Analysis of Lung Adenocarcinoma Subtypes Based on Immune Signatures Identifies Clinical Implications for Cancer Therapy

SIRT2 as a Potential Biomarker in Lung Adenocarcinoma: Implications for Immune Infiltration

SIRT2 play important roles in cell cycle and cellular metabolism in the development of non-small cell lung cancer (NSCLC), and SIRT2 exhibits its therapeutic effect on NSCLC tumors with high expression of SIRT2. Nevertheless, the clinical relevance of SIRT2 in lung adenocarcinoma (LUAD), particularly its impact on tumor growth and prognostic implications, remains obscure. This investigation entailed a comprehensive analysis of SIRT2 mRNA and protein expression levels in diverse tumor and corresponding healthy tissues, utilizing databases such as TIMER 2.0, UALCAN, and HPA. Prognostic correlations of SIRT2 expression in LUAD patients, stratified by distinct clinicopathological characteristics, were evaluated using the KM Plotter database. Additionally, the TCGA and TIMER 2.0 databases were employed to assess the relationship between SIRT2 and immune infiltration, as well as to calculate immunity, stromal, and estimation scores, thus elucidating the role of SIRT2 in modulating tumor immunotherapy responses. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to elucidate SIRT2's biological functions in pan-cancer cells. Our findings revealed a marked reduction in both mRNA and protein levels of SIRT2 in LUAD tumors relative to healthy tissue. Survival analysis indicated that diminished SIRT2 expression correlates with adverse prognostic outcomes in LUAD. Furthermore, SIRT2 expression demonstrated a significant association with various clinicopathologic attributes of LUAD patients, influencing survival outcomes across different clinicopathologic states. Functional enrichment analyses highlighted SIRT2's involvement in cell cycle regulation and immune response. Notably, SIRT2 exhibited a positive correlation with immune cell infiltration, including natural killer (NK) cells, macrophages, and dendritic cells (DCs). In summary, SIRT2 was a potential prognostic biomarker for LUAD and and a new immunotherapy target.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

Background

Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets.

Methods

According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared.

Results

Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients.

Conclusions

Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning

BACKGROUND

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

METHODS

We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision.

RESULTS

wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes.

CONCLUSIONS

The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

The potential role and mechanism of circRNAs in Ferroptosis: A comprehensive review

Cell death encompasses various mechanisms, including necrosis and apoptosis. Ferroptosis, a unique form of regulated cell death, emerged as a non-apoptotic process reliant on iron and reactive oxygen species (ROS). Distinguishing itself from other forms of cell death, ferroptosis exhibits distinct morphological, biochemical, and genetic features. Circular RNAs (circRNAs), a novel class of RNA molecules, play crucial regulatory roles in ferroptosis-mediated pathways and cellular processes. With their circular structure and stability, circRNAs function as microRNA sponges and participate in protein regulation, offering diverse mechanisms for cellular control. Accumulating evidence indicates that circRNAs are key players in diseases associated with ferroptosis, presenting opportunities for diagnostic and therapeutic applications. This study explores the regulatory roles of circRNAs in ferroptosis and their potential in diseases such as cancer, neurological disorders, and cardiovascular diseases. By investigating the relationship between circRNAs and ferroptosis, this research provides new insights into the diagnosis, treatment, and prognosis of ferroptosis-related diseases. Furthermore, the therapeutic implications of targeting circRNAs in cancer treatment and the modulation of ferroptosis pathways demonstrate the potential of circRNAs as diagnostic markers and therapeutic targets. Overall, understanding the involvement of circRNAs in regulating ferroptosis opens up new avenues for advancements in disease management.

A multiomic investigation of lung adenocarcinoma molecular subtypes

BACKGROUND

Lung adenocarcinoma-an aggressive and life-threatening malignancy-is a type of non-small-cell lung cancer. Despite medical advancements, the prognosis of lung adenocarcinoma remains unfavorable, likely because of its heterogeneous nature. Furthermore, few subtype-specific treatments are available for lung adenocarcinoma. This study was conducted to explore the molecular subtypes of lung adenocarcinoma.

METHODS

We performed a joint analysis of transcriptome and proteome data from East Asian patients with lung adenocarcinoma (nonsmokers, 86.5%).

RESULTS

Four novel subtypes were identified based on distinct molecular characteristics: subtypes I, II, III, and IV. In patients with subtype I lung adenocarcinoma, eukaryotic translation initiation factor 4 gamma 1 activates cell proliferation; inhibiting this factor suppresses tumor growth, and reducing its level induces autophagy. Subtype II is characterized by Kristen rat sarcoma viral oncogene homolog-activating oncogenesis; the onset age of this subtype is the lowest among all subtypes. Subtype III manifests as an advanced disease at diagnosis; it is characterized by a core serum response-related oncogenic signature, which indicates poor overall survival in Western patients with lung cancer. Subtype IV is more common in men than in women; it has astroglial characteristics. A Connectivity Map analysis revealed that the oncogenic expression patterns corresponding to subtypes I, II, III, and IV can be reversed by the inhibitors of Inhibitor of κB (IκB) kinase (eg, withaferin A), mammalian target of rapamycin (eg, everolimus), Src proto-oncogene (Src) (eg, saracatinib), and Transforming Growth Factor (TGF)-β/Smad (eg, LY-364947), respectively.

CONCLUSION

This study introduced an innovative multiomics data analysis pipeline. Using this approach, we successfully identified four molecular subtypes of lung adenocarcinoma and their candidate therapeutic agents. The newly identified subtypes can be combined with the current biomarkers to generate a comprehensive roadmap for treatment decision-making.

Investigating subtypes of lung adenocarcinoma by oxidative stress and immunotherapy related genes

Lung adenocarcinoma (LUAD) is one of the most widespread and fatal types of lung cancer. Oxidative stress, resulting from an imbalance in the production and accumulation of reactive oxygen species (ROS), is considered a promising therapeutic target for cancer treatment. Currently, immune checkpoint blockade (ICB) therapy is being explored as a potentially effective treatment for early-stage LUAD. In this research, we aim to identify distinct subtypes of LUAD patients by investigating genes associated with oxidative stress and immunotherapy. Additionally, we aim to propose subtype-specific therapeutic strategies. We conducted a thorough search of the Gene Expression Omnibus (GEO) datasets. From this search, we pinpointed datasets that contained both expression data and survival information. We selected genes associated with oxidative stress and immunotherapy using keyword searches on GeneCards. We then combined expression data of LUAD samples from both The Cancer Genome Atlas (TCGA) and 11 GEO datasets, forming a unified dataset. This dataset was subsequently divided into two subsets, Dataset_Training and Dataset_Testing, using a random bifurcation method, with each subset containing 50% of the data. We applied consensus clustering (CC) analysis to identify distinct LUAD subtypes within the Dataset_Training. Molecular variances associated with oxidative stress levels, the tumor microenvironment (TME), and immune checkpoint genes (ICGs) were then investigated among these subtypes. Employing feature selection combined with machine learning techniques, we constructed models that achieved the highest accuracy levels. We validated the identified subtypes and models from Dataset_Training using Dataset_Testing. A hub gene with the highest importance values in the machine learning model was identified. We then utilized virtual screening to discover potential compounds targeting this hub gene. In the unified dataset, we integrated 2,154 LUAD samples from TCGA-LUAD and 11 GEO datasets. We specifically selected 1,311 genes associated with immune and oxidative stress processes. The expression data of these genes were then employed for subtype identification through CC analysis. Within Dataset_Training, two distinct subtypes emerged, each marked by different levels of immune and oxidative stress pathway values. Consequently, we named these as the OX+ and IM+ subtypes. Notably, the OX+ subtype showed increased oxidative stress levels, correlating with a worse prognosis than the IM+ subtype. Conversely, the IM+ subtype demonstrated enhanced levels of immune pathways, immune cells, and ICGs compared to the OX+ subtype. We reconfirmed these findings in Dataset_Testing. Through gene selection, we identified an optimal combination of 12 genes for predicting LUAD subtypes: ACP1, AURKA, BIRC5, CYC1, GSTP1, HSPD1, HSPE1, MDH2, MRPL13, NDUFS1, SNRPD1, and SORD. Out of the four machine learning models we tested, the support vector machine (SVM) stood out, achieving the highest area under the curve (AUC) of 0.86 and an accuracy of 0.78 on Dataset_Testing. We focused on HSPE1, which was designated as the hub gene due to its paramount importance in the SVM model, and computed the docking structures for four compounds: ZINC3978005 (Dihydroergotamine), ZINC52955754 (Ergotamine), ZINC150588351 (Elbasvir), and ZINC242548690 (Digoxin). Our study identified two subtypes of LUAD patients based on oxidative stress and immunotherapy-related genes. Our findings provided subtype-specific therapeutic strategies.

