Immune-related eight-lncRNA signature for improving prognosis prediction of lung adenocarcinoma

FGF12: biology and function

Fibroblast growth factor 12 (FGF12) belongs to the fibroblast growth factor homologous factors (FHF) subfamily, which is also known as the FGF11 subfamily. The human FGF12 gene is located on chromosome 3 and consists of four introns and five coding exons. Their alternative splicing results in two FGF12 isoforms - the shorter 'b' isoform and the longer 'a' isoform. Structurally, the core domain of FGF12, is highly homologous to that of the other FGF proteins, providing the classical tertiary structure of β-trefoil. FGF12 is expressed in various tissues, most abundantly in excitable cells such as neurons and cardiomyocytes. For many years, FGF12 was thought to be exclusively an intracellular protein, but recent studies have shown that it can be secreted despite the absence of a canonical signal for secretion. The best-studied function of FGF12 relates to its interaction with sodium channels. In addition, FGF12 forms complexes with signaling proteins, regulates the cytoskeletal system, binds to the FGF receptors activating signaling cascades to prevent apoptosis and interacts with the ribosome biogenesis complex. Importantly, FGF12 has been linked to nervous system disorders, cancers and cardiac diseases such as epileptic encephalopathy, pulmonary hypertension and cardiac arrhythmias, making it a potential target for gene therapy as well as a therapeutic agent.

Regulation of overexpression lncRNA ATP2B1-AS1 on lung adenocarcinoma progression

BACKGROUND

LncRNA ATP2B1-AS1 (ATP2B1-AS1) is involved in the occurrence and development of various diseases, while the relationship between lung adenocarcinoma (LUAD) and ATP2B1-AS1 is unclear. This study was to investigate the expression of ATP2B1-AS1 in LUAD and its influence on survival and prognosis of patients.

METHODS

LUAD tissue samples from patients participating in this study were collected, and the expression levels of ATP2B1-AS1 and miR-141-3p in LUAD sampleswere detected by real-time quantitative polymerase chain reaction (RT-qPCR). The effect of ATP2B1-AS1 on the growth of A549 cells was investigated through cell counting kit-8 (CCK-8) and transwell experiments. Besides, the prognostic value of ATP2B1-AS1 in LUAD was assessed via Kaplan-Meier curve and multivariate Cox regression.

RESULTS

ATP2B1-AS1 was downregulated in LUAD tissues and cells, whereas miR-141-3p was upregulated. After pcDNA3.1-ATP2B1-AS1 was transfected into A549 cells, the proliferation ability of A549 cells was decreased, and the migration level and invasion of A549 cells were also inhibited. High expression of ATP2B1-AS1 sponge miR-141-3p exerted prognostic value.

CONCLUSIONS

ATP2B1-AS1 sponge miR-141-3p alleviated the progression of LUAD, and ATP2B1-AS1 may be deemed as a prognostic marker for LUAD.

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

Objective

To study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS.

Methods

We retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes.

Results

We obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores.

Conclusion

The study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

LncRNA SATB2-AS1 promotes tumor growth and metastasis and affects the tumor immune microenvironment in osteosarcoma by regulating SATB2

