Development and verification of a 7-lncRNA prognostic model based on tumor immunity for patients with ovarian cancer

Effects of NK cell-related lncRNA on the immune microenvironment and molecular subtyping for pancreatic ductal adenocarcinoma

Background

Patients with pancreatic ductal adenocarcinoma (PDAC) face a highly unfavorable outcome and have a poor response to standard treatments. Immunotherapy, especially therapy based on natural killer (NK) cells, presents a promising avenue for the treatment of PDAC.

Aims

This research endeavor seeks to formulate a predictive tool specifically designed for PDAC based on NK cell-related long non-coding RNA (lncRNA), revealing new molecular subtypes of PDAC to promote personalized and precision treatment.

Methods

Utilizing the Tumor Immune Single-cell Hub 2 platform, we discovered genes associated with NK cells in PDAC. We employed the TCGA-PAAD dataset to ascertain the expression profiles of these NK cell-related genes and to screen for lncRNAs correlated with NK cells. Subsequently, we utilized Cox regression analysis for hazard ratios and LASSO regression analysis to identify three NK cell-related lncRNAs that were used to develop a prognostic assessment model. The forecasting accuracy of this model was appraised using the ROC curve and validated using a test set and the complete dataset.

Results

Successful construction of a prognostic model comprising three lncRNAs was achieved, demonstrating good predictive efficiency in the training set, validation dataset, and the entire dataset. NK cells display robust interactions with malignant cells, CD8 T cells, and fibroblasts in the PDAC tumor microenvironment and participate in the transport of various signaling molecules and following immune responses in PDAC. According to the expression patterns of NK cell-related lncRNA, we labeled PDAC patients as four molecular subtypes, exhibiting significant differences in immune cell infiltration, drug sensitivity, and other aspects.

Conclusion

This study Uncovered the activity of NK cells within PDAC, proposed an NK cell-related lncRNA model, and delineated new molecular subtypes, thereby providing targets for personalized therapy.

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer

Background

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined.

Methods

Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells.

Results

A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells.

Conclusion

Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

Glycolysis-associated lncRNAs in cancer energy metabolism and immune microenvironment: a magic key

The dependence of tumor cells on glycolysis provides essential energy and raw materials for their survival and growth. Recent research findings have indicated that long chain non-coding RNAs (LncRNAs) have a key regulatory function in the tumor glycolytic pathway and offer new opportunities for cancer therapy. LncRNAs are analogous to a regulatory key during glycolysis. In this paper, we review the mechanisms of LncRNA in the tumor glycolytic pathway and their potential therapeutic strategies, including current alterations in cancer-related energy metabolism with lncRNA mediating the expression of key enzymes, lactate production and transport, and the mechanism of interaction with transcription factors, miRNAs, and other molecules. Studies targeting LncRNA-regulated tumor glycolytic pathways also offer the possibility of developing new therapeutic strategies. By regulating LncRNA expression, the metabolic pathways of tumor cells can be interfered with to inhibit tumor growth and metastasis, thus affecting the immune and drug resistance mechanisms of tumor cells. In addition, lncRNAs have the capacity to function as molecular markers and target therapies, thereby contributing novel strategies and approaches to the field of personalized cancer therapy and prognosis evaluation. In conclusion, LncRNA, as key molecules regulating the tumor glycolysis pathway, reveals a new mechanism of abnormal metabolism in cancer cells. Future research will more thoroughly investigate the specific mechanisms of LncRNA glycolysis regulation and develop corresponding therapeutic strategies, thereby fostering new optimism for the realization of precision medicine.

Identification of the biological functions and chemo-therapeutic responses of ITGB superfamily in ovarian cancer

BACKGROUND

Patients with ovarian cancer (OC) tend to face a poor prognosis due to a lack of typical symptoms and a high rate of recurrence and chemo-resistance. Therefore, identifying representative and reliable biomarkers for early diagnosis and prediction of chemo-therapeutic responses is vital for improving the prognosis of OC.

