Prognostic significance of an autophagy-related long non-coding RNA signature in patients with oral and oropharyngeal squamous cell carcinoma

Identification of MIR600HG/hsa-miR-342-3p/ANLN network as a potential prognosis biomarker associated with lmmune infiltrates in pancreatic cancer

Pancreatic cancer is one of the tumors with the worst prognosis, causing serious harm to human health. The RNA network and immune response play an important role in tumor progression. While a systematic RNA network linked to the tumor immune response remains to be further explored in pancreatic cancer. Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, the MIR600HG/hsa-miR-342-3p/ANLN network was determined. WB and IHC were used to confirm the high expression of ANLN in pancreatic cancer. The prognostic model based on the RNA network could effectively predict the survival prognosis of patients. The analysis of immune infiltration showed that the MIR600HG/hsa-miR-342-3p/ANLN network altered the level of infiltration of T helper 2 (Th2) and effector memory T (Tem) cells. Furthermore, we found that the chemokines chemokine ligand (CCL) 5 and CCL14 may play a key role in immune cell infiltration mediated by the RNA network. In conclusion, this study constructed a prognostic model based on the MIR600HG/hsa-miR-342-3p/ANLN network and found that it may function in tumor immunity.

Identification of m5C-related lncRNAs signature to predict prognosis and therapeutic responses in esophageal squamous cell carcinoma patients

Esophageal squamous cell carcinoma (ESCC) has a dismal prognosis because of atypical early symptoms and heterogeneous therapeutic responses. 5-methylcytosine (m5C) modification plays an important role in the onset and development of many tumors and is widespread in long non-coding RNA (lncRNA) transcripts. However, the functions of m5C and lncRNAs in ESCC have not been completely elucidated. Herein, this study aimed to explore the role of m5C-related lncRNAs in ESCC. The RNA-seq transcriptome profiles and clinical information were downloaded from the TCGA-ESCC database. Pearson analysis was used to identify m5C-related lncRNAs. Then we established the m5C-related lncRNAs prognostic signature (m5C-LPS) using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Then, the prognostic value of m5C-LPS was evaluated internally and externally using the TCGA-ESCC and GSE53622 databases through multiple methods. We also detected the expression of these lncRNAs in ESCC cell lines and patient tissues. Fluorescence in situ hybridization (FISH) was used to detect the prognostic value of specific lncRNA. In addition, clinical parameters, immune status, genomic variants, oncogenic pathways, enrichment pathways, and therapeutic response features associated with m5C-LPS were explored using bioinformatics methods. We constructed and validated a prognostic signature based on 9 m5C-related lncRNAs (AC002091.2, AC009275.1, CAHM, LINC02057.1, AC0006329.1, AC037459.3, AC064807.1, ATP2B1-AS1, and UBAC2-AS1). The quantitative real-time polymerase chain reaction (qRT-PCR) revealed that most lncRNAs were upregulated in ESCC cell lines and patient tissues. And AC002091.2 was validated to have significant prognostic value in ESCC patients. A composite nomogram was generated to facilitate clinical practice by integrating this signature with the N stage. Besides, patients in the low-risk group were characterized by good clinical outcomes, favorable immune status, and low oncogenic alteration. Function enrichment analysis indicated that the risk score was associated with mRNA splicing, ncRNA processing, and DNA damage repair response. At the same time, we found significant differences in the responses to chemoradiotherapy between the two groups, proving the value of m5C-LPS in treatment decision-making in ESCC. This study established a novel prognostic signature based on 9 m5C-related lncRNAs, which is a promising biomarker for predicting clinical outcomes and therapeutic response in ESCC.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

Constructing a novel signature and predicting the immune landscape of colon cancer using N6-methylandenosine-related lncRNAs

