Development and Verification of an Autophagy-Related lncRNA Signature to Predict Clinical Outcomes and Therapeutic Responses in Ovarian Cancer

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.

Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer

BACKGROUND

There has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators.

METHODS

RNA sequencing (RNA-seq) profiles with clinicopathological data from TCGA database were used to select prognostic CRLs and then constructed prognostic risk model using multivariate regression analysis and LASSO algorithms. An independent dataset from GEO database was used to validate the prognostic performance. Combined with clinical factors, we further constructed a prognostic nomogram. In addition, tumor immune microenvironment, somatic mutation and drug sensitivity were analyzed using ssGSEA, GSVA, ESTIMATE and CIBERSORT algorithms.

RESULT

A total of 129 CRLs were selected whose expression levels were significantly related to expression levels of 10 cuproptosis-related genes. The univariate Cox regression analysis showed that 12 CRLs were associated with overall survival (OS). Using LASSO algorithms and multivariate regression analysis, we constructed a four-CRLs prognostic signature in the training dataset. Patients in the training dataset could be classified into high- or low-risk subgroups with significantly different OS (log-rank p < 0.001). The prognostic performance was confirmed in TCGA-OC cohort (log-rank p < 0.001) and an independent GEO cohort (log-rank p = 0.023). Multivariate cox regression analysis proved the four-CRLs signature was an independent prognostic factor for OC. Additionally, different risk subtypes showed significantly different levels of immune cells, signal pathways, and drug response.

CONCLUSION

We established a prognostic signature based on cuproptosis-related lncRNAs for OC patients, which will be of great value in predicting the prognosis patients and may provide a new perspective for research and individualized treatment.

Non-coding RNAs: The recently accentuated molecules in the regulation of cell autophagy for ovarian cancer pathogenesis and therapeutic response

Autophagy is a self-recycling and conserved process, in which the senescent cytoplasmic components are degraded in cells and then recycled to maintain homeostatic balance. Emerging evidence has suggested the involvement of autophagy in oncogenesis and progression of various cancers, such as ovarian cancer (OC). Meanwhile, the non-coding RNAs (ncRNAs) frequently regulate the mRNA transcription and other functional signaling pathways in cell autophagy, displaying promising roles in human cancer pathogenesis and therapeutic response. This article mainly reviews the cutting-edge research advances about the interactions between ncRNAs and autophagy in OC. This review not only summarizes the underlying mechanisms of dynamic ncRNA-autophagy association in OC, but also discusses their prognostic implications and therapeutic biomarkers. The aim of this review was to provide a more in-depth knowledge framework exploring the ncRNA-autophagy crosstalk and highlight the promising treatment strategies for OC patients.

Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA

(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

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

BACKGROUND

Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC.

METHOD

Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation.

RESULTS

We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction.

CONCLUSIONS

We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.

Placental circadian lincRNAs and spontaneous preterm birth

Long non-coding RNAs (lncRNAs) have a much higher cell- and/or tissue-specificity compared to mRNAs in most cases, making them excellent candidates for therapeutic applications to reduce off-target effects. Placental long non-coding RNAs have been investigated in the pathogenesis of preeclampsia (often causing preterm birth (PTB)), but less is known about their role in preterm birth. Preterm birth occurs in 11% of pregnancies and is the most common cause of death among infants in the world. We recently identified that genes that drive circadian rhythms in cells, termed molecular clock genes, are deregulated in maternal blood of women with spontaneous PTB (sPTB) and in the placenta of women with preeclampsia. Next, we focused on circadian genes-correlated long intergenic non-coding RNAs (lincRNAs, making up most of the long non-coding RNAs), designated as circadian lincRNAs, associated with sPTB. We compared the co-altered circadian transcripts-correlated lincRNAs expressed in placentas of sPTB and term births using two published independent RNAseq datasets (GSE73712 and GSE174415). Nine core clock genes were up- or downregulated in sPTB versus term birth, where the RORA transcript was the only gene downregulated in sPTB across both independent datasets. We found that five circadian lincRNAs (LINC00893, LINC00265, LINC01089, LINC00482, and LINC00649) were decreased in sPTB vs term births across both datasets (p ≤ .0222, FDR≤.1973) and were negatively correlated with the dataset-specific clock genes-based risk scores (correlation coefficient r = -.65 ∼ -.43, p ≤ .0365, FDR≤.0601). Gene set variation analysis revealed that 65 pathways were significantly enriched by these same five differentially expressed lincRNAs, of which over 85% of the pathways could be linked to immune/inflammation/oxidative stress and cell cycle/apoptosis/autophagy/cellular senescence. These findings may improve our understanding of the pathogenesis of spontaneous preterm birth and provide novel insights into the development of potentially more effective and specific therapeutic targets against sPTB.

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

METHODS

Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

RESULTS

Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

CONCLUSIONS

We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Background

Increasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.

Methods

The ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.

Results

Three immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.

Conclusions

Our study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.