Our previous report has identified a lncRNA SATB2-AS1, which was significantly up-regulated in osteosarcoma tissue and promotes the proliferation of osteosarcoma cells in vitro. However, the mechanisms of SATB2-AS1 regulating the growth and metastasis of osteosarcoma cells in vivo and its role in the prognosis of osteosarcoma patients are still unclear. In this study, the transcriptome sequencing data of 87 patients with osteosarcoma from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and 7 patients from our clinical center (GZFPH) was used to evaluate the importance of SATB2-AS1 and SATB2 on the prognosis. The effect of SATB2-AS1 on the growth and metastasis of osteosarcoma cells in vivo was verified by a mouse tumor model. The potential mechanisms of SATB2-AS1 regulating SATB2 were further explored by dual-luciferase reporter gene assay, RNA pull-down assay, and bioinformatics analysis. The results suggested that increased co-expression of SATB2-AS1 and SATB2 was significantly associated with poor overall survival (OS) and relapse-free survival (RFS), and was a biomarker for risk stratification in patients with osteosarcoma. Mechanistically, SATB2-AS1 promotes tumor growth and lung metastasis by regulating SATB2 in vivo. SATB2-AS1 directly binds to POU3F1 for mediating SATB2 expression in MNNG/HOS cells. In addition, SATB2-AS1 and SATB2 might be potential immunomodulators for negatively affecting immune cell infiltration by the IL-17 signaling pathway. In summary, SATB2-AS1 promoted tumor cell growth and lung metastasis by activating SATB2, thereby associated with poor prognosis in patients with osteosarcoma, which indicated that SATB2-AS1 and SATB2 might be novel biomarkers for risk stratification and promising therapeutic targets for osteosarcoma.

Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma

PURPOSE

Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients.

METHODS

In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA.

RESULTS

Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001).

CONCLUSION

In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed. Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model. Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

lncRNA MANCR Inhibits NK Cell Killing Effect on Lung Adenocarcinoma by Targeting miRNA-30d-5p

Background. NK cells are imperative in spontaneous antitumor response of various cancers. Currently, lncRNAs are considered important modulators of the tumor microenvironment. This study investigated the molecular mechanism by which mitotically associated long noncoding RNA (MANCR) controls killing effect of NK cells on lung adenocarcinoma (LUAD) in the tumor microenvironment. Methods. The interplay between MANCR and miRNA-30d-5p was analyzed by bioinformatics. Expression of MANCR mRNA and miRNA-30d-5p was examined using qRT-PCR. Dual-luciferase reporter and RIP assays were utilized to verify the targeted relationship between MANCR and miRNA-30d-5p. To investigate regulation of MANCR/miRNA-30d-5p axis in NK cell killing effect on LUAD cells, western blot tested the protein level of perforin and granzyme B. ELISA determined the level of IFN-γ. CytoTox 96 Non-Radioactive Cytotoxicity Assay kit was applied for cytotoxicity detection of NK cells. Perforin and granzyme B fluorescence intensity was measured via immunofluorescence, and cell apoptosis levels were also revealed via flow cytometry. Results. MANCR was found to be upregulated, while miRNA-30d-5p expression was downregulated in LUAD tissues. Overexpression of MANCR in LUAD cells significantly reduced NK cell IFN-γ secretion, expression of granzyme B and perforin, and NK cell killing effect. In addition, MANCR could target and downregulate miRNA-30d-5p expression, and miRNA-30d-5p overexpression reversed the inhibition of NK cell killing effect caused by MANCR overexpression. Conclusion. MANCR inhibited the killing effect of NK cells on LUAD via targeting and downregulating miRNA-30d-5p and provided new ideas for antitumor therapy based on tumor microenvironment.

Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma

Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcriptomic data of LUAD were obtained from The Cancer Genome Atlas. Differently expressed necroptosis-related long non-coding RNAs got identified by performing both the univariate and co-expression Cox regression analyses. Subsequently, the least absolute shrinkage and selection operator technique was adopted in constructing the nrlncRNA model. We made a comparison of the areas under the curve, did the count of the values of Akaike information criterion of 1-year, 2-year, as well as 3-year receiver operating characteristic curves, after which the cut-off value was determined for the construction of an optimal model to be used in identifying high risk and low risk patients. Genes, tumor-infiltrating immune cells, clinical correlation analysis, and chemotherapeutic agents data of both the high-risk and low-risk subgroups were also performed. We identified 26 DEnrlncRNA pairs, which were involved in the Cox regression model constructed. The curve areas under survival periods of 1 year, 2 years, and 3 years of patients with LUAD were 0.834, 0.790, and 0.821, respectively. The cut-off value set was 2.031, which was used in the identification of either the high-risk or low-risk patients. Poor outcomes were observed in patients belonging to the high-risk group. The risk score was the independent predictor of the LUAD outcome (p < 0.001). The expression levels of immune checkpoint and infiltration of specific immune cells were anticipated by the gene risk model. The high-risk group was found to be highly sensitive to docetaxel, erlotinib, cisplatin, and paclitaxel. The model established through nrlncRNA pairs irrespective of the levels of expression could give a prediction on the LUAD patients’ prognosis and assist in identifying the patients who might gain more benefit from chemotherapeutic agents.