Background: Colon cancer (CC) is a prevalent malignant tumor that affects people all around the world. In this study, N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) in 473 colon cancers and 41 adjacent tissues of CC patients from The Cancer Genome Atlas (TCGA) were investigated. Method: The Pearson correlation analysis was conducted to examine the m6A-related lncRNAs, and the univariate Cox regression analysis was performed to screen 38 prognostic m6A-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression analysis were carried out on 38 prognostic lncRNAs to develop a 14 m6A-related lncRNAs prognostic signature (m6A-LPS) in CC. The availability of the m6A-LPS was evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Results: Three m6A modification patterns with significantly different N stages, survival time, and immune landscapes were identified. It has been discovered that the m6A-LPS, which is based on 14 m6A-related lncRNAs (TNFRSF10A-AS1, AC245041.1, AL513550.1, UTAT33, SNHG26, AC092944.1, ITGB1-DT, AL138921.1, AC099850.3, NCBP2-AS1, AL137782.1, AC073896.3, AP006621.2, AC147651.1), may represent a new, promising biomarker with great potential. It was re-evaluated in terms of survival rate, clinical features, tumor infiltration immune cells, biomarkers related to Immune Checkpoint Inhibitors (ICIs), and chemotherapeutic drug efficacy. The m6A-LPS has been revealed to be a novel potential and promising predictor for evaluating the prognosis of CC patients. Conclusion: This study revealed that the risk signature is a promising predictive indicator that may provide more accurate clinical applications in CC therapeutics and enable effective therapy strategies for clinicians.

Potential prognostic value of a eight ferroptosis-related lncRNAs model and the correlative immune activity in oral squamous cell carcinoma

Background

To investigate the prognostic value of ferroptosis-related long noncoding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC) and to construct a prognostic risk and immune activity model.

Methods

We obtained clinical and RNA-seq information on OSCC patient data in The Cancer Genome Atlas (TCGA) Genome Data Sharing (GDC) portal. Through a combination of a differential analysis, Pearson correlation analysis and Cox regression analysis, ferroptosis-related lncRNAs were identified, and a prognostic model was established based on these ferroptosis-related lncRNAs. The accuracy of the model was evaluated via analyses based on survival curves, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA). Univariate Cox and multivariate Cox regression analyses were performed to evaluate independent prognostic factors. Then, the infiltration and functional enrichment of immune cells in high- and low-risk groups were compared. Finally, certain small-molecule drugs that potentially target OSCC were predicted via use of the L1000FWD database.

Results

The prognostic model included 8 ferroptosis-related lncRNAs (FIRRE, LINC01305, AC099850.3, AL512274.1, AC090246.1, MIAT, AC079921.2 and LINC00524). The area under the ROC curve (AUC) was 0.726. The DCA revealed that the risk score based on the prognostic model was a better prognostic indicator than other clinical indicators. The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OSCC. There were differences in immune cell infiltration, immune functions, m6A-related gene expression levels, and signal pathway enrichment between the high- and low-risk groups. Subsequently, several small-molecule drugs were predicted for use against differentially expressed ferroptosis-related genes in OSCC.

Conclusions

We constructed a new prognostic model of OSCC based on ferroptosis-related lncRNAs. The model is valuable for prognostic prediction and immune evaluation, laying a foundation for the study of ferroptosis-related lncRNAs in OSCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01097-z.

Unravelling the roles of Autophagy in OSCC: A renewed perspective from mechanisms to potential applications

Oral squamous cell carcinoma (OSCC) is associated with a low survival rate and a high disability rate, making it a serious health burden, particularly in Southeast Asian countries. Therefore, improvements in the diagnosis, treatment, and prognosis prediction of OSCC are highly warranted. Autophagy has a significant impact on cancer development. Studies on autophagy in various human cancers have made outstanding contributions; however, the relationship between autophagy and OSCC remains to be explored. This review highlights the roles of autophagy in OSCC and discusses the relationship between autophagy and Epithelial–mesenchymal transition. Considering the lack of OSCC biomarkers, we focus on the studies involving OSCC-related bioinformatics analysis and molecular targets. Based on some classical targets, we summarize several key autophagy-related biomarkers with a considerable potential for clinical application, which may become the hotspot of OSCC research. In conclusion, we elaborate on the interrelationship between autophagy and OSCC and highlight the shortcomings of current studies to provide insights into the potential clinical strategies.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment. Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA-microRNA-mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center. Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature. Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

The role of long noncoding RNAs as regulators of the epithelial–Mesenchymal transition process in oral squamous cell carcinoma cells