Identification of M5c regulator-medicated methylation modification patterns for prognosis and immune microenvironment in glioma

Glioma is a common intracranial tumor and is generally associated with poor prognosis. Recently, numerous studies illustrated the importance of 5-methylcytosine (m5C) RNA modification to tumorigenesis. However, the prognostic value and immune correlation of m5C in glioma remain unclear. We obtained RNA expression and clinical information from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) datasets to analyze. Nonnegative matrix factorization (NMF) was used to classify patients into two subgroups and compare these patients in survival and clinicopathological characteristics. CIBERSORT and single-sample gene-set algorithm (ssGSEA) methods were used to investigate the relationship between m5C and the immune environment. The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to construct a m5C-related signature. Most of m5C RNA methylation regulators presented differential expression and prognostic values. There were obvious relationships between immune infiltration cells and m5C regulators, especially NSUN7. In the m5C-related module from WGCNA, we found SEPT3, CHI3L1, PLBD1, PHYHIPL, SAMD8, RAP1B, B3GNT5, RER1, PTPN7, SLC39A1, and MXI1 were prognostic factors for glioma, and they were used to construct the signature. The great significance of m5C-related signature in predicting the survival of patients with glioma was confirmed in the validation sets and CGGA cohort.

RNA modification in mRNA cancer vaccines

RNA modification is manifested as chemically altered nucleotides, widely exists in diverse natural RNAs, and is closely related to RNA structure and function. Currently, mRNA-based vaccines have received great attention and rapid development as novel and mighty fighters against various diseases including cancer. The achievement of RNA vaccines in clinical application is largely attributed to some methodological innovations including the incorporation of modified nucleotides into the synthetic RNA. The selection of optimal RNA modifications aimed at reducing the instability and immunogenicity of RNA molecules is a very critical task to improve the efficacy and safety of mRNA vaccines. This review summarizes the functions of RNA modifications and their application in mRNA vaccines, highlights recent advances of mRNA vaccines in cancer immunotherapy, and provides perspectives for future development of mRNA vaccines in the context of personalized tumor therapy.

Abnormal genetic and epigenetic patterns of m6A regulators associated with tumor microenvironment in colorectal cancer

Background

N6-methyladenosine (m6A) has a critical role in the development and progression of cancer. However, the genetic and epigenetic patterns, as well as tumor microenvironment (TME) infiltration characteristics of m6A regulators in colorectal cancer (CRC) remain largely unknown.

Methods

Molecular patterns of m6A modifications of 24 m6A regulators in CRC samples were evaluated using data from The Cancer Genome Atlas (TCGA). Mutations, copy number variations (CNVs), DNA methylation, and chromatin accessibility were examined to investigate the underlying mechanisms of the aberrant expression of m6A regulators. Correlations between m6A-related genes and TME cell-infiltrating characteristics were evaluated using Tumor Immune Estimation Resource (TIMER).

Results

The m6A regulators were frequently dysregulated in CRC, with two downregulated and 16 upregulated. All the m6A regulators had mutations (frequency ranging from 0.9% to 7%), with active mutations tending to occur in RBM15 and inactive mutations in ZC3H13. Only five m6A regulators had CNV frequency greater than 1%: YTHDC2 (2.4%), YTHDF1 (7.0%), YTHDF3 (1.9%), VIRMA (1.7%), and ZC3H13 (3.0%). The copy numbers of these five genes were positively correlated with their expression levels. The m6A regulators frequently showed imbalanced methylation in CRC, with hypomethylation of YTHDF2, IGF2BP3, FTO, and hypermethylation of HNRNPC, METTL3, and WTAP. Most m6A regulators had high chromatin accessibility, which was positively correlated with their gene expression. IGF2BP1 was identified as an independent prognostic factor for overall survival. Moreover, the expression of most m6A regulators was positively correlated with the infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells.

Conclusions

Aberrant expression of m6A regulators is associated with mutation, CNV, and chromatin accessibility, owing to both genetic and epigenetic modifications. The TME infiltration characterization of m6A regulators could guide the development of more effective immunotherapy strategies in CRC.

Tumor-infiltrating Leukocyte Profiling Defines Three Immune Subtypes of NSCLC with Distinct Signaling Pathways and Genetic Alterations

Resistance to immune checkpoint blockade remains challenging in patients with non-small cell lung cancer (NSCLC). Tumor-infiltrating leukocyte (TIL) quantity, composition, and activation status profoundly influence responsiveness to cancer immunotherapy. This study examined the immune landscape in the NSCLC tumor microenvironment by analyzing TIL profiles of 281 fresh resected NSCLC tissues. Unsupervised clustering based on numbers and percentages of 30 TIL types classified adenocarcinoma (LUAD) and squamous cell carcinoma (LUSQ) into the cold, myeloid cell-dominant, and CD8⁺ T cell-dominant subtypes. These were significantly correlated with patient prognosis; the myeloid cell subtype had worse outcomes than the others. Integrated genomic and transcriptomic analyses, including RNA sequencing, whole-exome sequencing, T-cell receptor repertoire, and metabolomics of tumor tissue, revealed that immune reaction-related signaling pathways were inactivated, while the glycolysis and K-ras signaling pathways activated in LUAD and LUSQ myeloid cell subtypes. Cases with ALK and ROS1 fusion genes were enriched in the LUAD myeloid subtype, and the frequency of TERT copy-number variations was higher in LUSQ myeloid subtype than in the others. These classifications of NSCLC based on TIL status may be useful for developing personalized immune therapies for NSCLC.

Significance

The precise TIL profiling classified NSCLC into novel three immune subtypes that correlates with patient outcome, identifying subtype-specific molecular pathways and genomic alterations that should play important roles in constructing subtype-specific immune tumor microenvironments. These classifications of NSCLC based on TIL status are useful for developing personalized immune therapies for NSCLC.

Identification of lung adenocarcinoma subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

Background: Lung adenocarcinoma (LUAD) is the most common variant of non-small cell lung cancer (NSCLC) across the world. Recently, the rapid development of immunotherapy has brought a new dawn for LUAD patients. Closely related to the tumor immune microenvironment and immune cell functions, more and more new immune checkpoints have been discovered, and various cancer treatment studies targeting these novel immune checkpoints are currently in full swing. However, studies on the phenotype and clinical significance of novel immune checkpoints in LUAD are still limited, and only a minority of patients with LUAD can benefit from immunotherapy. Methods: The LUAD datasets were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, and the immune checkpoints score of each sample were calculated based on the expression of the 82 immune checkpoints-related genes (ICGs). The weighted gene co-expression network analysis (WGCNA) was used to obtain the gene modules closely related to the score and two different LUAD clusters were identified based on these module genes by the Non-negative Matrix Factorization (NMF) Algorithm. The differentially expressed genes between the two clusters were further used to construct a predictive signature for prognosis, immune features, and the response to immunotherapy for LUAD patients through a series of regression analyses. Results: A new immune checkpoints-related signature was finally established according to the expression of 7 genes (FCER2, CD200R1, RHOV, TNNT2, WT1, AHSG, and KRTAP5-8). This signature can stratify patients into high-risk and low-risk groups with different survival outcomes and sensitivity to immunotherapy, and the signature has been well validated in different clinical subgroups and validation cohorts. Conclusion: We constructed a novel immune checkpoints-related LUAD risk assessment system, which has a good predictive ability and significance for guiding immunotherapy. We believe that these findings will not only aid in the clinical management of LUAD patients but also provide some insights into screening appropriate patients for immunotherapy.

