Identification of an Immune-Related Long Noncoding RNA Pairs Model to Predict Survival and Immune Features in Gastric Cancer

CD36-BATF2\MYB Axis Predicts Anti-PD-1 Immunotherapy Response in Gastric Cancer

Despite the utilization of anti-PD-1 therapy in gastric cancer (GC), the absence of a reliable predictive biomarker continues to pose a challenge. In this study, we utilized bioinformatic analysis and immunohistochemistry to develop a prediction model for activated CD4+ memory T cells, considering both mRNA and protein levels. An elevation of activated CD4+ memory T cells in GC was noted, which exhibited a strong association with the patients' overall survival. By utilizing WGCNA and DEG analysis, we discovered that BATF2, MYB, and CD36 are genes that exhibit differential expression and are linked to activated CD4+ memory T cells. Afterwards, a forecast model was built utilizing Stepwise regression and immunohistochemistry relying on the three genes. The model's high-risk score showed significant associations with a suppressive immune microenvironment. Moreover, our model exhibited encouraging prognostic value and superior performance in predicting response to immune checkpoint blockade therapy compared with the conventional CD8+PD-L1 model. In terms of mechanism, CD36 could function as a receptor upstream that identifies Helicobacter pylori and fatty acids. This recognition then results in the reduction of the BATF2-MYB protein complex and subsequent alterations in the transcription of genes associated with classical T cell activation. As a result, the activation state of CD4+ memory T cells is ultimately suppressed. The CD36-BATF2/MYB signature serves as a robust predictor of anti-PD-1 immunotherapy response in GC.

LMNTD2-AS1 regulates immune cell infiltration and promotes prostate cancer progression by targeting FUS to regulate NRF2 signal pathway

Numerous studies have demonstrated that long non-coding RNAs (lncRNAs) play crucial roles in tumor progression. This study aimed to identify lncRNAs associated with overall survival (OS) and progression-free interval (PFI) in prostate cancer (PCa) patients and to elucidate the driving mechanisms and functions of these lncRNAs. We utilized the TCGA database to screen for lncRNAs linked with OS and PFI. KM survival analysis, ROC curve analysis, and Cox survival analysis were employed to assess the prognostic significance of lncRNAs in PCa patients. We conducted a loss-of-function assay to explore the role of lncRNAs in PCa. Correlation analysis was performed to study the relationship between lncRNAs and immune cell infiltration. Lasso regression analysis was performed to screen proteins which might interact with lncRNAs, while rescue experiments verified the integrity of the signaling pathway. LMNTD2-AS1 was found to be the only lncRNA in PCa patients associated with both OS and PFI with significantly elevated levels in PCa. Elevated LMNTD2-AS1 expression was significantly linked to advanced stage, grade, primary treatment outcomes, residual tumors, and Gleason scores in PCa patients. Moreover, multivariate Cox regression analysis revealed that high LMNTD2-AS1 expression independently predicted PFI in PCa patients. The AUC of LMNTD2-AS1 for predicting 3-year OS and 5-year OS in PCa patients was 0.877 and 0.807, respectively, while for 3-year PFI and 5-year PFI it was 0.751 and 0.727, respectively. Overexpression of LMNTD2-AS1 correlated with infiltration of neutrophils, macrophages, pDC, NK CD56bright cells, and other immune cells. Furthermore, FUS and NRF2 are both potential binding proteins and related signaling pathways downstream of LMNTD2-AS1. Functional experiments demonstrated that LMNTD2-AS1 knockdown significantly inhibited migration, invasion, and proliferation of PCa cells while overexpression of FUS was found to rescue the functional inhibition caused by LMNTD2-AS1 knockdown. LMNTD2-AS1 functions as an oncogene in PCa, influencing patient prognosis and the immune microenvironment; it may regulate immune cell infiltration and promote PCa progression by interacting with the NRF2 signaling pathway via FUS binding.