Oral squamous cell carcinoma (OSCC) is a highly invasive and relatively prevalent cancer, accounting for around 3% of all cancers diagnosed. OSCC is associated with bad outcomes, with only 50% overall survival (OS) after five years. The ability of OSCC to invade local and distant tissues relies on the induction of the epithelial–mesenchymal transition (EMT), wherein epithelial cells shed their polarity and cell-to-cell contacts and acquire mesenchymal characteristics. Consequently, a comprehensive understanding of how tumor cell EMT induction is regulated has the potential of direct attempts to prevent tumor progression and metastasis, resulting in better patient outcomes. Several recent studies have established the significance of particular long noncoding RNAs (lncRNAs) in the context of EMT induction. Moreover, lncRNAs regulate a vast array of oncogenic pathways. With a focus on the mechanisms by which the underlined lncRNAs shape the metastatic process and a discussion of their potential utility as clinical biomarkers or targets for therapeutic intervention in patients with OSCC, the present review thus provides an overview of the EMT-related lncRNAs that are dysregulated in OSCC.

Construction of prognostic signature of patients with oral squamous cell carcinoma based on pyroptosis-related long non-coding RNAs

Background and objective

Oral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and its morbidity and mortality are increasing year by year. Changes in key genes are thought to be closely related to the occurrence and development of OSCC. Pyroptosis is an inflammatory form of programmed cell death that has been implicated in malignancies and inflammatory diseases. Changes in the expression of long noncoding RNAs may also affect tumorigenesis and progression. In this study, our main objective was to evaluate the association between pyroptosis-related lncRNAs and prognosis in patients with OSCC.

Methods

The RNA-seq data and clinicopathological data of OSCC patients are from The Cancer Genome Atlas database. The pyroptosis gene set is obtained from Gene Set Enrichment Analysis database. Univariate COX, Lasso and multivariate COX regression analyses were used for the construction of risk prognostic models of OSCC, eight lncRNAs were incorporated into prognostic models. The Kaplan-Meier method and log-rank test were used to evaluate the differences of overall survival between patients in high-risk and low-risk groups. The reliability of predictions across the dataset was analyzed by receiver operating characteristic (ROC) curves. The immune signature score was calculated using the single-sample gene set enrichment analysis.

Results

Eight pyroptosis-related lncRNAs were used to construct prognostic signature of OSCC, including AC136475.2, AC024075.2, JPX, ZFAS1, TNFRSF10A-AS1, LINC00847, AC099850.3 and IER3-AS1. According to this prognostic signature, patients with OSCC were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis showed that the survival rate of the high-risk group was significantly lower than the low-risk group. ROC area for risk score was 0.716, and ROC area of the 8 lncRNAs are all between 0.5 and 1, implied that these lncRNAs had high accuracy in predicting the prognosis of OSCC patients. Immune Infiltration findings suggested that these lncRNAs affected immune responses in the microenvironment of OSCC.

Conclusion

The prognostic signature based on pyroptosis-related lncRNAs potentially serves as an independent prognostic indicator for OSCC patients. And this signature facilitates research on targeted diagnosis and treatment of patients diagnosed with OSCC.

Emerging role of lncRNAs in drug resistance mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) originates in the squamous cell lining the mucosal surfaces of the head and neck region, including the oral cavity, nasopharynx, tonsils, oropharynx, larynx, and hypopharynx. The heterogeneity, anatomical, and functional characteristics of the patient make the HNSCC a complex and difficult-to-treat disease, leading to a poor survival rate and a decreased quality of life due to the loss of important physiologic functions and aggressive surgical injury. Alteration of driver-oncogenic and tumor-suppressing lncRNAs has recently been recently in HNSCC to obtain possible biomarkers for diagnostic, prognostic, and therapeutic approaches. This review provides current knowledge about the implication of lncRNAs in drug resistance mechanisms in HNSCC. Chemotherapy resistance is a major therapeutic challenge in HNSCC in which lncRNAs are implicated. Lately, it has been shown that lncRNAs involved in autophagy induced by chemotherapy and epithelial-mesenchymal transition (EMT) can act as mechanisms of resistance to anticancer drugs. Conversely, lncRNAs involved in mesenchymal-epithelial transition (MET) are related to chemosensitivity and inhibition of invasiveness of drug-resistant cells. In this regard, long non-coding RNAs (lncRNAs) play a pivotal role in both processes and are important for cancer detection, progression, diagnosis, therapy response, and prognostic values. As the involvement of more lncRNAs is elucidated in chemoresistance mechanisms, an improvement in diagnostic and prognostic tools could promote an advance in targeted and specific therapies in precision oncology.