Involvement of ZDHHC9 in lung adenocarcinoma: regulation of PD-L1 stability via palmitoylation

Palmitoylation is a post-translational modification occurring on cysteine residues, which process is catalyzed by a family of zinc finger Asp-His-His-Cys (DHHC) domain-containing (ZDHHC) protein acyltransferases. As a family member, ZDHHC9 plays a crucial role in varied malignancies by regulating protein stability via protein substrate palmitoylation. Based on the bioinformatic analysis of GEO gene microarray GSE75037 (|log₂ fold change|> 1, P < 0.05), ZDHHC9 was defined as a significantly upregulated gene in lung adenocarcinoma (LUAD), which was also confirmed in our collected clinical specimens. It is necessary to explore the biological function of ZDHHC9 in LUAD cells. The follow-up functional experiments revealed that ZDHHC9 deficiency inhibited proliferation, migration, and invasion, while stimulated apoptosis in HCC827 cells. Besides, these malignant phenotypes could be accelerated by ZDHHC9 overexpression in A549. Moreover, we revealed that ZDHHC9 knockdown could promote PD-L1 protein degradation by reducing its palmitoylation level. The reduction of PD-L1 protein level could enhance anti-tumor immunity and inhibit the growth of LUAD cells. Therefore, our study uncovers the tumor-promoting role of ZDHHC9 in LUAD via regulating PD-L1 stability through palmitoylation, highlighting ZDHHC9 as a novel therapeutic target for LUAD.

Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis

Purpose: To explore the clinical significance of copper-dependent-related genes (CDRG) in female breast cancer (BC).

Methods: CDRG were obtained by single-cell analysis of the GSE168410 dataset in the Gene Expression Omnibus (GEO) database. According to a 1:1 ratio, the Cancer Genome Atlas (TCGA) cohort was separated into a training and a test cohort randomly. Based on the training cohort, the prognostic model was built using COX and Lasso regression. The test cohort was used to validate the model. The GSE20685 dataset and GSE20711 dataset were used as two external validation cohorts to further validate the prognostic model. According to the median risk score, patients were classified as high-risk or low-risk. Survival analysis, immune microenvironment analysis, drug sensitivity analysis, and nomogram analysis were used to evaluate the clinical importance of this prognostic model.

Results: 384 CDRG were obtained by single-cell analysis. According to the prognostic model, patients were classified as high-risk or low-risk in both cohorts. The high-risk group had a significantly worse prognosis. The area under the curve (AUC) of the model was around 0.7 in the four cohorts. The immunological microenvironment was examined for a possible link between risk score and immune cell infiltration. Veliparib, Selumetinib, Entinostat, and Palbociclib were found to be more sensitive medications for the high-risk group after drug sensitivity analysis.

Conclusion: Our CDRG-based prognostic model can aid in the prediction of prognosis and treatment of BC patients.

The significance of m6A RNA methylation modification in prognosis and tumor microenvironment immune infiltration of cervical cancer

Recent studies have highlighted that N6-methyladenosine (m6A) plays a significant role in tumorigenicity and progression. However, the mechanism of m6A modifications in the tumor microenvironment (TME) immune cell infiltration in cervical cancer (CC) remains unclear. Clinical and RNA sequencing data of 25 m6A RNA methylation regulators were acquired from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. LASSO Cox regression analysis was used to generate a prognostic risk signature. m6A modification patterns were identified based on the expression of 25 m6A regulators, and their correlation with TME immune cell-infiltrating characterization was analyzed. Principal component analysis was used to construct an m6A-scoring signature (m6A score) to evaluate the m6A modification patterns of individual CC samples and guide the selection of more effective immunotherapeutic strategies. Genetic and expression alterations of 25 m6A regulators were highly heterogeneous between CC and normal tissues. METTL14 and IGF2BP1 were selected to conduct the prognostic risk signature. Three m6A modification patterns were identified in 659 CC samples, which were associated with distinct clinical outcomes and biological pathways. The TME immune cell-infiltrating characterization of the three m6A modification patterns was highly consistent with 3 tumor immune phenotypes, including immune-excluded, immune-inflamed, and immune-desert phenotypes. Due to the heterogeneity of m6A modification patterns, an m6A scoring signature was established to evaluate the m6A modification patterns of individual CC samples. Univariate and multivariate Cox regression analyses revealed that the m6A score is a robust and independent prognostic biomarker for assessing the prognosis of CC patients. A low m6A score, characterized by higher somatic mutation and higher expression of proliferation-related and DNA repair-related genes, indicated poor overall survival. Activation of immune infiltration was exhibited by the high m6A score, which was likely to have a good response and clinical benefits to antiPD-1/L1 immunotherapy. This study highlights the prognostic value of 25 m6A regulators in CC. The m6A modification is related to immune regulation and the formation of TME heterogeneity and complexity. An m6A scoring signature to clarify the individual m6A modification pattern could enhance our understanding of TME immune cell-infiltrating characterization and guide immunotherapy strategies.

State-of-the-Art Molecular Oncology of Lung Cancer in Taiwan

Lung cancers are life-threatening malignancies that cause great healthcare burdens in Taiwan and worldwide. The 5-year survival rate for Taiwanese patients with lung cancer is approximately 29%, an unsatisfactorily low number that remains to be improved. We first reviewed the molecular epidemiology derived from a deep proteogenomic resource in Taiwan. The nuclear factor erythroid 2-related factor 2 (NRF2)antioxidant mechanism was discovered to mediate the oncogenesis and tumor progression of lung adenocarcinoma. Additionally, DNA replication, glycolysis and stress response are positively associated with tumor stages, while cell-to-cell communication, signaling, integrin, G protein coupled receptors, ion channels and adaptive immunity are negatively associated with tumor stages. Three patient subgroups were discovered based on the clustering analysis of protein abundance in tumors. The first subgroup is associated with more advanced cancer stages and visceral pleural invasion, as well as higher mutation burdens. The second subgroup is associated with EGFR L858R mutations. The third subgroup is associated with PI3K/AKT pathways and cell cycles. Both EGFR and PI3K/AKT signaling pathways have been shown to induce NRF2 activation and tumor cell proliferation. We also reviewed the clinical evidence of patient outcomes in Taiwan given various approved targeted therapies, such as EGFR-tyrosine kinase inhibitors and anaplastic lymphoma kinase (ALK)inhibitors, in accordance with the patients’ characteristics. Somatic mutations occurred in EGFR, KRAS, HER2 and BRAF genes, and these mutations have been detected in 55.7%, 5.2%, 2.0% and 0.7% patients, respectively. The EGFR mutation is the most prevalent targetable mutation in Taiwan. EML4-ALK translocations have been found in 9.8% of patients with wild-type EGFR. The molecular profiling of advanced NSCLC is critical to optimal therapeutic decision-making. The patient characteristics, such as mutation profiles, protein expression profiles, drug-resistance profiles, molecular oncogenic mechanisms and patient subgroup systems together offer new strategies for personalized treatments and patient care.

Multi-Omics Integrative Analysis of Lung Adenocarcinoma: An in silico Profiling for Precise Medicine

Lung adenocarcinoma (LUAD) is one of the most common histological subtypes of lung cancer. The aim of this study was to construct consensus clusters based on multi-omics data and multiple algorithms. In order to identify specific molecular characteristics and facilitate the use of precision medicine on patients we used gene expression, DNA methylation, gene mutations, copy number variation data, and clinical data of LUAD patients for clustering. Consensus clusters were obtained using a consensus ensemble of five multi-omics integrative algorithms. Four molecular subtypes were identified. The CS1 and CS2 subtypes had better prognosis. Based on the immune and drug sensitivity predictions, we inferred that CS1 may be less responsive to immunotherapy and less sensitive to chemotherapeutic drugs. The high immune infiltration of CS2 cells may respond well to immunotherapy. Additionally, the CS2 subtype may also respond to EGFR molecular targeted therapy. The CS3 and CS4 subtypes were associated with poor prognosis. These two subtypes had more mutations, especially TP53 ones, as well as higher sensitivity to chemotherapeutics for lung cancer. However, CS3 was enriched in immune-related pathways and may respond to anti-PD1 immunotherapy. In addition, CS1 and CS4 were less sensitive to ferroptosis inhibitors. We performed a comprehensive analysis of the five types of omics data using five clustering algorithms to reveal the molecular characteristics of LUAD patients. These findings provide new insights into LUAD subtypes and potential clinical treatment strategies to guide personalized management and treatment.

