Identification and potential mechanisms of a 4-lncRNA signature that predicts prognosis in patients with laryngeal cancer

Bioinformatic analysis indicated that STARD4-AS1 might be a novel ferroptosis-related biomarker of oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) stands as the predominant form of oral cancer, marked by a poor prognosis. Ferroptosis, a type of programmed cell death, plays a critical role in the initiation and progression of various cancers. Long non-coding RNAs (lncRNAs) are prominent in modulating cancer development. Nevertheless, the prognostic significance of ferroptosis-related lncRNAs (FRLs) in OSCC remains inadequately explored. This study aims to develop a predictive signature based on FRLs to forecast the prognosis of OSCC patients.

Methods

We gathered expression profiles of FRLs along with clinical data from The Cancer Genome Atlas (TCGA) and FerrDb databases. A prognostic model based on 10 FRLs were constructed using Cox regression analyses with LASSO algorithms, and their predictive power was evaluated. Then, the model was used to investigate functional enrichment, immune landscape, m6A genes, somatic variations, and drug response in different risk cohorts of patients. Finally, the expression and function of STARD4-AS1 (steroidogenic acute regulator protein-related lipid transfer domain containing 4-antisense RNA 1), a potential prognostic marker for OSCC screening based on our bioinformatics analysis, were investigated in vitro.

Results

We developed a signature comprising 10 FRLs to stratify patients into two risk cohorts according to their calculated risk scores. Patients classified as high-risk exhibited significantly poorer prognoses compared to those in the low-risk cohort. Furthermore, survival analysis, patient risk heat plot, and risk curve verified the accuracy of the signature. The role of this signature in OSCC was well investigated using immune microenvironment, mutational, and gene set enrichment analysis (GSEA). Moreover, seven drugs, including cisplatin and docetaxel, were identified as potential treatments for patients with high-risk cancers. In addition, the knockdown of STARD4-AS1 in OSCC cell lines markedly inhibited cell proliferation and migration and induced ferroptosis.

Conclusion

Using this signature may improve overall survival predictions in OSCC, throwing new light on immunotherapies and targeted therapies. Moreover, STARD4-AS1 might regulate the process of ferroptosis and could be used as a novel biomarker of OSCC.

Developing targeted therapies for neuroblastoma by dissecting the effects of metabolic reprogramming on tumor microenvironments and progression

Rationale: Synergic reprogramming of metabolic dominates neuroblastoma (NB) progression. It is of great clinical implications to develop an individualized risk prognostication approach with stratification-guided therapeutic options for NB based on elucidating molecular mechanisms of metabolic reprogramming. Methods: With a machine learning-based multi-step program, the synergic mechanisms of metabolic reprogramming-driven malignant progression of NB were elucidated at single-cell and metabolite flux dimensions. Subsequently, a promising metabolic reprogramming-associated prognostic signature (MPS) and individualized therapeutic approaches based on MPS-stratification were developed and further validated independently using pre-clinical models. Results: MPS-identified MPS-I NB showed significantly higher activity of metabolic reprogramming than MPS-II counterparts. MPS demonstrated improved accuracy compared to current clinical characteristics [AUC: 0.915 vs. 0.657 (MYCN), 0.713 (INSS-stage), and 0.808 (INRG-stratification)] in predicting prognosis. AZD7762 and etoposide were identified as potent therapeutics against MPS-I and II NB, respectively. Subsequent biological tests revealed AZD7762 substantially inhibited growth, migration, and invasion of MPS-I NB cells, more effectively than that of MPS-II cells. Conversely, etoposide had better therapeutic effects on MPS-II NB cells. More encouragingly, AZD7762 and etoposide significantly inhibited in-vivo subcutaneous tumorigenesis, proliferation, and pulmonary metastasis in MPS-I and MPS-II samples, respectively; thereby prolonging survival of tumor-bearing mice. Mechanistically, AZD7762 and etoposide-induced apoptosis of the MPS-I and MPS-II cells, respectively, through mitochondria-dependent pathways; and MPS-I NB resisted etoposide-induced apoptosis by addiction of glutamate metabolism and acetyl coenzyme A. MPS-I NB progression was fueled by multiple metabolic reprogramming-driven factors including multidrug resistance, immunosuppressive and tumor-promoting inflammatory microenvironments. Immunologically, MPS-I NB suppressed immune cells via MIF and THBS signaling pathways. Metabolically, the malignant proliferation of MPS-I NB cells was remarkably supported by reprogrammed glutamate metabolism, tricarboxylic acid cycle, urea cycle, etc. Furthermore, MPS-I NB cells manifested a distinct tumor-promoting developmental lineage and self-communication patterns, as evidenced by enhanced oncogenic signaling pathways activated with development and self-communications. Conclusions: This study provides deep insights into the molecular mechanisms underlying metabolic reprogramming-mediated malignant progression of NB. It also sheds light on developing targeted medications guided by the novel precise risk prognostication approaches, which could contribute to a significantly improved therapeutic strategy for NB.

