD patients and LUSC patients.

Leveraging diverse cell-death patterns to predict the prognosis, immunotherapy and drug sensitivity of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) poses clinical challenges due to its varied prognosis, tumor microenvironment attributes, and responses to immunotherapy. We established a novel Programmed Cell Death-related Signature (PRS) for ccRCC assessment, derived through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. We validated PRS using the E-MTAB-1980 dataset and created PCD-related clusters via non-negative matrix factorization (NMF). Our investigation included an in-depth analysis of immune infiltration scores using various algorithms. Additionally, we integrated data from the Cancer Immunome Atlas (TCIA) for ccRCC immunotherapy insights and leveraged the Genomics of Drug Sensitivity in Cancer (GDSC) database to assess drug sensitivity models. We complemented our findings with single-cell sequencing data and employed the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and qRT-PCR to compare gene expression profiles between cancerous and paracancerous tissues. PRS serves as a valuable tool for prognostication, immune characterization, tumor mutation burden estimation, immunotherapy response prediction, and drug sensitivity assessment in ccRCC. We identify five genes with significant roles in cancer promotion and three genes with cancer-suppressive properties, further validated by qRT-PCR and CPTAC analyses, showcasing gene expression differences in ccRCC tissues. Our study introduces an innovative PCD model that amalgamates diverse cell death patterns to provide accurate predictions for clinical outcomes, mutational profiles, and immune characteristics in ccRCC. Our findings hold promise for advancing personalized treatment strategies in ccRCC patients.

Downregulation of pro-surfactant protein B contributes to the recurrence of early-stage non-small cell lung cancer by activating PGK1-mediated Akt signaling

Recurrence is one of the main causes of treatment failure in early-stage non-small cell lung cancer (NSCLC). However, there are no predictors of the recurrence of early-stage NSCLC, and the molecular mechanism of its recurrence is not clear. In this study, we used clinical sample analysis to demonstrate that low levels of expression of precursor surfactant protein B (pro-SFTPB) in primary NSCLC tissue compared to their adjacent tissues are closely correlated with recurrence and poor prognosis in early-stage NSCLC patients. In vitro and in vivo experiments showed that downregulation of pro-SFTPB expression activates the Akt pathway by upregulating PGK1, which promotes metastasis and tumorigenicity in NSCLC cells. We then demonstrated that pro-SFTPB suppresses the formation of the ADRM1/hRpn2/UCH37 complex by binding to ADRM1, which inhibits PGK1 deubiquitination, thus accelerating ubiquitin-mediated PGK1 degradation. In summary, our findings indicate that low expression of pro-SFTPB in primary NSCLC compared to their adjacent tissue has potential as a predictor of recurrence and poor prognosis in early-stage NSCLC. Mechanistically, downregulation of pro-SFTPB attenuates inhibition of ADRM1-deubiquitinated PGK1, resulting in elevated levels of PGK1 protein; this activates the Akt pathway, ultimately leading to the progression of early-stage NSCLC.

Candidate pathway analysis of surfactant proteins identifies CTSH and SFTA2 that influences lung cancer risk

Pulmonary surfactant is a lipoprotein synthesized and secreted by alveolar type II cells in lung. We evaluated the associations between 200,139 single nucleotide polymorphisms (SNPs) of 40 surfactant-related genes and lung cancer risk using genotyped data from two independent lung cancer genome-wide association studies. Discovery data included 18,082 cases and 13,780 controls of European ancestry. Replication data included 1,914 cases and 3,065 controls of European descent. Using multivariate logistic regression, we found novel SNPs in surfactant-related genes CTSH [rs34577742 C > T, odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.89-0.93, P = 7.64 × 10-9] and SFTA2 (rs3095153 G > A, OR = 1.16, 95% CI = 1.10-1.21, P = 1.27 × 10-9) associated with overall lung cancer in the discovery data and validated in an independent replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.80-0.96, P = 5.76 × 10-3) and SFTA2 (rs3095153 G > A, OR = 1.14, 95% CI = 1.01-1.28, P = 3.25 × 10-2). Among ever smokers, we found SNPs in CTSH (rs34577742 C > T, OR = 0.89, 95% CI = 0.85-0.92, P = 1.94 × 10-7) and SFTA2 (rs3095152 G > A, OR = 1.20, 95% CI = 1.14-1.27, P = 4.25 × 10-11) associated with overall lung cancer in the discovery data and validated in the replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.79-0.97, P = 1.64 × 10-2) and SFTA2 (rs3095152 G > A, OR = 1.15, 95% CI = 1.01-1.30, P = 3.81 × 10-2). Subsequent transcriptome-wide association study using expression weights from a lung expression quantitative trait loci study revealed genes most strongly associated with lung cancer are CTSH (PTWAS = 2.44 × 10-4) and SFTA2 (PTWAS = 2.32 × 10-6).

Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation

The immune-specialized environment of the healthy brain is tightly regulated to prevent excessive neuroinflammation. However, after cancer development, a tissue-specific conflict between brain-preserving immune suppression and tumor-directed immune activation may ensue. To interrogate potential roles of T cells in this process, we profiled these cells from individuals with primary or metastatic brain cancers via integrated analyses on the single-cell and bulk population levels. Our analysis revealed similarities and differences in T cell biology between individuals, with the most pronounced differences observed in a subgroup of individuals with brain metastasis, characterized by accumulation of CXCL13-expressing CD39+ potentially tumor-reactive T (pTRT) cells. In this subgroup, high pTRT cell abundance was comparable to that in primary lung cancer, whereas all other brain tumors had low levels, similar to primary breast cancer. These findings indicate that T cell-mediated tumor reactivity can occur in certain brain metastases and may inform stratification for treatment with immunotherapy.

Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis

BACKGROUND

Lung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments.

METHODS

The expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes.

RESULTS

A total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis.

CONCLUSION

A novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

Immune checkpoint inhibitors for the treatment of non-small cell lung cancer brain metastases

ABSTRACT

Lung cancer has the highest risk of brain metastasis (BM) among all solid carcinomas. The emergence of BM has a significant impact on the selection of oncologic treatment for patients. Immune checkpoint inhibitors (ICIs) are the most promising treatment option for patients without druggable mutations and have been shown to improve survival in patients with non-small cell lung cancer (NSCLC) BM in clinical trials with good safety. Moreover, ICI has shown certain effects in NSCLC BM, and the overall intracranial efficacy is comparable to extracranial efficacy. However, a proportion of patients showed discordant responses in primary and metastatic lesions, suggesting that multiple mechanisms may exist underlying ICI activity in BM. According to studies pertaining to tumor immune microenvironments, ICIs may be capable of provoking immunity in situ. Meanwhile, systematic immune cells activated by ICIs can migrate into the central nervous system and exert antitumor effects. This review summarizes the present evidence for ICI treatment efficacy in NSCLC BM and proposes the possible mechanisms of ICI treatment for NSCLC BMs based on existing evidence.

Genes whose expressions in the primary lung squamous cell carcinoma are able to accurately predict the progression of metastasis through lymphatic system, inferred from a bioinformatics analyses

Lymph node metastasis is the most important prognostic factor in patients with lung squamous cell carcinoma. The current findings show that lymph node metastatic tumor cells can arise by programming metastasis in primary tumor cells. Thereby, the genetic alterations responsible for the metastasis could be detected in the primary tumors. This bioinformatic study aimed to determine novel potential prognostic biomarkers shared between primary lung squamous cell tumors (without lymph node metastasis) and lymphatic metastasis, using the Cancer Genome Atlas database. Differentially expressed genes were screened by limma statistical package in R environment. Gene ontology and biological pathways analyses were performed using Enrichr for up-regulated and down-regulated genes. Also, we selected lymph node metastasis related genes among DEGs using correlation analysis between DEGs and suitable references genes for metastasis. Receiver operating characteristic curves was applied using pROC and R package ggplot2 to evaluate diagnostic value of differentially expressed genes. In addition, survival and drug resistance analyses were performed for differentially expressed genes. The miRNA-mRNA interaction networks were predicted by miRwalk and TargetScan databases and expression levels analysis of the miRNAs which were mainly targeting mRNAs was performed using UALCAN database. Protein-protein interaction network analysis and hub genes identification were performed using FunRich and Cytoscape plugin cytoHubba. In this study, a total of 397 genes were differentially expressed not only with a significant difference between N + vs. normal and N0 vs. normal but also with significant difference between N + vs. N0. Identified GO terms and biological pathways were consistent with DEGs role in the lung squamous cell carcinoma and lymph node metastasis. A significant correlation between 56 genes out of 397 differentially expressed genes with reference genes prompted them being considered for identifying lymph node metastasis of lung squamous cell carcinoma. In addition, SLC46A2, ZNF367, AC107214.1 and NCBP1 genes were identified as survival-related genes of patients with lung squamous cell carcinoma. Moreover, NEDD9, MRPL21, SNRPF, and SCLT1 genes were identified to be involved in lung squamous cell carcinoma drug sensitivity/resistance. We have identified several numbers of miRNAs and their related target genes which could emerge as potential diagnostic biomarkers. Finally, CDK1, PLK1, PCNA, ZWINT and NDC80 identified as hub genes for underlying molecular mechanisms of lung squamous cell carcinoma and lymphatic metastasis. Our study highlights new target genes according to their relation to lymph node metastasis, whose expressions in the primary lung squamous cell carcinoma are able to accurately assess the presence of lymphatic metastasis.