Identification of a Tumor Immunological Phenotype-Related Gene Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant subtype of primary liver cancer and represents a highly heterogeneous disease, making it hard to predict the prognosis and therapy efficacy. Here, we established a novel tumor immunological phenotype-related gene index (TIPRGPI) consisting of 11 genes by Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict HCC prognosis and immunotherapy response. TIPRGPI was validated in multiple datasets and exhibited outstanding performance in predicting the overall survival of HCC. Multivariate analysis verified it as an independent predictor and a TIPRGPI-integrated nomogram was constructed to provide a quantitative tool for clinical practice. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in tumor microenvironment were shown between the TIPRGPI high and low-risk groups. Notably, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade (ICB) therapy of the low-risk group. Besides, six potential drugs binding to the core target of the TIPRGPI signature were predicted via molecular docking. Taken together, our study shows that the proposed TIPRGPI was a reliable signature to predict the risk classification, immunotherapy response, and drugs candidate with potential application in the clinical decision and treatment of HCC. The novel “TIP genes”-guided strategy for predicting the survival and immunotherapy efficacy, we reported here, might be also applied to more cancers other than HCC.

N6-Methyladenine-Related Signature for Immune Microenvironment and Response to Immunotherapy in Hepatocellular Carcinoma

Background

The prognostic value of m6A-related genes in hepatocellular carcinoma (HCC) and its correlation with the immune microenvironment still requires further investigation.

Methods

Consensus clustering by m6A related genes was used to classify 374 patients with HCC from The Cancer Genome Atlas (TCGA) database. Then we performed the least absolute shrinkage and selection operator (LASSO) to construct the m6A related genes model. The International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) datasets were used to verify and evaluate the model. ESTIMATE, CIBERSORTx, the expression levels of immune checkpoint genes, and TIDE were used to investigate the tumor microenvironment (TME) and the response to immunotherapy. Gene set enrichment analyses (GSEA), tumor-associated macrophages (TAMs), and gene-drug sensitivity were also analyzed.

Results

By expression value and regression coefficient of five m6A related genes, we constructed the risk score of each patient. The patients with a higher risk score had a considerably poorer prognosis in the primary and validated cohort. For further discussing TME and the response to immunotherapy, we divided the entire set into two groups based on the risk score. Our findings implied that the tumor-infiltrating lymphocytes (TILs) were proportional to the risk scores, which seemed to contradict that patients with higher scores had a poor prognosis. Further, we found that the high-risk group had higher expression of PD-L1, CTLA-4, and PDCD1, indicating immune dysfunction, which may be a fundamental reason for poor prognosis. This was further reinforced by the fact that the low-risk group responded better than the high-risk group to monotherapy and combination therapy.

Conclusion

The m6A related risk score is a new independent prognostic factor that correlates with immunotherapy response. It can provide a new therapeutic strategy for improving individual immunotherapy in HCC.

Identification of an Immune Classification and Prognostic Genes for Lung Adenocarcinoma Based on Immune Cell Signatures

Objective

Current advances in immunotherapy requires accurate tumor sub-classification due to the heterogeneity of lung adenocarcinoma (LUAD). This study aimed to develop a LUAD sub-classification system based on immune cell signatures and identified prognostic gene markers.

Methods

Signatures related to the prognosis of TCGA-LUAD and 4 GSE cohorts were screened and intersected from 184 previously published immune cell signatures. The LUAD samples in the TCGA were clustered by ConsensusClusterPlus. Molecular characteristics, immune characteristics and sensitivity to immunotherapies/chemotherapies were compared. LDA score was established through Linear Discriminant Analysis (LDA). Co-expression module was constructed by Weighted Gene Co-Expression Network Analysis (WGCNA).

Results

Four LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in prognosis among the four subtypes were observed. The IS1 subtype with the worst prognosis showed the highest number of TMB, mutant genes, IFN γ score, angiogenesis score and immune score. Twenty co-expression modules were generated by WGCNA. Blue module, sky blue module and light yellow module were significantly correlated with LUAD prognosis. The hub genes (CCDC90B, ARNTL2, RIPK2, SMCO2 and ADA and NBN) showing great prognostic significance were identified from the blue module. A total of 8 hub genes (NLRC3, CLEC2D, GIMAP5, CXorf65, PARP15, AKNA, ZC3H12D, and ARRDC5) were found in the light yellow module. Except for CXorf65, the expression of the other seven genes were significantly correlated with LUAD prognosis.

Conclusion

This study determined four LUAD subtypes with different molecular and immune characteristics and 13 genes closely related to the prognosis of LUAD. The current findings could help understand the heterogeneity of LUAD immune classes.

Subtyping of sarcomas based on pathway enrichment scores in bulk and single cell transcriptomes

Background

Sarcomas are highly heterogeneous in molecular, pathologic, and clinical features. However, a classification of sarcomas by integrating different types of pathways remains mostly unexplored.

Methods

We performed hierarchical clustering analysis of sarcomas based on the enrichment scores of 14 pathways involved in immune, stromal, DNA damage repair (DDR), and oncogenic signatures in three bulk tumor transcriptome datasets.

Results

Consistently in the three datasets, sarcomas were classified into three subtypes: Immune Class (Imm-C), Stromal Class (Str-C), and DDR Class (DDR-C). Imm-C had the strongest anti-tumor immune signatures and the lowest intratumor heterogeneity (ITH); Str-C showed the strongest stromal signatures, the highest genomic stability and global methylation levels, and the lowest proliferation potential; DDR-C had the highest DDR activity, expression of the cell cycle pathway, tumor purity, stemness scores, proliferation potential, and ITH, the most frequent TP53 mutations, and the worst survival. We further validated the stability and reliability of our classification method by analyzing a single cell RNA-Seq (scRNA-seq) dataset. Based on the expression levels of five genes in the pathways of T cell receptor signaling, cell cycle, mismatch repair, focal adhesion, and calcium signaling, we built a linear risk scoring model (ICMScore) for sarcomas. We demonstrated that ICMScore was an adverse prognostic factor for sarcomas and many other cancers.

Conclusions

Our classification method provides novel insights into tumor biology and clinical implications for sarcomas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03248-3.

N6-Methyladenosine RNA Modification in the Tumor Immune Microenvironment: Novel Implications for Immunotherapy

N6-methyladenosine (m⁶A) methylation is one of the most common modifications of RNA in eukaryotic cells, and is mainly regulated by m⁶A methyltransferases (writers), m⁶A demethylases (erasers), and m⁶A binding proteins (readers). Recently, accumulating evidence has shown that m⁶A methylation plays crucial roles in the regulation of the tumor immune microenvironment, greatly impacting the initiation, progression, and metastasis processes of various cancers. In this review we first briefly summarizes the m⁶A-related concepts and detection methods, and then describes in detail the associations of m⁶A methylation modification with various tumor immune components especially immune cells (e.g., regulatory T cells, dendritic cells, macrophages, and myeloid-derived suppressor cells) in a variety of cancers. We discuss the relationship between m⁶A methylation and cancer occurrence and development with the involvement of tumor immunity highlighted, suggesting novel markers and potential targets for molecular pathological diagnosis and immunotherapy of various cancers.