YTHDC1-Mediated lncRNA MSC-AS1 m6A Modification Potentiates Laryngeal Squamous Cell Carcinoma Development via Repressing ATXN7 Transcription

Laryngeal squamous cell carcinoma (LSCC) has the highest mortality rate among head and neck squamous cell carcinoma. This study was designed to investigate the biological effect of long noncoding RNA (lncRNA) MSC antisense RNA 1 (MSC-AS1) on LSCC development and the underlying mechanism. The expression and prognostic value of lncRNAs in head and neck squamous cell carcinoma were predicted in the bioinformatics tools. The overexpression of MSC-AS1 in LSCC patients predicted a poor prognosis. Depletion of MSC-AS1 using shRNA repressed the malignant phenotype of AMC-HN-8 and TU-177 cells. MSC-AS1, mainly localized in the nucleus, interacted closely with the transcription factor CCCTC-binding factor (CTCF). CTCF played anti-tumor effects in vitro and in vivo. Ataxin-7 (ATXN7) was predicted to be a downstream target of CTCF, whose expression was negatively controlled by MSC-AS1. MSC-AS1 was found to block the expression of CTCF, thereby repressing ATXN7. Finally, MSC-AS1 overexpression in LSCC was governed by YTH domain-containing protein 1 (YTHDC1)-mediated m6A modification. In summary, our research identified the YTHDC1/MSC-AS1/CTCF/ATXN7 axis in LSCC development, which indicated that MSC-AS1 is an attractive biomarker in the LSCC treatment.

LncRNAs exhibit subtype-specific expression, survival associations, and cancer-promoting effects in breast cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer progression, influencing processes such as invasion, metastasis, and drug resistance. Their reported cell type-dependent expression patterns suggest the potential for specialized functions in specific contexts. In breast cancer, lncRNA expression has been associated with different subtypes, highlighting their relevance in disease heterogeneity. However, our understanding of lncRNA function within breast cancer subtypes remains limited, warranting further investigation. We conducted a comprehensive analysis using the TANRIC dataset derived from the TCGA-BRCA cohort, profiling the expression, patient survival associations and immune cell type correlations of 12,727 lncRNAs across subtypes. Our findings revealed subtype-specific associations of lncRNAs with patient survival, tumor infiltrating lymphocytes and other immune cells. Targeting of lncRNAs exhibiting subtype-specific survival associations and expression in a panel of breast cancer cells demonstrated a selective reduction in cell proliferation within their associated subtype, supporting subtype-specific functions of certain lncRNAs. Characterization of HER2 + -specific lncRNA LINC01269 and TNBC-specific lncRNA AL078604.2 showed nuclear localization and altered expression of hundreds of genes enriched in cancer-promoting processes, including apoptosis, cell proliferation and immune cell regulation. This work emphasizes the importance of considering the heterogeneity of breast cancer subtypes and the need for subtype-specific analyses to fully uncover the relevance and potential impact of lncRNAs. Collectively, these findings demonstrate the contribution of lncRNAs to the distinct molecular, prognostic, and cellular composition of breast cancer subtypes.