Immune related biomarkers for cancer metastasis to the brain

Brain metastasis accounts for a large number of cancer-related deaths. The host immune system, involved at each step of the metastatic cascade, plays an important role in both the initiation of the brain metastasis and their treatment responses to various modalities, through either local and or systemic effect. However, few reliable immune biomarkers have been identified in predicting the development and the treatment outcome in patients with cancer brain metastasis. Here, we provide a focused perspective of immune related biomarkers for cancer metastasis to the brain and a thorough discussion of the potential utilization of specific biomarkers such as tumor mutation burden (TMB), genetic markers, circulating and tumor-infiltrating immune cells, cytokines, in predicting the brain disease progression and regression after therapeutic intervention. We hope to inspire the field to extend the research and establish practical guidelines for developing and validating immune related biomarkers to provide personalized treatment and improve treatment outcomes in patients with metastatic brain cancers.

Establishing an 8-gene immune prognostic model based on TP53 status for lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) results in a majority of cancer burden worldwide. TP53 is the most commonly mutated in LUAD. This study aimed to reveal the relation between TP53 and tumor microenvironment (TME) for improving LUAD treatment.

Methods

Differentially expressed genes (DEGs) related to immunity were analyzed between TP53‐WT and TP53‐MUT groups. Least absolute shrinkage and selection operator (LASSO) Cox regression was applied to screen prognostic DEGs. Two independent datasets were included to evaluate the robustness of the prognostic model.

Results

An 8‐gene prognostic model containing ANLN, CCNB1, DLGAP5, FAM83A, GJB2, NAPSA, SFTPB, and SLC2A1 was established based on DEGs. LUAD samples were classified into high‐ and low‐risk groups with differential overall survival in the two datasets. M0 macrophages, M1 macrophages, and activated memory CD4 T cells were more enriched in high‐risk group. Immune checkpoints of PDCD1, LAG3, and CD274 were also high‐expressed in high‐risk group.

Conclusion

The study improved the understanding of the role of TP53 in the TME modulation. The 8‐gene model had robust performance to predict LUAD prognosis in clinical practice. In addition, the eight prognostic genes may also serve as potential targets for designing therapeutic drugs for LUAD patients.

Differential Regulation of Human Surfactant Protein A Genes, SFTPA1 and SFTPA2, and Their Corresponding Variants

The human SFTPA1 and SFTPA2 genes encode the surfactant protein A1 (SP-A1) and SP-A2, respectively, and they have been identified with significant genetic and epigenetic variability including sequence, deletion/insertions, and splice variants. The surfactant proteins, SP-A1 and SP-A2, and their corresponding variants play important roles in several processes of innate immunity as well in surfactant-related functions as reviewed elsewhere [1]. The levels of SP-A have been shown to differ among individuals both under baseline conditions and in response to various agents or disease states. Moreover, a number of agents have been shown to differentially regulate SFTPA1 and SFTPA2 transcripts. The focus in this review is on the differential regulation of SFTPA1 and SFTPA2 with primary focus on the role of 5′ and 3′ untranslated regions (UTRs) and flanking sequences on this differential regulation as well molecules that may mediate the differential regulation.

ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

BACKGROUND

Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study.

METHODS

Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells.

RESULTS

We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes.

CONCLUSION

Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.

Tumor immune microenvironment in brain metastases from gynecologic malignancies

INTRODUCTION

The density and distribution of the tumor immune microenvironment associated with brain metastases (BM) from gynecologic malignancies are unknown and have not been previously reported. We sought to describe the clinical features of a cohort of patients with BM from gynecologic malignancies and to characterize the tumor immune microenvironment from available archival surgical specimens.

METHODS

We performed a retrospective review of electronic medical records from 2002 to 2018 for patients with BM from gynecologic malignancies. Data on patient characteristics, treatment regimens, and clinical outcomes were procured. CD4, CD8, CD45RO, CD68, CD163, and FOXP3 immunohistochemistry were evaluated from available archival surgical specimens from primary disease site and neurosurgical resection.

RESULTS

A cohort of 44 patients with BM from gynecologic malignancies was identified, 21 (47.7%) endometrial primaries and 23 (52.3%) ovarian primaries. Tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) were evaluated in 13 primary cases and 15 BM cases. For the 13 primary cases, CD4+ TILs were evident in 76.9% of cases, CD8+ in 92.3%, CD45RO+ in 92.3%, and FOXP3+ in 46.2%, as well as CD68+ TAMs in 100% and CD163+ in 100%. For the 15 BM cases, CD4+ TILs were evident in 60.0% of cases, CD8+ in 93.3%, CD45RO+ in 73.3%, and FOXP3+ in 35.7%, as well as CD68+ TAMs in 86.7% and CD163+ in 100%.