WT1-AS/IGF2BP2 Axis Is a Potential Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma According to ceRNA Network Comprehensive Analysis Combined with Experiments

Lung adenocarcinoma (LUAD) is one of the most common malignancies, and there is still a lack of effective biomarkers for early detection and prognostic prediction. Here, we comprehensively analyze the characteristics of. an RNA sequencing data set of LUAD samples. In total, 395 long non-coding RNAs (lncRNAs), 89 microRNAs (miRNAs), and 872 mRNAs associated with c-Myc were identified, which were differentially expressed between tumor and normal tissues. The most relevant pathway was found to be WT1-AS–miR-200a-3p–IGF2BP2 according to the rules of competitive endogenous RNA (ceRNA) regulation. WT1-AS and IGF2BP2 expression were positively correlated and increased in LUAD samples, while miR-200a-3p had relatively low expression. The high expression of WT1-AS and IGF2BP2 was associated with poor prognosis in LUAD patients, while low expression of miR-200a-3p predicted reduced survival (p < 0.05). The analysis of the multi-gene regulation model indicated that the WT1-AS (downregulation)–miR-200a-3p (upregulation)–IGF2BP2 (downregulation) pattern significantly improved the survival of LUAD patients. Finally, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting were detected in LUAD cells, and the results are consistent with the bioinformatics analysis. In summary, the WT1-AS/IGF2BP2 axis is a potential prognostic biomarker in LUAD and is expected to become an effective target for diagnosis and treatment.

The Predictive Role of Immune Related Subgroup Classification in Immune Checkpoint Blockade Therapy for Lung Adenocarcinoma

Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete. Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort. Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification. Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.

Identification of ubiquitinated substrate proteins and their gene expression patterns in lung adenocarcinoma

Background

Lung cancer is a malignant disease with the highest cancer-related mortality rate. In lung adenocarcinoma (LUAD), protein ubiquitination can regulate multiple biological processes. A LUAD ubiquitylome analysis has not yet been reported.

Methods

We used for the first time ion mobility into liquid chromatography-mass spectrometry to perform accurate and reliable ubiquitylome and proteomic analysis of clinical LUAD and normal tissues and combined it with transcriptome data obtained from public databases. Ubiquitinated protein substrates and their gene expression pattern landscapes in LUAD were identified using bioinformatics methods.

Results

Our data revealed a ubiquitination landscape in LUAD and identified characteristic protein ubiquitination motifs. We found that the ubiquitinated peptide motifs in LUAD were completely different from those of previously published lung squamous cell carcinoma (LUSC). Moreover, we identified two gene expression patterns of ubiquitinated proteins and revealed that survival differences between these patterns may be correlated with the tumor immune infiltrating microenvironment. Finally, we constructed a prognostic predictive model to quantify the relationship between expression patterns and survival. We found a relationship between the patient-applied model score and multiple drug sensitivity. Therefore, our model can serve as a guide for LUAD clinical treatment.

Conclusions

Our work addresses the lack of ubiquitylome studies in LUAD and provides new perspectives for subsequent research and clinical treatment.

N6-Methyladenosine RNA Modification: An Emerging Immunotherapeutic Approach to Turning Up Cold Tumors

Immunotherapy is a novel clinical approach that has shown clinical efficacy in multiple cancers. However, only a fraction of patients respond well to immunotherapy. Immuno-oncological studies have identified the type of tumors that are sensitive to immunotherapy, the so-called hot tumors, while unresponsive tumors, known as “cold tumors,” have the potential to turn into hot ones. Therefore, the mechanisms underlying cold tumor formation must be elucidated, and efforts should be made to turn cold tumors into hot tumors. N⁶-methyladenosine (m⁶A) RNA modification affects the maturation and function of immune cells by controlling mRNA immunogenicity and innate immune components in the tumor microenvironment (TME), suggesting its predominant role in the development of tumors and its potential use as a target to improve cancer immunotherapy. In this review, we first describe the TME, cold and hot tumors, and m⁶A RNA modification. Then, we focus on the role of m⁶A RNA modification in cold tumor formation and regulation. Finally, we discuss the potential clinical implications and immunotherapeutic approaches of m⁶A RNA modification in cancer patients. In conclusion, m⁶A RNA modification is involved in cold tumor formation by regulating immunity, tumor-cell-intrinsic pathways, soluble inhibitory mediators in the TME, increasing metabolic competition, and affecting the tumor mutational burden. Furthermore, m⁶A RNA modification regulators may potentially be used as diagnostic and prognostic biomarkers for different types of cancer. In addition, targeting m⁶A RNA modification may sensitize cancers to immunotherapy, making it a promising immunotherapeutic approach for turning cold tumors into hot ones.

tRNA-derived small RNAs: novel regulators of cancer hallmarks and targets of clinical application

tRNAs are a group of conventional noncoding RNAs (ncRNAs) with critical roles in the biological synthesis of proteins. Recently, tRNA-derived small RNAs (tsRNAs) were found to have important biological functions in the development of human diseases including carcinomas, rather than just being considered pure degradation material. tsRNAs not only are abnormally expressed in the cancer tissues and serum of cancer patients, but also have been suggested to regulate various vital cancer hallmarks. On the other hand, the application of tsRNAs as biomarkers and therapeutic targets is promising. In this review, we focused on the basic characteristics of tsRNAs, and their biological functions known thus far, and explored the regulatory roles of tsRNAs in cancer hallmarks including proliferation, apoptosis, metastasis, tumor microenvironment, drug resistance, cancer stem cell phenotype, and cancer cell metabolism. In addition, we also discussed the research progress on the application of tsRNAs as tumor biomarkers and therapeutic targets.

Identification of subtypes correlated with tumor immunity and immunotherapy in cutaneous melanoma

Because immune checkpoint inhibitors (ICIs) are effective for a subset of melanoma patients, identification of melanoma subtypes responsive to ICIs is crucial. We performed clustering analyses to identify immune subtypes of melanoma based on the enrichment levels of 28 immune cells using transcriptome datasets for six melanoma cohorts, including four cohorts not treated with ICIs and two cohorts treated with ICIs. We identified three immune subtypes (Im-H, Im-M, and Im-L), reproducible in these cohorts. Im-H displayed strong immune signatures, low stemness and proliferation potential, genomic stability, high immunotherapy response rate, and favorable prognosis. Im-L showed weak immune signatures, high stemness and proliferation potential, genomic instability, low immunotherapy response rate, and unfavorable prognosis. The pathways highly enriched in Im-H included immune, MAPK, apoptosis, calcium, VEGF, cell adhesion molecules, focal adhesion, gap junction, and PPAR. The pathways highly enriched in Im-L included Hippo, cell cycle, and ErbB. Copy number alterations correlated inversely with immune signatures in melanoma, while tumor mutation burden showed no significant correlation. The molecular features correlated with favorable immunotherapy response included immune-promoting signatures and pathways of PPAR, MAPK, VEGF, calcium, and glycolysis/gluconeogenesis. Our data recapture the immunological heterogeneity in melanoma and provide clinical implications for the immunotherapy of melanoma.

Subtyping of head and neck squamous cell cancers based on immune signatures

Although head and neck squamous cell cancer (HNSCC) is one of the cancer types in which immune checkpoint inhibitors (ICIs) has achieved a certain success, only a subset of HNSCC patients respond to ICIs. Thus, identification of HNSCC subtypes responsive to ICIs is crucial. Using hierarchical clustering, we identified three subtypes of HNSCC, termed Immunity-H, Immunity-M, and Immunity-L, based on the enrichment scores of 28 immune cells generated by the single-sample gene-set enrichment analysis of transcriptome data. We demonstrated that this subtyping method was stable and producible in four different HNSCC cohorts. Immunity-H had the highest levels of immune infiltrates and PD-L1 expression, lowest levels of stemness, intratumor heterogeneity and genomic instability, and favorable prognosis. In contrast, Immunity-L had the lowest levels of immune infiltrates and PD-L1 expression, highest levels of stemness, intratumor heterogeneity and genomic instability, and unfavorable prognosis. We found that somatic copy number alteration had a significant negative association with anti-tumor immunity in HNSCC, while tumor mutation burden showed no significant association. TP53, COL11A1, NSD1, and PKHD1L1 were more frequently mutated in Immunity-H versus Immunity-L, and their mutations were associated with increased immune signatures in HNSCC. Besides immune-related pathways, many stromal and oncogenic pathways were highly enriched in Immunity-H, including cell adhesion molecules, focal adhesion, ECM-receptor interaction, calcium signaling, MAPK signaling, apoptosis, VEGF signaling, and PPAR signaling. The high levels of PD-L1 expression and immune infiltration in Immunity-H indicate that this subtype responds best to ICIs. Our study recaptures the immunological heterogeneity in HNSCC and provide clinical implications for the immunotherapy of HNSCC.