Comprehensive Analysis of N6-Methylandenosine-Related lncRNAs in Clear Cell Renal Cell Carcinoma: A Correlation With Prognosis, Tumor Progression, and Therapeutic Response

This study aims to develop a prognostic signature based on m6A-related lncRNAs for clear cell renal cell carcinoma (ccRCC). Differential expression analysis and Pearson correlation analysis were used to identify m6A-related lncRNAs associated with patient outcomes in The Cancer Genome Atlas (TCGA) database. Our approach led to the development of an m6A-related lncRNA risk score (MRLrisk), formulated using six identified lncRNAs: NFE4, AL008729.2, AL139123.1, LINC02154, AC124854.1 and ARHGAP31-AS1. Higher MRLrisk was identified as a risk factor for patients' prognosis in ccRCC. Furthermore, an MRLrisk-based nomogram was developed and demonstrated as a reliable tool for prognosis prediction in ccRCC. Enrichment analysis and tumor mutation signature studies were conducted to investigate MRLrisk-related biological phenotypes. The tumor immune dysfunction and exclusion (TIDE) score was employed to infer patients' response to immunotherapy, indicating a negative correlation between high MRLrisk and immunotherapy response. Our focus then shifted to LINC02154 for deeper exploration. We assessed LINC02154 expression in 28 ccRCC/normal tissue pairs and 3 ccRCC cell lines through quantitative real-time polymerase chain reaction (qRT-PCR). Functional experiments, including EdU incorporation, flow cytometry and transwell assays, were performed to assess the role of LINC02154 in ccRCC cell functions, discovering that its downregulation hinders cancer cell proliferation and migration. Furthermore, the influence of LINC02154 on ccRCC cells' sensitivity to Sunitinib was explored using CCK-8 assays, demonstrating that decreased LINC02154 expression increases Sunitinib sensitivity. In summary, this study successfully developed an MRLrisk model with significant prognostic value for ccRCC and established LINC02154 as a critical biomarker and prospective therapeutic target in ccRCC management.

Potential Clinical Role of LncRNA miR503HG in Multiple Myeloma and its Effect on the Proliferation and Adhesion of Myeloma Cells

This study mainly explored the role of lncRNA miR503HG in multiple myeloma and the potential downstream regulatory mechanism affecting disease. Real-time quantitative polymerase chain reaction was used to measure the expression levels of miR503HG and miR-103. A cell counting kit-8 assay was performed to detect cell viability. The concentrations of adhesion-related factors (MUC-1, VCAM-1, ICAM-1) were determined using enzyme-linked immunosorbent assay. The targeting relationship between miR503HG and miR-103 was detected by dual-luciferase reporter assay. The miR503HG expression in peripheral blood of multiple myeloma patients was lower than that of normal healthy individuals and associated with ISS stage and worse overall survival. miR-103 was identified as the downstream target of miR503HG. Upregulation of miR503HG could inhibit cell proliferation and adhesion of multiple myeloma cell lines, which could partially reverse the inhibition of adhesion and proliferation by high expression of miR-103. lncRNA miR503HG expression was downregulated in multiple myeloma and had potential diagnostic/prognostic value. MiR503HG exerts a molecular sponge effect on miR-103 and affects its expression, thus achieving the inhibitory effect on multiple myeloma.

A novel necroptosis-related long non-coding RNA signature predicts prognosis and immune response in cervical cancer patients

BACKGROUND

Necroptosis has been linked to the development of tumors. Long non-coding RNAs (IncRNAs) have been identified as having a major role in numerous biological and pathological procedures. Despite this, the precise role that necroptosis-related lncRNAs (NRLs) have in cervical cancer (CC) and their potential for predicting its prognosis is still to a large extent unclear.

