Long Non-Coding RNAs in Lung Cancer: The Role in Tumor Microenvironment

Development of CD73 Inhibitors in Tumor Immunotherapy and Opportunities in Imaging and Combination Therapy

CD73 is a member of the membrane-bound enucleotidase family, which catalyzes the extracellular hydrolysis of adenosine monophosphate (AMP) to produce anti-inflammatory and immunosuppressive adenosine. As a novel checkpoint protein, CD73 is overexpressed in the immune system of various tumors, where adenosine is abundantly enriched. A large number of monoclonal antibodies (mAbs), nucleotides, and non-nucleotides as potent CD73 inhibitors are being discovered, providing opportunities for novel tumor immunotherapy. Currently, 18 CD73 inhibitors are in clinical trials, showing promising results in combination therapy for various solid tumors. The development of CD73-specific companion positron emission tomography imaging ligands holds potential for facilitating diagnosis, patient selection, and treatment efficacy evaluation throughout the entire process of CD73-targeted therapeutic development.

EFTUD2 is a promising diagnostic and prognostic indicator involved in the tumor immune microenvironment and glycolysis of lung adenocarcinoma

Background

Elongation Factor Tu GTP Binding Domain Containing 2 (EFTUD2), a conserved spliceosomal GTPase, is involved in craniofacial development and various cancers, but its role in lung adenocarcinoma (LUAD) remains unclear.

Methods

EFTUD2 expression in LUAD tissues was analyzed using data from TCGA and GEO, and validated by immunohistochemistry, RT-qPCR, and Western blotting. The relationship between EFTUD2 expression and clinical features was examined using Fisher's exact test. Diagnostic and prognostic analyses were performed in R. Hub genes related to EFTUD2 were identified through topological algorithms, and immune infiltration was assessed using CIBERSORT. The cGAS-STING pathway and m6A modification were also analyzed in the TCGA LUAD cohort. Functional assays were conducted to assess EFTUD2's impact on LUAD cell proliferation, cell cycle, invasion, and metastasis, while glycolytic enzyme levels were measured by Western blotting.

Results

EFTUD2 was upregulated in LUAD tissues and cells, correlating with N classification, visceral pleural invasion, intravascular tumor embolism, and cytokeratin-19 fragment antigen 21-1. Sixteen EFTUD2-related hub genes were identified. Higher EFTUD2 expression was linked to altered immune cell infiltration, with increased TumorPurity scores and decreased StromalScore, ImmuneScore, and ESTIMATEScore values. Gene enrichment analyses highlighted EFTUD2's involvement in cell adhesion, immune response. EFTUD2 was strongly associated with the cGAS-STING pathway and m6A modification. EFTUD2 knockdown inhibited LUAD cell proliferation, migration, and tumorigenicity, causing G0/G1 phase cell cycle arrest, and altered glycolytic enzyme expression. These findings may suggest that EFTUD2 positively regulates the progression of LUAD and modulates the glycolytic activity of tumor cells, making it valuable for LUAD treatment and prognosis.

Conclusions

EFTUD2 is a potential diagnostic and prognostic marker for LUAD, associated with immune infiltration, the tumor microenvironment, the cGAS-STING pathway, m6A modification, and glycolysis.

Deciphering the Interplay of the PD-L1/MALT1/miR-200a Axis During Lung Cancer Development

Lung cancer remains a leading cause of cancer-related mortality worldwide. Our study investigates the involvement of the PD-L1/MALAT1/miR-200a-3p axis in lung tumor progression using a murine model of lung carcinogenesis. Lung tumors were induced in rats, which were divided into groups and sacrificed at different stages of tumor development. A histopathological examination was performed to assess tumor progression. Immunohistochemistry was applied to evaluate the expression of Ki-67 and programmed death-ligand 1 (PD-L1). The level of carcinoembryonic antigen (CEA) and expression analysis of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), miR-200a-3p, and zinc finger E-box binding homeobox 1 (ZEB1) were evaluated for each stage of induction. Immunohistochemical analysis demonstrated a progressive upregulation of the proliferative marker Ki-67 and the immune checkpoint protein PD-L1 during the induction process, indicative of enhanced tumor proliferation and immune evasion. Additionally, CEA levels revealed a progressive increase across induction stages, with a significant increase in advanced tumor stages, highlighting its clinical relevance as a biomarker for lung cancer progression. Expression analysis revealed dynamic upregulation of MALAT1 and downregulation of miR-200a during lung tumor induction, which correlated with advanced tumor stages and elevated PD-L1 expression, suggesting that the negative correlation between MALAT1 and miR-200a is involved in the development of lung tumors. ZEB1 expression exhibited a notable increase in the advanced stages of induction, consistent with its association with aggressive lung cancer. Our findings underscore the interplay between molecular pathways involved in lung tumor development and the potential diagnostic and therapeutic implications of the PD-L1/MALAT1/miR-200a-3p axis.