Development of six immune-related lncRNA signature prognostic model for smoking-positive lung adenocarcinoma

BACKGROUND

Smoking is one of the most hazardous risk factors for the development of lung adenocarcinoma (LUAD). Many survival and prognosis-related biomarkers were discovered using database mining. However, the precision of immune-related long noncoding RNAs (lncRNAs) predictions is insufficient. We identified a novel signature to improve the estimate of smoking-related LUAD prognosis.

METHODS

The Cancer Genome Atlas database (TCGA) was used to obtain the LUAD lncRNA expression profiles. The smoking-related LUAD cohort was randomly split into discovery and validation cohorts. To determine the risk score, use the LASSO Cox regression technique on the prognostic immune-related lncRNA. The risk signature has been developed.

RESULTS

A total of 643 immune-related lncRNAs were identified as potential candidates for a risk signature. Finally, six immune-related lncRNAs (AL359915.2, AP000695.1, HSPC324, TGFB2-AS1, AC026355.1, and AC002128.2) were identified and used to carry out risk signature, which showed a close association with overall survival in the discovery cohort. We classified patients as high risk or low risk based on a median risk score of 1.0783. In the discovery cohort, overall survival was marginally longer in the low-risk group than in the high-risk category (p = 2.28e08). The area under the curves (AUC) for 1-, 3-, and 5-year survival was 0.67, 0.7, and 0.82, respectively. Furthermore, we successfully validated and combined cohorts using this risk profile. We discovered a strong positive connection between HSPC324 and VIPR1 as a possible novel biomarker for smoking-related LUAD development in our study.

CONCLUSIONS

Our research has established a six immune-lncRNA signature that may be used to predict the prognosis of smoking-related LUAD with great accuracy.

TNF-α Induced Myotube Atrophy in C2C12 Cell Line Uncovers Putative Inflammatory-Related lncRNAs Mediating Muscle Wasting

Background: Muscle atrophy is a complex catabolic condition developing under different inflammatory-related systemic diseases resulting in wasting of muscle tissue. While the knowledge of the molecular background of muscle atrophy has developed in recent years, how the atrophic conditions affect the long non-coding RNA (lncRNAs) machinery and the exact participation of the latter in the mediation of muscle loss are still unknown. The purpose of the study was to assess how inflammatory condition developing under the tumor necrosis factor alpha (TNF-α) treatment affects the lncRNAs’ expression in a mouse skeletal muscle cell line. Materials and method: A C2C12 mouse myoblast cell line was treated with TNF-α to develop atrophy, and inflammatory-related lncRNAs mediating muscle loss were identified. Bioinformatics was used to validate and analyze the discovered lncRNAs. The differences in their expression under different TNF-α concentrations and treatment times were investigated. Results: Five lncRNAs were identified in a discovery set as atrophy related and then validated. Three lncRNAs, Gm4117, Ccdc41os1, and 5830418P13Rik, were selected as being significant for inflammatory-related myotube atrophy. Dynamics changes in the expression of lncRNAs depended on both TNF-α concentration and treatment time. Bioinformatics analysis revealed the mRNA and miRNA target for selected lncRNAs and their putative involvement in the molecular processes related to muscle atrophy. Conclusions: The inflammatory condition developing in the myotube under the TNF-α treatment affects the alteration of lncRNAs’ expression pattern. Experimental and bioinformatics testing suggested the prospective role of lncRNAs in the mediation of muscle loss under an inflammatory state.

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.

Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.

Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.

Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.