Systematic profiling of immune signatures identifies prognostic predictors in lung adenocarcinoma

Tumor-infiltrating macrophage associated lncRNA signature in cutaneous melanoma: implications for diagnosis, prognosis, and immunotherapy

Along with the increasing knowledge of long noncoding RNA, the interaction between the long noncoding RNA (lncRNA) and tumor immune infiltration is increasingly valued. However, there is a lack of understanding of correlation between regulation of specific lncRNAs and tumor-infiltrating macrophages within melanoma. In this research, a macrophage associated lncRNA signature was identified by multiple machine learning algorithms and the robust and effectiveness of signature also validated in other independent datasets. The signature contained six specific lncRNAs (PART1, LINC00968, LINC00954, LINC00944, LINC00518 and C20orf197) was constructed, which could diagnose melanoma and predict the prognosis of patients. Moreover, our signature achieves higher accuracy than the previous well-established markers and regarded as an independent prognostic indicator. The pathway enrichment revealed that these lncRNAs were closely correlated with many immune processes. In addition, the signature was associated with different immune microenvironment and applied to predict response of immune checkpoint inhibitor therapy (low risk of patients well respond to anti-PD-1 therapy and high risk is insensitive to anti-CTLA-4 therapy). Therefore, our finding supplies a more accuracy and effective lncRNA signature for tumor-infiltrating macrophages targeting treatment approaches and affords a new clinical application for predicting the response of immunotherapies in melanomas.

circ_0004140 promotes LUAD tumor progression and immune resistance through circ_0004140/miR-1184/CCL22 axis

Lung adenocarcinoma (LUAD) is a highly prevalent cancer with high mortality. Immune resistance and tumor metastasis are the pivotal factors for the promotion of LUAD. CircRNAs have been revealed a crucial pre-clinical diagnostic and therapeutic potentials in LUAD. Herein, we identify a novel circRNA (circ_0004140), derived from the oncogene YAP1, which is up-regulated in LUAD. The high expression of circ_0004140 is correlated with poor prognosis and CTL cells dysfunction in LUAD patients. Knockdown of circ_0004140 regulated LUAD cells proliferation, migration, and apoptosis. Mechanistically, circ_0004140 served as a sponge of miR-1184 targeting C-C motif chemokine ligand 22(CCL22). Overexpression of CCL22 reversed the inhibitory effect induced by si-circ_0004140 on cells proliferation and migration. Moreover, we also revealed that elevated circ_ooo4140 was related to cytotoxic lymphocyte exhaustion, and a combination therapy of C-021 (CCL22/CCR4 axis inhibitor) and anti-PD-1 attenuated LUAD promotion and immune resistance. In conclusion, circ_0004140 may drive resistance to anti-PD-1 immunotherapy, providing a novel potential therapeutic target for LUAD treatment.

Identification of MAP3K15 as a potential prognostic biomarker and correlation with immune infiltrates in osteosarcoma

Background

Osteosarcoma (OS) is a type of primary malignant tumor, and increasing evidence shows the clinical benefits of immunotherapy in treating OS. However, the lack of comprehensive studies on the complex OS immune microenvironment hinders the application of immunotherapy. Thus, this study aimed to systematically explore the immune characteristics of OS and identify novel biomarkers for OS treatment.

Methods

We systematically studied the immune score and proportions of infiltrating immune cells in OS in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases using the ESTIMATE and CIBERSORT algorithms. Differential expression and functional analyses were used to identify dysregulated genes and explore their functions. Survival and Cox regression analyses were applied to establish an immune-related prognostic signature. Additionally, qPCR and immunohistochemistry were performed to validate the results.

Results

A total of 103 differentially expressed immune genes (DEIGs) were found in the TARGET-OS and GSE39058 databases, and these DEIGs were mainly enriched in leukocyte proliferation, leukocyte differentiation, osteoclast differentiation, natural killer (NK) cell-mediated cytotoxicity, and the adaptive immune system. A predictive signature was constructed based on the survival analysis, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.65. Moreover, we found that mitogen-activated protein kinase kinase kinase 15 (MAP3K15) can predict the prognosis of patients with OS and is closely related to CD4⁺ T cells and macrophages. The OS patients with high MAP3K15 expression had a significantly poorer prognosis.

Conclusions

Our study found that MAP3K15, whose expression level is closely related to immune activity in tumors, is a critical immune-related biomarker, and our findings may provide a basis for OS immunotherapy.

Identification of Tumor Microenvironment-Related Prognostic lncRNAs in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is a severe threat to human health. Although many therapies have been applied to LUAD, the long-term survival rate of patients remains unsatisfactory. We aim to find reliable immune microenvironment-related lncRNA biomarkers to improve LUAD prognosis.