METHODS

Gene expression RNA-sequencing data, mutational data, and clinical profiles for 309 CC patients were obtained from the Cancer Genome Atlas (TCGA) database. The NRLs were then identified with Pearson correlation analysis followed by splitting of the patients into training and validation sets in a 3:2 ratio. Cox and LASSO regression models were performed to construct a cervical cancer prognostic signature based on NRLs. This 5-NRLs signature was then verified by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and nomogram for prognostic prediction. Further, a correlation study between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, tumor mutation burden (TMB), and the sensitivity of chemotherapy drug was conducted. To validate the 5-NRLs, a quantitative reverse transcription polymerase chain reaction (qRT-PCR) was finally performed.

RESULTS

The 5-NRLs signature was designed to accurately predict the prognosis of CC. It consists of AC092153.1, AC007686.3, LINC01281, AC009097.2, and RUSC1-AS1 and was found to be highly predictive using ROC and Kaplan-Meier curves. Furthermore, when analyzed through stratified survival analysis, it was confirmed to be an independent risk factor for prognosis. The nomogram and calibration curves further validated its clinical utility. Moreover, distinct differences between two risk groups were observed when examining immune cell infiltration, immune checkpoint molecules, somatic gene alterations and half-inhibitory concentration of anticancer drug.

CONCLUSIONS

The 5-NRLs signature is a novel and valuable tool for evaluating the prognosis of CC patients, providing clinicians with an informed decision-making framework to formulate tailored treatment plans for their patients.

Basement Membrane-Associated lncRNA Risk Model Predicts Prognosis and Guides Clinical Treatment in Clear Cell Renal Cell Carcinoma

The basement membrane (BM) affects the invasion and growth of malignant tumors. The role and mechanism of BM-associated lncRNAs in clear cell renal cell carcinoma (ccRCC) are unknown. In this study, we identified biomarkers of ccRCC and developed a risk model to assess patient prognosis. We downloaded transcripts and clinical data from the Cancer Genome Atlas (TCGA). Differential analysis, co-expression analysis, Cox regression analysis, and lasso regression were used to identify BM-associated prognostic lncRNAs and create a risk prediction model. We evaluated and validated the accuracy of the model using multiple methods and constructed a nomogram to predict the prognosis of ccRCC. GO, KEGG, and immunity analyses were used to explore differences in biological function. We constructed a risk model containing six BM-associated lncRNAs (LINC02154, IGFL2-AS1, NFE4, AC112715.1, AC092535.5, and AC105105.3). The risk model has higher diagnostic efficiency compared to clinical characteristics and can be used to forecast patient prognoses. We used renal cancer cells and tissue microarrays to verify the expression of lncRNAs in the risk model. We found that knocking down LINC02154 and AC112715.1 could inhibit the invasion ability of renal cancer cells. The risk model based on BM-associated lncRNAs can well predict ccRCC and guide clinical treatment.

Identification of a two metastasis-related prognostic signature in the process of predicting the survival of laryngeal squamous cell carcinoma

Metastasis is a major cause of treatment failure and poor outcomes in cancer patients. The data used in the current study was downloaded from TCGA and GEO databases. Differentially expressed metastasis-related genes were identified and the biological functions were implemented. Kaplan-Meier analysis univariate, and, multivariate Cox regression analyses were performed to identify robust prognostic biomarkers, followed by construction of the risk model and nomogram. Gene set enrichment analysis was performed to identify pathways enriched in low- and high-risk groups. POLR2J3 and MYH11 were treated as prognostic biomarkers in LSCC and the risk model was constructed. Receiver operating characteristic curves revealed the good performance of the risk model. A nomogram with high accuracy was constructed, as evidenced by calibration and decision curves. Moreover, we found that the expressions of POLR2J3 and MYH11 was significantly higher in metastasis tissues compared with those in non-metastasis tissues by RT-qPCR and IHC. Our study identified novel metastasis-related prognostic biomarkers in LSCC and constructed a unique nomogram for predicting the prognosis of LSCC patients. Moreover, we explored the related mechanisms of metastasis-related genes in regulating LSCC.

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.