A Signature-Based Classification of Gastric Cancer That Stratifies Tumor Immunity and Predicts Responses to PD-1 Inhibitors

Gastric cancer is a leading cause of cancer-related deaths with considerable heterogeneity among patients. Appropriate classifications are essential for prognosis prediction and individualized treatment. Although immunotherapy showed potential efficacy in a portion of patients with gastric cancer, few studies have tried to classify gastric cancer specifically based on immune signatures. In this study, we established a 3-subtype cluster with low (C_LIM), medium (C_MIM), and high (C_HIM) enrichment of immune signatures based on immunogenomic profiling. We validated the classification in multiple independent datasets. The C_HIM subtype exhibited a relatively better prognosis and showed features of “hot tumors”, including low tumor purity, high stromal components, overexpression of immune checkpoint molecules, and enriched tumor-infiltrated immune cells (activated T cells and macrophages). In addition, C_HIM tumors were also characterized by frequent ARID1A mutation, rare TP53 mutation, hypermethylation status, and altered protein expression (HER2, β-catenin, Cyclin E1, PREX1, LCK, PD-L1, Transglutaminase, and cleaved Caspase 7). By Gene Set Variation Analysis, “TGFβ signaling pathway” and “GAP junction” were enriched in C_LIM tumors and inversely correlated with CD8⁺ and CD4⁺ T cell infiltration. Of note, the C_HIM patients showed a higher response rate to immunotherapy (44.4% vs. 11.1% and 16.7%) and a more prolonged progression-free survival (4.83 vs. 1.86 and 2.75 months) than C_MIM and C_LIM patients in a microsatellite-independent manner. In conclusion, the new immune signature-based subtypes have potential therapeutic and prognostic implications for gastric cancer management, especially immunotherapy.

Identification of gastric cancer subtypes based on pathway clustering

Gastric cancer (GC) is highly heterogeneous in the stromal and immune microenvironment, genome instability (GI), and oncogenic signatures. However, a classification of GC by combining these features remains lacking. Using the consensus clustering algorithm, we clustered GCs based on the activities of 15 pathways associated with immune, DNA repair, oncogenic, and stromal signatures in three GC datasets. We identified three GC subtypes: immunity-deprived (ImD), stroma-enriched (StE), and immunity-enriched (ImE). ImD showed low immune infiltration, high DNA damage repair activity, high tumor aneuploidy level, high intratumor heterogeneity (ITH), and frequent TP53 mutations. StE displayed high stromal signatures, low DNA damage repair activity, genomic stability, low ITH, and poor prognosis. ImE had strong immune infiltration, high DNA damage repair activity, high tumor mutation burden, prevalence of microsatellite instability, frequent ARID1A mutations, elevated PD-L1 expression, and favorable prognosis. Based on the expression levels of four genes (TAP2, SERPINB5, LTBP1, and LAMC1) in immune, DNA repair, oncogenic, and stromal pathways, we developed a prognostic model (IDOScore). The IDOScore was an adverse prognostic factor and correlated inversely with immunotherapy response in cancer. Our identification of new GC subtypes provides novel insights into tumor biology and has potential clinical implications for the management of GCs.

Importance of N6-methyladenosine RNA modification in lung cancer (Review)

The N6-methyladenosine (m6A) modification is the most common mRNA modification in eukaryotes and exerts biological functions by affecting RNA metabolism. The m6A modification is installed by m6A methyltransferases, removed by demethylases and recognized by m6A-binding proteins. The interaction between these three elements maintains the dynamic equilibrium of m6A in cells. Accumulating evidence indicates that m6A RNA methylation has a significant impact on RNA metabolism and is involved in the pathogenesis of cancer. Lung cancer is the leading cause of cancer-related deaths worldwide. The treatment options for lung cancer have developed considerably over the past few years; however, the survival rate of patients with lung cancer still remains very low. Although diagnostic methods and targeted therapies have been rapidly developed in recent years, the underlying mechanism and importance of m6A RNA methylation in the pathogenesis of lung cancer remains ambiguous. The current review summarized the biological functions of m6A modification and considers the potential roles of m6A regulators in the occurrence and development of lung cancer.

Identification of m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression and Kaplan–Meier (K–M) analysis were performed to identify m6A methyltransferase-related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes (BPs) and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. The present study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Background

CD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG).

Methods

The prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways.

Results

Pan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG.

Conclusions

Integrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.

Identification of COVID-19 subtypes based on immunogenomic profiling

Although previous studies have shown that the host immune response is crucial in determining clinical outcomes in COVID-19 patients, the association between host immune signatures and COVID-19 patient outcomes remains unclear. Based on the enrichment levels of 11 immune signatures (eight immune-inciting and three immune-inhibiting signatures) in leukocytes of 100 COVID-19 patients, we identified three COVID-19 subtypes: Im-C1, Im-C2, and Im-C3, by clustering analysis. Im-C1 had the lowest immune-inciting signatures and high immune-inhibiting signatures. Im-C2 had medium immune-inciting signatures and high immune-inhibiting signatures. Im-C3 had the highest immune-inciting signatures while the lowest immune-inhibiting signatures. Im-C3 and Im-C1 displayed the best and worst clinical outcomes, respectively, suggesting that antiviral immune responses alleviated the severity of COVID-19 patients. We further demonstrated that the adaptive immune response had a stronger impact on COVID-19 outcomes than the innate immune response. The patients in Im-C3 were younger than those in Im-C1, indicating that younger persons have stronger antiviral immune responses than older persons. Nevertheless, we did not observe a significant association between sex and immune responses in COVID-19 patients. In addition, we found that the type II IFN response signature was an adverse prognostic factor for COVID-19. Our identification of COVID-19 immune subtypes has potential clinical implications for the management of COVID-19 patients.

A five-m6A regulatory gene signature is a prognostic biomarker in lung adenocarcinoma patients

We analyzed the prognostic value of N6-methyladenosine (m6A) regulatory genes in lung adenocarcinoma (LADC) and their association with tumor immunity and immunotherapy response. Seventeen of 20 m6A regulatory genes were differentially expressed in LDAC tissue samples from the TCGA and GEO databases. We developed a five-m6A regulatory gene prognostic signature based on univariate and Lasso Cox regression analysis. Western blot analysis confirmed that the five prognostic m6A regulatory proteins were highly expressed in LADC tissues. We constructed a nomogram with five-m6A regulatory gene prognostic risk signature and AJCC stages. ROC curves and calibration curves showed that the nomogram was well calibrated and accurately distinguished high-risk and low-risk LADC patients. Weighted gene co-expression analysis showed significant correlation between prognostic risk signature genes and the turquoise module enriched with cell cycle genes. The high-risk LADC patients showed significantly higher PD-L1 levels, increased tumor mutational burden, and a lower proportion of CD8⁺ T cells in the tumor tissues and improved response to immune checkpoint blockade therapy. These findings show that this five-m6A regulatory gene signature is a prognostic biomarker in LADC and that immune checkpoint blockade is a potential therapeutic option for high-risk LADC patients.

A Prognostic Signature Based on Immunogenomic Profiling Offers Guidance for Esophageal Squamous Cell Cancer Treatment

Our study aimed to develop an immune prognostic signature that could provide accurate guidance for the treatment of esophageal squamous cell cancer (ESCC). By implementing Single-Sample Gene Set Enrichment Analysis (ssGSEA), we established two ESCC subtypes (Immunity High and Immunity Low) in GSE53625 based on immune-genomic profiling of twenty-nine immune signature. We verified the reliability and reproducibility of this classification in the TCGA database. Immunity High could respond optimally to immunotherapy due to higher expression of immune checkpoints, including PD1, PDL1, CTLA4, and CD80. We used WGCNA analysis to explore the underlying regulatory mechanism of the Immunity High group. We further identified differentially expressed immune-related genes (CCR5, TSPAN2) in GSE53625 and constructed an independent two-gene prognostic signature we internally validated through calibration plots. We established that high-risk ESCC patients had worse overall survival (P=0.002, HR=2.03). Besides, high-risk ESCC patients had elevated levels of infiltrating follicle-helper T cells, naïve B cells, and macrophages as well as had overexpressed levels of some immune checkpoints, including B3H7, CTLA4, CD83, OX40L, and GEM. Moreover, through analyzing the Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk group demonstrated drug resistance to some chemotherapy and targeted drugs such as paclitaxel, gefitinib, erlotinib, and lapatinib. Furthermore, we established a robust nomogram model to predict the clinical outcome in ESCC patients. Altogether, our proposed immune prognostic signature constitutes a clinically potential biomarker that will aid in evaluating ESCC outcomes and promote personalized treatment.