Analysis of Pyroptosis-Related Immune Signatures and Identification of Pyroptosis-Related LncRNA Prognostic Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common urinary system malignant tumor with a high incidence and recurrence rate. Pyroptosis is a kind of programmed cell death caused by inflammasomes. More and more evidence had confirmed that pyroptosis plays a very significant part in cancer, and it is controversial whether pyroptosis promotes or inhibits tumors. Consistently, its potential role in ccRCC treatment efficacy and prognosis remains unclear. In this study, we systematically investigated the role of pyroptosis in the ccRCC samples from The Cancer Genome Atlas (TCGA) database. Based on the differentially expressed pyroptosis-related genes (DEPRGs), we identified three pyroptosis subtypes with different clinical outcomes, immune signatures, and responses to immunotherapy. Gene set variation analysis (GSVA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that pyroptosis activation meant infiltration of more immune cells that is conducive to tumor progression. To further investigate the immunomodulatory effect of pyroptosis in ccRCC, we constructed a pyroptosis-score based on the common differential prognostic genes of the three pyroptosis subtypes. It was found that patients with high pyroptosis-score were in an unfavorable immune environment and the prognosis was worse. Gene set enrichment analysis suggested that immune-related biological processes were activated in the high pyroptosis-score group. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression was implemented for constructing a prognostic model of eight pyroptosis-related long noncoding RNAs (PRlncRNAs) in the TCGA dataset, and the outcomes revealed that, compared with the low-risk group, the model-based high-risk group was intently associated with poor overall survival (OS). We further explored the relationship between high- and low-risk groups with tumor microenvironment (TME), immune infiltration, and drug therapy. Finally, we constructed and confirmed a robust and reliable PRlncRNA pairs prediction model of ccRCC, identified PRlncRNA, and verified it by experiments. Our findings suggested the potential role of pyroptosis in ccRCC, offering new insights into the prognosis of ccRCC and guiding effectual targeted therapy and immunotherapy.

An Immune-Related Long Noncoding RNA Pair as a New Biomarker to Predict the Prognosis of Patients in Breast Cancer

Background: Immune-related long non-coding RNAs (irlncRNAs) might remodel the tumor immune microenvironment by changing the inherent properties of tumor cells and the expression of immune genes, which have been used to predict the efficacy of immunotherapy and the prognosis of various tumors. However, the value of irlncRNAs in breast cancer (BRCA) remains unclear.

Materials and Methods: Initially, transcriptome data and immune-related gene sets were downloaded from The Cancer Genome Atlas (TCGA) database. The irlncRNAs were extracted from the Immunology Database and Analysis Portal (ImmPort) database. Differently expressed irlncRNAs (DEirlncRNAs) were further identified by utilizing the limma R package. Then, univariate and multivariate Cox regression analyses were conducted to select the DEirlncRNAs associated with the prognosis of BRCA patients. In addition, the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to determine the DEirlncRNA pairs with the independent prediction capability of prognosis in BRCA patients. Finally, the chosen DEirlncRNA pair would be evaluated in terms of survival time, clinicopathological characteristics, tumor-infiltrating immune cells, immune checkpoints (ICs), signaling pathways, and potential small-molecule drugs.

Results: A total of 21 DEirlncRNA pairs were extracted, and among them, lncRNA MIR4435-2HG and lncRNA U62317.1 were chosen to establish a risk signature that served as an independent prognostic biomarker in BRCA patients. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they also had an abundance of infiltration of CD4+ T and CD8+ T cells to enhance the immune response to tumor cells. Furthermore, the risk signature showed a strong correlation with ICs, signaling pathways, and potential small-molecule drugs.

Conclusion: Our research revealed that the risk signature independent of specific DEirlncRNA pair expression was closely associated with the prognosis and tumor immune microenvironment in BRCA patients and had the potential to function as an independent prognostic biomarker and a predictor of immunotherapy for BRCA patients, which would provide new insights for BRCA accurate treatment.

