Multi-omics analysis of an immune-based prognostic predictor in non-small cell lung cancer

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.

METHODS

Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.

RESULTS

By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.

CONCLUSION

In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering

We performed UCC on the expression data of lung squamous cell carcinoma tumor samples to identify the classification of lung squamous cell carcinoma (LUSC) tumor samples, and calculated the levels of different classified immune cells by single-sample gene enrichment analysis (ssGSEA) to obtain a set of immune-related subtype gene tags, which can be used for subtype classification of lung squamous cell carcinoma. TCGA-LUSC and GSE30219 data of lung squamous cell carcinoma were obtained from TCGA and GEO databases. Prognostic-associated subtypes were identified by unsupervised consensus clustering (UCC). Using ssGSEA analysis to calculate the level of immune cells of different subtypes, obtain the connection between subtypes and immunity, identify the gene signatures recognized by subtypes, and verify this group of gene signatures through GSE30219. We effectively identified 2 subtypes that were significantly associated with prognostic survival by UCC, and calculated according to ssGSEA, the 2 subtypes were significantly different at the level of immune cells, followed by introducing a This weighted thinking computes a set of gene signatures that are significantly associated with subtype 1. During validation, this set of gene signatures could efficiently and robustly identify distinct prognostic immune subtypes, demonstrated the validity of this set of gene signatures, as well as 2 subtypes of lung squamous cell carcinoma. We used lung squamous cell carcinoma data from public databases and identified 2 prognostic immunosubtypes of lung squamous cell carcinoma and a set of gene tags that can be used to classify immune subtypes of lung squamous cell carcinoma, which may provide effective evidence for accurate clinical treatment of lung squamous cell carcinoma.

Metabolic barriers in non-small cell lung cancer with LKB1 and/or KEAP1 mutations for immunotherapeutic strategies

Currently, immune checkpoint inhibitors (ICIs) are widely considered the standard initial treatment for advanced non-small cell lung cancer (NSCLC) when there are no targetable driver oncogenic alternations. NSCLC tumors that have two alterations in tumor suppressor genes, such as liver kinase B1 (LKB1) and/or Kelch-like ECH-associated protein 1 (KEAP1), have been found to exhibit reduced responsiveness to these therapeutic strategies, as revealed by multiomics analyses identifying immunosuppressed phenotypes. Recent advancements in various biological approaches have gradually unveiled the molecular mechanisms underlying intrinsic reprogrammed metabolism in tumor cells, which contribute to the evasion of immune responses by the tumor. Notably, metabolic alterations in glycolysis and glutaminolysis have a significant impact on tumor aggressiveness and the remodeling of the tumor microenvironment. Since glucose and glutamine are essential for the proliferation and activation of effector T cells, heightened consumption of these nutrients by tumor cells results in immunosuppression and resistance to ICI therapies. This review provides a comprehensive summary of the clinical efficacies of current therapeutic strategies against NSCLC harboring LKB1 and/or KEAP1 mutations, along with the metabolic alterations in glycolysis and glutaminolysis observed in these cancer cells. Furthermore, ongoing trials targeting these metabolic alterations are discussed as potential approaches to overcome the extremely poor prognosis associated with this type of cancer.

Comprehensive analysis of a novel cuproptosis-related lncRNA signature associated with prognosis and tumor matrix features to predict immunotherapy in soft tissue carcinoma

Background: A crucial part of the malignant processes of soft tissue sarcoma (STS) is played by cuproptosis and lncRNAs. However, the connection between cuproptosis-related lncRNAs (CRLs) and STS is nevertheless unclear. As a result, our objective was to look into the immunological activity, clinical significance, and predictive accuracy of CRLs in STS. Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively, provided information on the expression patterns of STS patients and the general population. Cuproptosis-related lncRNA signature (CRLncSig) construction involved the univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analysis. The predictive performance of the CRLncSig was evaluated using a serial analysis. Further research was done on the connections between the CRLncSig and the tumor immune milieu, somatic mutation, immunotherapy response, and chemotherapeutic drug susceptibility. Notably, an in vitro investigation served to finally validate the expression of the hallmark CRLs. Results: A novel efficient CRLncSig composed of seven CRLs was successfully constructed. Additionally, the low-CRLncSig group's prognosis was better than that of the high-CRLncSig group's based on the new CRLncSig. The innovative CRLncSig then demonstrated outstanding, consistent, and independent prognostic and predictive usefulness for patients with STS, according to the evaluation and validation data. The low-CRLncSig group's patients also displayed improved immunoreactivity phenotype, increased immune infiltration abundance and checkpoint expression, and superior immunotherapy response, whereas those in the high-CRLncSig group with worse immune status, increased tumor stemness, and higher collagen levels in the extracellular matrix. Additionally, there is a noticeable disparity in the sensitivity of widely used anti-cancer drugs amongst various populations. What's more, the nomogram constructed based on CRLncSig and clinical characteristics of patients also showed good predictive ability. Importantly, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) demonstrated that the signature CRLs exhibited a significantly differential expression level in STS cell lines. Conclusion: In summary, this study revealed the novel CRLncSig could be used as a promising predictor for prognosis prediction, immune activity, tumor immune microenvironment, immune response, and chemotherapeutic drug susceptibility in patients with STS. This may provide an important direction for the clinical decision-making and personalized therapy of STS.

Development of a Simple and Objective Prognostication Model for Patients with Advanced Solid Malignant Tumors Treated with Immune Checkpoint Inhibitors: A Pan-Cancer Analysis

BACKGROUND

Systemic therapy using immune checkpoint inhibitors (ICIs) has recently become prevalent in the treatment of patients with various types of advanced cancers; however, difficulties are still associated with predicting the outcomes of patients receiving ICIs due to heterogenous responses to these agents.

OBJECTIVE

To develop a prognostic model for advanced cancer patients treated with ICIs.

PATIENTS AND METHODS

This study retrospectively analyzed the impact of clinical parameters on overall survival (OS) in 329 patients with several advanced solid malignant tumors who received systemic therapy using ICIs.

RESULTS

The primary tumors of 329 patients were as follows: lung (n = 89), kidney (n = 70), urinary tract (n = 52), skin (n = 50), stomach (n = 30), esophagus (n = 21), and head and neck (n = 17). Median OS after the introduction of ICIs was 17.3 months. Among the factors that correlated with OS in a univariate analysis, body mass index, C-reactive protein, hemoglobin, lymphocytes, and platelets were identified as independent predictors of OS in a multivariate analysis. Following the classification of patients into 3 groups based on positive numbers of these independent risk factors, median OS was not reached in the favorable risk group with 0 or 1 risk factor (n = 76), 19.5 months in the intermediate-risk group with 2 or 3 risk factors (n = 182), and 7.2 months in the poor risk group (n = 71) with 4 or 5 risk factors.

CONCLUSIONS

Although this is a simple and objective model, it may be used as a reliable tool to predict the outcomes of advanced cancer patients receiving ICIs across multiple tumor types.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

Background

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80–85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

Methods

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan–Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

Results

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

Conclusion

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone

In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.