Exonuclease 1 (EXO1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

Background

Exonuclease 1 (EXO1) has been identified to be highly expressed in different human malignancies, but its expression and prognostic role in lung adenocarcinoma (LUAD) remain unknown.

Materials and Methods

Two independent cohorts extracted from public databases and one cohort from our center were analyzed in this study. Expression levels of EXO1 in LUAD tissues and paired para-cancer tissues were detected. The prognostic value of EXO1 in LUAD patients was evaluated in the three cohorts. Enrichment analyses were performed to explore the possible underlying biological pathways. Moreover, we also explored the correlations between EXO1 and tumor-infiltrating immune cells and evaluated the impact of EXO1 knock-down on the migration of lung cancer cells.

Results

In this study, we found that EXO1 was highly expressed in LUAD tissues compared with para-cancerous tissues in public databases (p < 0.01), which was consistent with our data (p < 0.01). Survival analysis indicated that high expression of EXO1 was associated with poor prognosis in LUAD (p < 0.01). Enrichment analyses indicated that biological pathways like cell cycle regulation, DNA damage and repair, immune response, neuroactive ligand-receptor interaction, may be associated with EXO1 aberrant expression. Moreover, high expression of EXO1 was correlated with decreased infiltrating B cells (p < 0.01) and CD4+ T cells (p < 0.01) levels, and low infiltrating levels of B cells (p < 0.01) and dendritic cells (DCs) (p < 0.05) indicated poor overall survival (OS) in LUAD. Additionally, in vitro experiments suggested that knockdown of EXO1 may inhibit the migratory ability of lung cancer cells.

Conclusion

In conclusion, EXO1 is a potential prognostic biomarker in LUAD, and correlates with infiltrating levels of immune cells in the tumor microenvironment. Further prospective validation of EXO1 in lung cancer is warranted.

CD155-Prognostic and Immunotherapeutic Implications Based on Multiple Analyses of Databases Across 33 Human Cancers

Growing evidence has suggested that CD155 participates in the regulation of many biological processes ranging cell growth, invasion, and migration from regulation of immune responses in most malignances. However, the impact of prognostic value and CD115-related immune response on the survival in multiple cancers remains incompletely clear. In our study, we assessed the prognostic significance and immune-associated mechanism of CD155 based on data from multiple databases and methods, including UCSC Xena, Oncomine, PrognoScan. We identified that CD155 was commonly upregulated in most human cancers, and High expression of CD155 was closely correlated with unfavorable clinical outcomes in 10/33 of human cancers, while CD155 at low level was responsible for better survival in KICH and PAAD. More intriguingly, CD155 expression had a significant interaction with immune function in several tumors by analyzing Tumor mutational burden and microsatellite in stability, immune score and stromal score. The correlation between immune infiltration and CD155 expression also indicated that CD155 expression positively correlated with CD4+ T cells in Head and Neck squamous cell carcinoma, Lung adenocarcinoma and Colon adenocarcinoma, while had inversely interaction with CD8+ T in Kidney renal clear cell carcinoma and Head and Neck squamous cell carcinoma as well as Tregs in Skin Cutaneous Melanoma, Head and Neck squamous cell carcinoma and Bladder Urothelial Carcinoma. These findings indicate CD155 correlates with cancer immunotherapy function. In conclusions, our observations revealed CD155 might function as immune-associated system in the development of human cancers, and acted as a promising prognostic and therapeutic target against human cancers.

Cisplatin loaded multiwalled carbon nanotubes reverse drug resistance in NSCLC by inhibiting EMT

Background

Lung cancer is one of the important health threats worldwide, of which 5-year survival rate is less than 15%. Non-small-cell lung cancer (NSCLC) accounts for about 80% of all lung cancer with high metastasis and mortality.

Methods

Cisplatin loaded multiwalled carbon nanotubes (Pt-MWNTS) were synthesized and used to evaluate the anticancer effect in our study. The NSCLC cell lines A549 (cisplatin sensitive) and A549/DDP (cisplatin resistant) were used in our in vitro assays. MTT was used to determine Cancer cells viability and invasion were measured by MTT assay and Transwell assay, respectively. Apoptosis and epithelial-mesenchymal transition related marker proteins were measured by western blot. The in vivo anti-cancer effect of Pt-MWNTs were performed in male BALB/c nude mice (4-week old).

Results

Pt-MWNTS were synthesized and characterized by X-ray diffraction, Raman, FT-IR spectroscopy and scan electron microscopy. No significant cytotoxicity of MWNTS was detected in both A549/DDP and A549 cell lines. However, Pt-MWNTS showed a stronger inhibition effect on cell growth than free cisplatin, especially on A549/DDP. We found Pt-MWNTS showed higher intracellular accumulation of cisplatin in A549/DDP cells than free cisplatin and resulted in enhanced the percent of apoptotic cells. Western blot showed that application of Pt-MWNTS can significantly upregulate the expression level of Bax, Bim, Bid, Caspase-3 and Caspase-9 while downregulate the expression level of Bcl-2, compared with free cisplatin. Moreover, the expression level of mesenchymal markers like Vimentin and N-cadherin was more efficiently reduced by Pt-MWNTS treatment in A549/DDP cells than free cisplatin. In vivo study in nude mice proved that Pt-MWNTS more effectively inhibited tumorigenesis compared with cisplatin, although both of them had no significant effect on body weight.

Conclusion

Pt-MWNT reverses the drug resistance in the A549/DDP cell line, underlying its possibility of treating NSCLC with cisplatin resistance.

Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression

Non-small-cell lung cancer (NSCLC) is one of the most devastating diseases worldwide. The study is aimed at identifying reliable prognostic biomarkers and to improve understanding of cancer initiation and progression mechanisms. RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Subsequently, comprehensive bioinformatics analysis incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the protein-protein interaction (PPI) network was conducted to identify differentially expressed genes (DEGs) closely associated with NSCLC. Eight hub genes were screened out using Molecular Complex Detection (MCODE) and cytoHubba. The prognostic and diagnostic values of the hub genes were further confirmed by survival analysis and receiver operating characteristic (ROC) curve analysis. Hub genes were validated by other datasets, such as the Oncomine, Human Protein Atlas, and cBioPortal databases. Ultimately, logistic regression analysis was conducted to evaluate the diagnostic potential of the two identified biomarkers. Screening removed 1,411 DEGs, including 1,362 upregulated and 49 downregulated genes. Pathway enrichment analysis of the DEGs examined the Ras signaling pathway, alcoholism, and other factors. Ultimately, eight prioritized genes (GNGT1, GNG4, NMU, GCG, TAC1, GAST, GCGR1, and NPSR1) were identified as hub genes. High hub gene expression was significantly associated with worse overall survival in patients with NSCLC. The ROC curves showed that these hub genes had diagnostic value. The mRNA expressions of GNGT1 and NMU were low in the Oncomine database. Their protein expressions and genetic alterations were also revealed. Finally, logistic regression analysis indicated that combining the two biomarkers substantially improved the ability to discriminate NSCLC. GNGT1 and NMU identified in the current study may empower further discovery of the molecular mechanisms underlying NSCLC's initiation and progression.