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients

BACKGROUND

ccRCC is considered as the main subtype of RCC, which accounted for sixth deadliest cancer worldwide. Recently, ubiquitination has been reported to be closely involved in the progression of tumore. The purpose of this study was to identify the ubiquitination-associated genes and co-expressed lncRNAs on the prognosis of clear cell renal cell carcinoma (ccRCC) patients.

METHODS AND PATIENTS

We downloaded 530 cases and the corresponding transcriptome profiling from The Cancer Genome Atlas (TCGA) database. We distinguished mRNA and lncRNA expression data from the transcriptome profiling and then extracted the expression of mRNAs that regulate protein ubiquitination. We obtained lncRNAs associated with protein ubiquitination regulation from the lncRNA data by gene co-expression analysis. Cox regression analysis of survival time, survival status, and lncRNA expression level was carried out, and a prognostic index (PI) was constructed.

RESULTS

The PI was established based on 8 prognostic lncRNAs that regulate protein ubiquitination and distinguish the high-risk group patients from all patients. Multivariate analysis indicated that this PI was an individualized clinical prognostic factor for patients with ccRCC. Regarding clinical characteristics, a ubiquitination-associated clinical-prognostic index (UCPI), containing 8 ubiquitination-related lncRNAs and age, was established and tested with AUC of 0.80.

CONCLUSION

We established a UCPI containing 8 lncRNAs related to protein ubiquitination. This UCPI may become an appropriate model to predict the prognosis in ccRCC patients and guide clinicians to adjust the follow-up regimen.

MIR600HG sponges miR-125a-5p to regulate glycometabolism and cisplatin resistance of oral squamous cell carcinoma cells via mediating RNF44

There is increasing evidence that dysregulated long non-coding RNA (lncRNA) is implicated in tumorigenesis and progression. We aim to explore the role of lncRNA MIR600HG in glycometabolism and cisplatin (DDP) resistance of oral squamous cell carcinoma (OSCC) cells via regulating microRNA-125a-5p (miR-125a-5p) and RING finger 44 (RNF44). Expression of MIR600HG, miR-125a-5p, and RNF44 in OSCC clinical samples, cell lines, and DDP-resistant OSCC cells (SCC-9/DDP) was determined. In SCC-9 cells, proliferation, IC50 value of DDP, migration, invasion, and apoptosis were detected; in SCC-9/DDP cells, proliferation, IC50 value of DDP, apoptosis, glucose consumption, and production of lactic acid and ATP were evaluated. The interaction of MR600HG, miR-125a-5p, and RNF44 was verified. MIR600HG and RNF44 were upregulated while miR-125a-5p was downregulated in OSCC tissues and cell lines, and also in SCC-9/DDP cells. In SCC-9 cells, MIR600HG overexpression improved cell growth, metastasis, and inhibited cell susceptibility to DDP; in SCC-9/DDP cells, silencing of MIR600HG promoted apoptosis, improved DDP sensitivity, and inhibited cell glycolysis. Downregulation of miR-125a-5p showed the opposite effect to downregulation of MIR600HG. MIR600HG bound to miR-125a-5p and miR-125a-5p targeted RNF44. Downregulation of miR-125a-5p reversed the improvement of DDP sensitivity and the inhibition of cell glycolysis by downregulated MIR600HG on SCC-9/DDP cells. Downregulating RNF44 reversed the promotion of DDP resistance and cell glycolysis of SCC-9/DDP cells mediated by downregulation of miR-125a-5p. Collectively, our study addresses that MIR600HG downregulation elevates miR-125a-5p and reduces RNF44 expression, thereby improving DDP sensitivity and inhibiting glycolysis in DDP-resistant OSCC cells.