Methods

ESTIMATE analysis was performed to evaluate the degree of immune infiltration of each patient in TAGA LUAD cohort. Correlation analysis was used to identify the immune microenvironment-related lncRNAs. Univariate cox regression analysis, LASSO analysis, and Kaplan Meier analysis were used to construct and validate the prognostic model based on microenvironment-related lncRNAs.

Results

We obtained 1,178 immune microenvironment-related lncRNAs after correlation analysis. One hundred and eighty of them are independent prognostic lncRNAs. Sixteen key lncRNAs were selected by LASSO method. This lncRNA-based model successfully predicted patients’ prognosis in validation cohort, and the risk score was related to pathological stage. Besides, we also found that TP53 had the highest frequency mutation in LUAD, and the mutation of TP53 in the high-risk group, which was identified by our survival model, has a poor prognosis. lncRNA-mRNA co-expression network further suggested that these lncRNAs play a vital role in the prognosis of LUAD.

Conclusion

Here, we filtered 16 key lncRNAs, which could predict the survival of LUAD and may be potential biomarkers and therapeutic targets.

Development and external validation of a composite immune-clinical prognostic model associated with EGFR mutation in East-Asian patients with lung adenocarcinoma

BACKGROUND

EGFR mutation is a common oncogene driver in East Asians with lung adenocarcinoma (LUAD), conferring a favorable prognosis with effective targeted therapy. However, the EGFR mutation is a weak predictor of long-term survival. Therefore, a powerful predictive tool is urgently needed to estimate disease prognosis and patient survival for East-Asian patients with LUAD.

METHODS

In this first systematic analysis of the relationships among EGFR mutation, immunophenotype, and prognosis in LUAD samples from East-Asian patients, we constructed a prognostic signature consisting of EGFR-associated immune-related gene pairs (EIGPs). The predictive performance for overall survival (OS) and the clinical significance of this signature were then comprehensively investigated.

RESULTS

Based on transcriptome data analysis of a training set, we proposed the EIGP index (EIGPI), represented by five EIGPs, which was significantly associated with the OS of East-Asian patients with LUAD. It was also well validated in a test set. Furthermore, the prognostic performance of the EIGPI was further verified using protein levels in an additional independent set. Stratification analysis and multivariate Cox regression analysis revealed that the EIGPI was an independent prognostic factor. When combined with stage, the composite immune-clinical prognostic model index (ICPMI) showed improved prognostic accuracy in all datasets.

CONCLUSION

This study was the first to systematically investigate the relationships among EGFR mutation, immunophenotype, and prognosis in East Asians with LUAD and develop a composite clinical and immune model associated with EGFR mutation. This model may be a reliable and promising prognostic tool and help further personalize patient management.

Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes

Background

The acquisition of invasive tumor cell behavior is considered to be the cornerstone of the metastasis cascade. Thus, genetic markers associated with invasiveness can be stratified according to patient prognosis. In this study, we aimed to identify an invasive genetic trait and study its biological relevance in lung adenocarcinoma.

Methods

250 TCGA patients with lung adenocarcinoma were used as the training set, and the remaining 250 TCGA patients, 500 ALL TCGA patients, 226 patients with GSE31210, 83 patients with GSE30219, and 127 patients with GSE50081 were used as the verification data sets. Subtype classification of all TCGA lung adenocarcinoma samples was based on invasion-associated genes using the R package ConsensusClusterPlus. Kaplan-Meier curves, LASSO (least absolute contraction and selection operator) method, and univariate and multivariate Cox analysis were used to develop a molecular model for predicting survival.

Results

As a consequence, two molecular subtypes for LUAD were first identified from all TCGA all data sets which were significant on survival time. C1 subtype with poor prognosis has higher clinical characteristics of malignancy, higher mutation frequency of KRAS and TP53, and a lower expression of immune regulatory molecules. 2463 differentially expressed invasion genes between C1 and C2 subtypes were obtained, including 580 upregulation genes and 1883 downregulation genes. Functional enrichment analysis found that upregulated genes were associated with the development of tumor pathways, while downregulated genes were more associated with immunity. Furthermore, 5-invasion gene signature was constructed based on 2463 genes, which was validated in four data sets. This signature divided patients into high-risk and low-risk groups, and the LUDA survival rate of the high-risk group is significantly lower than that of the low-risk group. Multivariate Cox analysis revealed that this gene signature was an independent prognostic factor for LUDA. Compared with other existing models, our model has a higher AUC.

Conclusion

In this study, two subtypes were identified. In addition, we developed a 5-gene signature prognostic risk model, which has a good AUC in the training set and independent validation set and is a model with independent clinical characteristics. Therefore, we recommend using this classifier as a molecular diagnostic test to assess the prognostic risk of patients with LUDA.

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Background

Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.

Method

RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).

Results

Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.

Conclusion

In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.