Glycolysis-Based Genes Are Potential Biomarkers in Thyroid Cancer

While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid cancer (THCA), the prognostic role of it remains to be further elucidated. Here we aimed to determine a specific glycolysis-associated risk model to predict THCA patients' survival. We also explored the interaction between this signature and tumor immune microenvironment and performed drug screening to identify specific drugs targeting the glycolysis-associated signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, and FBP2) comprised the specific model, which was an independent prognostic indicator in THCA patients determined by univariate, LASSO and multivariate Cox regression analyses. The receiver operating characteristic (ROC) curve analysis confirmed the excellent clinical performance of the prognostic signature. According to the specific gene signature, patients were categorized into high- and low-risk subgroups. The high-risk group was characterized by decreased immune score and elevated tumor purity, as well as worser survival prognosis compared to the low-risk group. We also validated the expression of these genes in clinical samples and in-vitro experiments. Lastly, we identified potential drugs targeting the glycolysis-associated signature. The derived glycolysis-related signature is an independent prognostic biomarker for THCA patients and might be used as an efficacy of biomarker for drug-sensitivity prediction.

Immunological classification of gliomas based on immunogenomic profiling

Background

Gliomas are heterogeneous in the tumor immune microenvironment (TIM). However, a classification of gliomas based on immunogenomic profiling remains lacking.

Methods

We hierarchically clustered gliomas based on the enrichment levels of 28 immune cells in the TIM in five datasets and obtained three clusters: immunity-high, immunity-medium, and immunity-low.

Results

Glioblastomas were mainly distributed in immunity-high and immunity-medium, while lower-grade gliomas were distributed in all the three subtypes and predominated in immunity-low. Immunity-low displayed a better survival than other subtypes, indicating a negative correlation between immune infiltration and survival prognosis in gliomas. IDH mutations had a negative correlation with glioma immunity. Immunity-high had higher tumor stemness and epithelial-mesenchymal transition scores and included more high-grade tumors than immunity-low, suggesting that elevated immunity is associated with tumor progression in gliomas. Immunity-high had higher tumor mutation burden and more frequent somatic copy number alterations, suggesting a positive association between tumor immunity and genomic instability in gliomas.

Conclusions

The identification of immune-specific glioma subtypes has potential clinical implications for the immunotherapy of gliomas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-020-02030-w.

DNA methylation-based lung adenocarcinoma subtypes can predict prognosis, recurrence, and immunotherapeutic implications

The marked heterogeneity of lung adenocarcinoma (LUAD) makes its diagnosis and treatment difficult. In addition, the aberrant DNA methylation profile contributes to tumor heterogeneity and alters the immune response. We used DNA methylation array data from publicly available databases to establish a predictive model for LUAD prognosis. Thirty-three methylation sites were identified as specific prognostic biomarkers, independent of patients' clinical characteristics. These methylation profiles were used to identify potential drug candidates and study the immune microenvironment of LUAD and response to immunotherapy. When compared with the high-risk group, the low-risk group had a lower recurrence rate and favorable prognosis. The tumor microenvironment differed between the two groups as reflected by the higher number of resting dendritic cells and a lower number of monocytes and resting mast cells in the low-risk group. Moreover, low-risk patients reported higher immune and stromal scores, lower tumor purity, and higher expression of HLA genes. Low-risk patients responded well to immunotherapy due to higher expression of immune checkpoint molecules and lower stemness index. Thus, our model predicted a favorable prognosis and increased overall survival for patients in the low-risk methylation group. Further, this model could provide potential drug targets to develop effective immunotherapies for LUAD.

A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients

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We utilized multidimensional bioinformatics methods to validate the robustness of prognostic value for our 17 IRGPs signature and explored the relationship between the IRGPs model and immune status.

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The low-risk group demonstrated a distinctly favorable prognosis, including OS, DSS, and PFS.

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Through comparing PD-L1, MSI, and TIDE between high and low risk groups, low-risk group was much more sensitive to immunotherapy.

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Compared to the high-risk group, the low-risk group demonstrated a distinctly favourable prognosis, and the low-risk group was much more sensitive to immunotherapy.

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The IRGPs model is an independent biomarker for estimating the prognosis, and could be used to predict immunotherapeutic response in HNSCC patients .

Head and neck squamous cell carcinoma (HNSCC) is an invasive malignancy with high worldwide mortality. Growing evidence has indicated a pivotal correlation between HNSCC prognosis and immune signature. This study investigated an immune-related gene pairs (IRGPs) signature to predict the prognostic value of HNSCC patients. We constructed IRGPs via integrating multiple IRG expression data sets. Moreover, we established the predictive model base on the IRGPs for HNSCC, and utilized multidimensional bioinformatics methods to validate the robustness of prognostic value of the IRGPs signature. In addition, we explored the relationship between the IRGPs model and immune status. Seventeen IRGPs signature was built as the predictive model which predicted prognosis independently and reliably for HNSCC. Compared to the high-risk group, the low-risk group demonstrated a distinctly favorable prognosis including overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). The low-risk group showed higher-immune score and lower-tumor purity than the high-risk group. In addition, the low-risk group exhibited higher expression of Programmed cell death 1 ligand 1 (PD-L1) and Microsatellite instability (MSI) score, and lower expression of Tumor Immune Dysfunction and Exclusion (TIDE), which indicated the low-risk group was much more sensitive to immunotherapy. Lastly, the IRGs signature has achieved a higher accuracy than clinical properties for estimation of survival. The IRGPs model is an independent biomarker for estimating the prognosis, and could be also used to predict immunotherapeutic response in HNSCC patients. These findings may provide new ideas for novel biomarkers and may be helpful to formulate personalized immunotherapy strategy.

A signature of immune-related gene pairs predicts oncologic outcomes and response to immunotherapy in lung adenocarcinoma

In this study, we established the predictive model for lung adenocarcinoma (LUAD) depending on immune-related gene pairs (IRGPs) signature, which could not consider the technical bias of different platforms. Furthermore, we explored the predictive model with regard to the immune microenvironment and response to immunotherapy and identified specific drugs targeting the IRGPs model. Twenty-three IRGPs were identified and comprised the predictive model. When compared with the high-risk group, the low-risk group displayed a distinctly favorable prognosis and was characterized by increased immune score and decreased tumor purity. In addition, the low-risk group exhibited higher expression of immune checkpoint molecules, lower tumor stemness index, and was much more sensitive to immunotherapy. Lastly, candidate drugs that aimed at LUAD subtype differentiation were identified. The derived IRGPs model is an adverse independent biomarker for estimating oncologic outcomes in LUAD patients, and may be helpful to formulate personalized immunotherapy strategy.

Prognosis Analysis and Validation of m6A Signature and Tumor Immune Microenvironment in Glioma

Glioma is one of the most typical intracranial tumors, comprising about 80% of all brain malignancies. Several key molecular signatures have emerged as prognostic biomarkers, which indicate room for improvement in the current approach to glioma classification. In order to construct a more veracious prediction model and identify the potential prognosis-biomarker, we explore the differential expressed m⁶A RNA methylation regulators in 665 gliomas from TCGA-GBM and TCGA-LGG. Consensus clustering was applied to the m6A RNA methylation regulators, and two glioma subgroups were identified with a poorer prognosis and a higher grade of WHO classification in cluster 1. The further chi-squared test indicated that the immune infiltration was significantly enriched in cluster 1, indicating a close relation between m⁶A regulators and immune infiltration. In order to explore the potential biomarkers, the weighted gene co-expression network analysis (WGCNA), along with Least absolute shrinkage and selection operator (LASSO), between high/low immune infiltration and m⁶A cluster 1/2 groups were utilized for the hub genes, and four genes (TAGLN2, PDPN, TIMP1, EMP3) were identified as prognostic biomarkers. Besides, a prognostic model was constructed based on the four genes with a good prediction and applicability for the overall survival (OS) of glioma patients (the area under the curve of ROC achieved 0.80 (0.76-0.83) and 0.72 (0.68-0.76) in TCGA and Chinese Glioma Genome Atlas (CGGA), respectively). Moreover, we also found PDPN and TIMP1 were highly expressed in high-grade glioma from The Human Protein Atlas database and both of them were correlated with m6A and immune cell marker in glioma tissue samples. In conclusion, we construct a novel prognostic model which provides new insights into glioma prognosis. The PDPN and TIMP1 may serve as potential biomarkers for prognosis of glioma.