Identification of the Ferroptosis-Related Long Non-Coding RNAs Signature to Improve the Prognosis Prediction in Papillary Renal Cell Carcinoma

Papillary renal cell carcinoma (pRCC) is one of the epithelial renal cell carcinoma (RCC) histological subtypes. Ferroptosis is a new iron-dependent form of cell death that has been seen in a variety of clinical situations. Using differentially expressed ferroptosis-related long non-coding RNAs (lncRNAs) from patients with pRCC in The Cancer Genome Atlas; we built a prognostic lncRNA-based signature. We discovered seven different lncRNAs that were strongly linked to the prognosis of patients with pRCC. High-risk scores were linked to a poor prognosis for pRCC, which was confirmed by the findings of Kaplan–Meier studies. In addition, the constructed lncRNA signature has a 1-year area under the curve (AUC) of 0.908, suggesting that it has a high predictive value in pRCC. In the high-risk group, Gene set enrichment analyses (GSEA) analysis identified immunological and tumor-related pathways. Furthermore, single-sample GSEA (ssGSEA) revealed significant differences in T cell functions checkpoint, antigen presenting cell (APC) co-stimulation, inflammation promoting, and para inflammation between the two groups with different risk scores. In addition, immune checkpoints like PDCD1LG2 (PD-L2), LAG3, and IDO1 were expressed differently in the two risk groups. In summary, a novel signature based on ferroptosis-related lncRNAs could be applied in predicting the prognosis of patients with pRCC.

Development and Validation of a Ferroptosis-Related lncRNAs Prognosis Model in Oral Squamous Cell Carcinoma

Objectives: Ferroptosis is an iron-dependent form of programmed cell death, which affects the prognosis of many cancers. Some long non-coding RNA (lncRNA) can affect the prognosis of cancer by regulating the process of ferroptosis. However, the role of ferroptosis-related lncRNA (frlncRNA) in oral squamous cell carcinoma (OSCC) is not yet clear.

Materials and Methods: The data of OSCC patients were downed from The Cancer Genome Atlas (TCGA). After univariate and multivariate Cox regression analysis, the prognosis-related ferroptosis-related lncRNAs were obtained to construct a prognostic model. Calculated the risk score to divide patients into high and low risk groups, and evaluated the predictive ability of the model and the differential expression of immunity in the high and low risk groups.

Results: The prognostic model for OSCC was constructed based on 8 prognostic-related frlncRNAs which co-expressed with 25 mRNAs. Kaplan-Meier analyses displayed that the risk score is inversely proportional to patient survival. Receiver operating characteristic (ROC) and decision curve analysis (DCA) indicated that the risk score is superior to other clinical characteristics, and independent prognostic analysis demonstated that risk score is independent factor for the overall survival (OS) rate. The results of immunological analysis showed differences in immune cells, functions, immune checkpoints, and m6A expression between high and low risk groups.

Conclusion: We constructed an OSCC patients prognosis model based on 8 frlncRNAs, which can provide prognostic evaluation and immune analysis for OSCC patients, and provided new direction for OSCC targeted therapy.

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

Background

Pyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.

Methods

Based on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan-Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs.

Results

A total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation.

Conclusion

The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.

Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.

Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.

Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.

Silencing of LINC01963 enhances the chemosensitivity of prostate cancer cells to docetaxel by targeting the miR-216b-5p/TrkB axis

Docetaxel (DTX) treatment effectively prolongs the overall survival of patients with prostate cancer. However, most patients eventually develop resistance to chemotherapy and experience tumor progression or even death. Long noncoding RNAs (lncRNAs) affect docetaxel chemosensitivity. However, the biological role and regulatory mechanisms of lncRNAs in docetaxel-resistant prostate cancer remain unclear. Differences in lncRNAs were evaluated by lncRNA sequencing and evaluated using quantitative real-time polymerase chain reaction, and TrkB expression was measured through western blot analysis. Proliferation was measured using the MTS, while apoptosis and cell cycle were measured using flow cytometry. In addition, migration and invasion were measured using transwell assays. Forty-eight female BALB/c nude mice were used for subcutaneous tumorigenicity and lung metastasis assays. We found that LINC01963 was overexpressed in the PC3-DR cells. LINC01963 silencing enhanced the chemosensitivity of PC3-DR to docetaxel and inhibited tumorigenicity and lung metastasis, while LINC01963 overexpression enhanced the chemoresistance of PC3 cells to docetaxel. It was found that LINC01963 bind to miR-216b-5p. The miR-216b-5p inhibitor reversed the suppressive effect of sh-LINC01963 on PC3-DR cell proliferation, migration, and invasion. Furthermore, miR-216b-5p can bind to the 3'-UTR of NTRK2 and inhibit TrkB protein levels. TrkB enhances docetaxel resistance in prostate cancer and reverses the effects of LINC01963 silencing and miR-216b-5p overexpression. In conclusion, silencing LINC01963 inhibited TrkB protein level to enhance the chemosensitivity of PC3-DR to docetaxel by means of competitively binding to miR-216b-5p. This study illustrates that LINC01963 is a novel therapeutic target for treating prostate cancer patients with DTX resistance.