METHODS

Expression levels, IHC staining, and subcellular distribution of eight ITGBs were analyzed using The Cancer Genome Atlas (TCGA)-Ovarian Serous Cystadenocarcinoma (OV) database, GEO DataSets, and the HPA website. PrognoScan and Univariate Cox were used for prognostic analysis. TIDE database, TIMER database, and GSCA database were used to analyze the correlation between immune functions and ITGBs. Consensus clustering analysis was performed to subtype OC patients in the TCGA database. LASSO regression was used to construct the predictive model. The Cytoscape software was used for identifying hub genes. The 'pRRophetic' R package was applied to predict chemo-therapeutic responses of ITGBs.

RESULTS

ITGBs were upregulated in OC tissues except ITGB1 and ITGB3. High expression of ITGBs correlated with an unfavorable prognosis of OC except ITGB2. In OC, there was a strong correlation between immune responses and ITGB2, 6, and 7. In addition, the expression matrix of eight ITGBs divided the TCGA-OV database into two subgroups. Subgroup A showed upregulation of eight ITGBs. The predictive model distinguishes OC patients from favorable prognosis to poor prognosis. Chemo-therapeutic responses showed that ITGBs were able to predict responses of common chemo-therapeutic drugs for patients with OC.

CONCLUSIONS

This article provides evidence for predicting prognosis, immuno-, and chemo-therapeutic responses of ITGBs in OC and reveals related biological functions of ITGBs in OC.

Relationships of SIGLEC family-related lncRNAs with clinical prognosis and tumor immune microenvironment in ovarian cancer

Long non-coding RNAs (lncRNAs) and Sialic acid-binding immunoglobulin-type lectin (SIGLEC) family members play an important role in proliferation, apoptosis, immune-cell activation and tumor development. However, the relationships of SIGLEC family-related lncRNAs with clinical prognosis and tumor immune microenvironment in ovarian cancer (OC) are still unclear. 426 SIGLEC family-related lncRNAs were obtained according to the screening criteria R > 0.4 and p < 0.05 using Pearson correlation analysis. A risk model contained AL133279.1, AL021878.2, AC078788.1, AC039056.2, AC008750.1 and AC007608.3 was conducted based on the univariate Cox regression analysis, a least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses. OC patient were divided into high-and low-risk group based on the median riskscore. K-M curve and ROC curve revealed that risk model has an abuset prognostic potential for OC patients. Moreover, we successfully validated the prognostic value of the model in the internal datasets, external datasets and clinical sample dataset. Finally, we found that the riskscore was positively correlated with the vast majority of immune cell infiltration. In conclusion, our research identified that a novel SIGLEC family-related lncRNAs risk model to predict the prognosis of OC patients. SIGLEC family-related lncRNAs risk model also has a positive relationship with the tumor immune microenvironment of OC, which may provide a new direction for immunotherapy of OC.

Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma

Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Background

Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.

Methods

420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.

Results

64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.

Conclusion

Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer

BACKGROUND

The leading cause of death related to gynecologic cancer is ovarian cancer, which typically has a poor prognosis. T cells are referred to as key mediators of immunosurveillance and tumor eradication, and unbalanced regulation or lack of T cells in tumors result in immunotherapy resistance.

METHODS

The identification of T cell related markers depended on single-cell RNA-seq analysis. Using data from multiple datasets, including TCGA, GSE14764, GSE26193, GSE26712, and GSE140082, we constructed a prognostic signature called TRS (T cell-related signature) using 10 different machine learning algorithms. The correlation between TRS and drug sensitivity were analyzed using the data from GSE91061 and IMvigor210 dataset.

RESULTS

PlsRcox method based TRS was as a risk factor for the clinical outcome of ovarian cancer patients. In comparison with stage, grade and many prognostic signatures, the performance of our TRS in evaluating the clinical outcome was better in ovarian cancer. TRS-based risk score showed distinct association with the level of ESTIMATE score, immune-related function score and immune cells. Moreover, TRS could be used to predict the immunotherapy response and chemotherapy response in ovarian cancer.

CONCLUSION

In conclusion, we constructed a powerful TRS in ovarian cancer, which could accurately predict the clinical outcome of patients and be used to predict the immunotherapy response and chemotherapy response.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.