CONCLUSION

An active tumor immune microenvironment is present with similar distribution in the primary disease site and BM from patients with gynecologic malignancies.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data

MOTIVATION

The discovery of cancer subtyping can help explore cancer pathogenesis, determine clinical actionability in treatment, and improve patients' survival rates. However, due to the diversity and complexity of multi-omics data, it is still challenging to develop integrated clustering algorithms for tumor molecular subtyping.

RESULTS

We propose Subtype-GAN, a deep adversarial learning approach based on the multiple-input multiple-output neural network to model the complex omics data accurately. With the latent variables extracted from the neural network, Subtype-GAN uses consensus clustering and the Gaussian Mixture model to identify tumor samples' molecular subtypes. Compared with other state-of-the-art subtyping approaches, Subtype-GAN achieved outstanding performance on the benchmark data sets consisting of ∼4,000 TCGA tumors from 10 types of cancer. We found that on the comparison data set, the clustering scheme of Subtype-GAN is not always similar to that of the deep learning method AE but is identical to that of NEMO, MCCA, VAE, and other excellent approaches. Finally, we applied Subtype-GAN to the BRCA data set and automatically obtained the number of subtypes and the subtype labels of 1031 BRCA tumors. Through the detailed analysis, we found that the identified subtypes are clinically meaningful and show distinct patterns in the feature space, demonstrating the practicality of Subtype-GAN.

AVAILABILITY

The source codes, the clustering results of Subtype-GAN across the benchmark data sets are available at https://github.com/haiyang1986/Subtype-GAN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell RNA sequencing reveals heterogeneous tumor and immune cell populations in early-stage lung adenocarcinomas harboring EGFR mutations

Lung adenocarcinoma (LUAD) harboring EGFR mutations prevails in Asian population. However, the inter-patient and intra-tumor heterogeneity has not been addressed at single-cell resolution. Here we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II LUAD samples harboring EGFR mutations and five tumor-adjacent lung tissues. We identified diverse cell types within the tumor microenvironment (TME) in which myeloid cells and T cells were the most abundant stromal cell types in tumors and adjacent lung tissues. Within tumors, accompanied by an increase in CD1C+ dendritic cells, the tumor-associated macrophages (TAMs) showed pro-tumoral functions without signature gene expression of defined M1 or M2 polarization. Tumor-infiltrating T cells mainly displayed exhausted and regulatory T-cell features. The adenocarcinoma cells can be categorized into different subtypes based on their gene expression signatures in distinct pathways such as hypoxia, glycolysis, cell metabolism, translation initiation, cell cycle, and antigen presentation. By performing pseudotime trajectory, we found that ELF3 was among the most upregulated genes in more advanced tumor cells. In response to secretion of inflammatory cytokines (e.g., IL1B) from immune infiltrates, ELF3 in tumor cells was upregulated to trigger the activation of PI3K/Akt/NF-κB pathway and elevated expression of proliferation and anti-apoptosis genes such as BCL2L1 and CCND1. Taken together, our study revealed substantial heterogeneity within early-stage LUAD harboring EGFR mutations, implicating complex interactions among tumor cells, stromal cells and immune infiltrates in the TME.

Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) is a frequently diagnosed histologic subtype with increasing morbidity and mortality. RalGDS-Like 4 (RGL4) has not been reported to be associated with cancer risk, prognosis, immunotherapy or any other treatments. We perform a bioinformatics analysis on data downloaded from the Cancer Genome Atlas (TCGA)-LUAD, and we find that low expression of RGL4 is accompanied by worse outcomes and prognosis in LUAD patients. As a promising predictor, the potential influence and mechanisms of RGL4 on overall survival are worth exploring. Moreover, RGL4 expression is significantly associated with a variety of tumor-infiltrating immune cells (TIICs), particularly memory B cells, CD8⁺T cells and neutrophils. Subsequently, we evaluated the most notable KEGG pathways, including glycolysis gluconeogenesis, the P53 signaling pathway, RNA degradation, and the B cell receptor signaling pathway, among others. Our findings provide evidence that the decreased expression of RGL4 is significantly associated with poor prognosis and immune cell infiltration in patients with LUAD and highlight the use of RGL4 as a novel predictive biomarker for the prognosis of LUAD and other cancers. RGL4 may also be used in combination with immune checkpoints to identify the benefits of immunotherapy. Subjects: Bioinformatics, Genomics, Oncology, Thoracic surgery.