Identification and validation of a novel prognosis model based on m5C-related long non-coding RNAs in colorectal cancer

BACKGROUND

The prognosis of tumor patients can be assessed by measuring the levels of lncRNAs (long non-coding RNAs), which play a role in controlling the methylation of the RNA. Prognosis in individuals with colorectal adenocarcinoma (CRC) is strongly linked to lncRNA expression, making it imperative to find lncRNAs that are associated with RNA methylation with strong prognostic value.

METHODS

In this study, by analyzing TCGA dataset, we were able to develop a risk model for lncRNAs that are associated with m5C with prognostic significance by employing LASSO regression and univariate Cox proportional analysis. There were a number of methods employed to ensure the model was accurate, including multivariate and univariate Cox regression analysis, Kaplan analysis, and receiver operating characteristic curve analysis. The principal component analysis, GSEA and GSVA analysis were used for risk model analysis. The CIBERSORT instrument and the TIMER database were used to evaluate the link between the immune cells that infiltrate tumors and the risk model. In vitro experiments were also performed to validate the predicted m5C-related significant lncRNAs.

RESULTS

The m5c regulators were differentially expressed in colorectal cancer and normal tissue. Based on the screening criteria and LASSO regression, 11 m5c-related lncRNAs were identified for developing the prognostic risk model. Multivariate and univariate Cox regression analysis showed the risk score is a crucial prognostic factor in CRC patients. The 1-year, 3-year, and 5-year AUC curves showed the risk score was higher than those identified for other clinicopathological characteristics. A nomogram using the risk score as a quantitative tool was developed for predicting patients' outcomes in clinical settings. In addition, the risk profile of m5C-associated lncRNAs can discriminate between tumor immune cells' characteristics in CRC. Mutation patterns and chemotherapy were analyzed between high- and low- risk groups of CRC patients. Moreover, TNFRSF10A-AS1 was chosen for the in vitro verification of the m5C-connected lncRNA to demonstrate impressive effects on the proliferation, migration and invasion of CRC cells.

CONCLUSION

A risk model including the prognostic value of 11 m5C-associated lncRNAs proves to be a useful prognostic tool for CRC and improves the care of patients suffering from CRC based on these findings.

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma

Background: Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear. Methods: RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan-Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model's accuracy. We examined this model's link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature. Results: An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis. Conclusion: In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature's prognostic power.

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.

Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.

Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.

Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Lung cancer is the common malignant tumor with the highest mortality rate. Lung cancer patients have achieved benefits from immunotherapy, including immune checkpoint inhibitors (ICIs) therapy. Unfortunately, cancer patients acquire adaptive immune resistance, leading to poor prognosis. Tumor microenvironment (TME) has been demonstrated to play a critical role in participating in acquired adaptive immune resistance. TME is associated with molecular heterogeneity of immunotherapy efficacy in lung cancer. In this article, we discuss how immune cell types of TME are correlated with immunotherapy in lung cancer. Moreover, we describe the efficacy of immunotherapy in driven gene mutations in lung cancer, including KRAS, TP53, EGFR, ALK, ROS1, KEAP1, ZFHX3, PTCH1, PAK7, UBE3A, TNF-α, NOTCH, LRP1B, FBXW7, and STK11. We also emphasize that modulation of immune cell types of TME could be a promising strategy for improving adaptive immune resistance in lung cancer.