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.

LncRNA-AK137033 inhibits the osteogenic potential of adipose-derived stem cells in diabetic osteoporosis by regulating Wnt signaling pathway via DNA methylation

OBJECTIVES

Bone tissue engineering based on adipose-derived stem cells (ASCs) is expected to become a new treatment for diabetic osteoporosis (DOP) patients with bone defects. However, compared with control ASCs (CON-ASCs), osteogenic potential of DOP-ASCs is decreased, which increased the difficulty of bone reconstruction in DOP patients. Moreover, the cause of the poor osteogenesis of ASCs in a hyperglycemic microenvironment has not been elucidated. Therefore, this study explored the molecular mechanism of the decline in the osteogenic potential of DOP-ASCs from the perspective of epigenetics to provide a possible therapeutic target for bone repair in DOP patients with bone defects.

MATERIALS AND METHODS

An animal model of DOP was established in mice. CON-ASCs and DOP-ASCs were isolated from CON and DOP mice, respectively. AK137033 small interfering RNA (SiRNA) and an AK137033 overexpression plasmid were used to regulate the expression of AK137033 in CON-ASCs and DOP-ASCs in vitro. Lentiviruses that carried shRNA-AK137033 or AK137033 cDNA were used to knockdown or overexpress AK137033, respectively, in CON-ASCs and DOP-ASCs in vivo. Hematoxylin and eosin (H&E), Masson's, alizarin red, and alkaline phosphatase (ALP) staining, micro-computed tomography (Micro-CT), flow cytometry, qPCR, western blotting, immunofluorescence, and bisulfite-specific PCR (BSP) were used to analyze the functional changes of ASCs.

RESULTS

The DOP mouse model was established successfully. Compared with CON-ASCs, AK137033 expression, the DNA methylation level of the sFrp2 promoter region, Wnt signaling pathway markers, and the osteogenic differentiation potential were decreased in DOP-ASCs. In vitro experiments showed that AK137033 silencing inhibited the Wnt signaling pathway and osteogenic ability of CON-ASCs by reducing the DNA methylation level in the sFrp2 promoter region. Additionally, overexpression of AK137033 in DOP-ASCs rescued these changes caused by DOP. Moreover, the same results were obtained in vivo.

CONCLUSIONS

LncRNA-AK137033 inhibits the osteogenic potential of DOP-ASCs by regulating the Wnt signaling pathway via modulating the DNA methylation level in the sFrp2 promoter region. This study provides an important reference to find new targets for the treatment of bone defects in DOP patients.

Immune-Related Long Non-coding RNA Constructs a Prognostic Signature of Ovarian Cancer

Background

Since ovarian cancer leads to the poor prognosis in women all over the world, we aim to construct an immune-related lncRNAs signature to improve the survival of ovarian cancer patients.

Methods

Normal and cancer patient samples and corresponding clinical data of ovarian were obtained from The Genotype-Tissue Expression (GTEx) portal and The Cancer Genome Atlas (TCGA) database. The predictive signature was constructed by the lasso penalty Cox proportional hazard regression model. The division of different risk groups was accounting for the optimal critical value of the time-dependent Receiver Operating Characteristic (ROC) curve. Finally, we validated and evaluated the application of this prognostic signature based on the clinical factors, chemo-sensitivity and immune status of different risk groups.

Results

The signature was established from 145 DEirlncRNAs and can be shown as an independent prognostic risk factor with accurate prediction on overall survival in ovarian cancer patients. Further analysis on the application of the prognostic signature showed that patients with low-risk had a better sensitivity to chemotherapy and a higher immunogenicity.

Conclusion

We constructed and verified an effective signature based on DEirlncRNA pairs, which could predict the prognosis, drug sensitivity and immune status of ovarian cancer patients and promote the prognostic estimation and individualized treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12575-021-00161-9.