Construction of a ceRNA Network and Analysis of Tumor Immune Infiltration in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

Construction and Validation of a Novel Ferroptosis-Related lncRNA Signature to Predict Prognosis in Colorectal Cancer Patients

Background: Colorectal cancer (CRC) ranks as the third most common malignancy worldwide but a reliable prognostic biomarker of CRC is still lack. Thus, the purpose of our study was to explore whether ferroptosis - related lncRNAs could predict the prognosis of CRC. Methods: The mRNA expression profiling of colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) patients in The Cancer Genome Atlas (TCGA) database were downloaded. Univariate Cox and multivariate Cox regression analyses was used to obtain prognostic differently expressed ferroptosis-related lncRNAs (DE-FLs) and a risk signature was developed. Quantitative polymerase chain reaction (q-PCR) was used to validated the different expressions of DE-FLs. The calibration curves, C-index and the receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of nomogram. Gene set enrichment analyses (GSEA) were carried out to explore the biological mechanism between high- and low-risk group and the potential regulated pathway of prognostic DE-FLs in CRC. Results: Forty-nine DE-FLs were identified between CRC and normal tissue. Then, a 4-DE-FLs (AC016027.1, AC099850.3, ELFN1-AS1, and VPS9D1-AS1) prognostic signature model was generated. AC016027.1 was downregulated in CRC tissue; VPS9D1-AS1 and ELFN1-AS1 were upregulated by q-PCR. The model had a better accuracy presenting by 1-, 3-, and 5-years ROC curve (AUC ≥0.6), and identified survival probability (p < 0.05) well. Moreover, the risk signature could play as an independent factor of CRC (p < 0.05). Further, a nomogram including age, pathologic stage, T stage, and risk score with good prognostic capability (C-index = 0.789) was constructed. In addition, we found biological pathways mainly related to metabolism and apoptosis were down-regulated in high-risk group who with poor outcome. Finally, the functional enrichment showed prognostic DE-FLs may significantly impact bile secretion in CRC. Conclusion: A risk model and nomogram based on ferroptosis-related lncRNAs were created to predict 1-, 3-, and 5-years survival probability of CRC patients. Our data suggested that the prognostic lncRNAs could serve as valuable prognostic marker.

Identification of Autophagy- and Ferroptosis-Related lncRNAs Functioned through Immune-Related Pathways in Head and Neck Squamous Carcinoma

The interplay between autophagy and ferroptosis has been highlighted as an important event to decide cancer cell fate. However, the underlying mechanisms remain largely unclear. In this study, we systematically explored the expression, prognostic value and functional roles of lncRNA in autophagy and ferroptosis. By a set of bioinformatics analyses, we identified 363 autophagy- and ferroptosis-related lncRNAs (AF-lncRNAs) and found 17 of them are dramatically related to the prognosis of head and neck squamous cell carcinoma (HNSC) patients, named as prognosis-related AF-lncRNAs (PAF-lncRNAs). Based on six key PAF-lncRNAs, a risk score model was developed and used to categorize the TCGA-retrieved HNSC patients into two groups (high-risk vs. low-risk). Functional analysis showed the differentially expressed genes (DEGs) between the two groups were mainly enriched in immune-related pathways and regulated by a PAF-lncRNA-directed ceRNA (competitive endogenous RNA) network. Combined with a variety of immune infiltration analyses, we also found a decreased landscape of immune cell infiltration in high-risk groups. Together, by revealing PAF-lncRNAs with tumor prognostic features functioned through immune-related pathways, our work would contribute to show the pathogenesis of a lncRNA-directed interplay among autophagy, ferroptosis and tumor immunity in HNSC and to develop potential prognostic biomarkers and targets for tumor immunotherapy.