LncRNA AL161431.1 predicts prognosis and drug response in head and neck squamous cell carcinoma

Background

Long non-coding RNAs (lncRNAs) are increasingly recognized as essential players in various biological processes due to their interactions with DNA, RNA, and protein. Emerging studies have demonstrated lncRNAs as prognostic biomarkers in multiple cancers. However, the prognostic effect of lncRNA AL161431.1 in head and neck squamous cell carcinoma (HNSCC) patients has not been reported.

Methods

In the present study, we conducted a series of analyses to identify and validate the prognostic value of lncRNA AL161431.1 in HNSCC, which included differential lncRNAs screening, survival analysis, Cox regression analysis, time ROCanalysis, nomogram prediction, enrichment analysis, tumor infiltration of immune cells, drug sensitivity analysis, and quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In this study, we performed a comprehensive survival and predictive analysis and demonstrated that AL161431.1 was an independent prognostic factor of HNSCC, for which a high AL161431.1 level indicated poor survival in HNSCC. Functional enrichment analyses found that cell growth and immune-related pathways were significantly enriched in HNSCC, suggesting that AL161431.1 may play a role in tumor development and tumor microenvironment (TME). AL161431.1-related immune cells infiltration analysis demonstrated that AL161431.1 expression is significantly positively associated with M0 macrophages in HNSCC (P<0.001). Using "OncoPredict", we recognized chemotherapy drugs sensitive to the high expression group. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to identify the expression level of AL161431.1 in HNSCC, and the results further validated our findings.

Conclusions

Our findings suggest that AL161431.1 is a reliable prognostic marker for HNSCC and can potentially be an effective therapeutic target.

Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients

Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma.

Integrative analysis to screen novel pyroptosis-related LncRNAs for predicting clinical outcome of glioma and validation in tumor tissue

BACKGROUND

Pyroptosis, also known as inflammatory necrosis, is a programmed cell death that manifests itself as a continuous swelling of cells until the cell membrane breaks, leading to the liberation of cellular contents, which triggers an intense inflammatory response. Pyroptosis might be a panacea for a variety of cancers, which include immunotherapy and chemotherapy-insensitive tumors such as glioma. Several findings have observed that long non-coding RNAs (lncRNAs) modulate the bio-behavior of tumor cells by binding to RNA, DNA and protein. Nevertheless, there are few studies reporting the effect of lncRNAs in pyroptosis processes in glioma.

METHODS

The principal goal of this study was to identify pyroptosis-related lncRNAs (PRLs) utilizing bioinformatic algorithm and to apply PCR techniques for validation in human glioma tissues. The second goal was to establish a prognostic model for predicting the overall survival patients with glioma. Predict algorithm was used to construct prognosis model with good diagnostic precision for potential clinical translation.

RESULTS

Noticeably, molecular subtypes categorized by the PRLs were not distinct from any previously published subtypes of glioma. The immune and mutation landscapes were obviously different from previous subtypes of glioma. Analysis of the sensitivity (IC50) of patients to 30 chemotherapeutic agents identified 22 agents as potential therapeutic agents for patients with low riskscores.

CONCLUSIONS

We established an exact prognostic model according to the expression profile of PRLs, which may facilitate the assessment of patient prognosis and treatment patterns and could be further applied to clinical.

Bioinformatics analysis and experimental validation of cuproptosis-related lncRNA LINC02154 in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is common in urinary system tumors. Cuproptosis is a non-apoptotic cell death pathway. Copper binds to fatty acylated mitochondrial proteins and activates various forms of cell death. LncRNA LINC02154 is significantly highly expressed in cells and tissues of many types of tumors, and the risk signature of LINC02154 in some tumors has been validated for effectiveness.

METHODS

We constructed a risk prognostic signature by obtaining differentially expressed long noncoding RNAs (lncRNAs) associated with ccRCC outcomes and cuproptosis from The Cancer Genome Atlas (TCGA). We used TCGA to construct training and testing sets to analyze the risk signature and the impact of LINC02154, and we performed relevant survival analyses. Tumor mutational burdens were analyzed in different LINC02154 expression groups and risk score groups. We next analyzed the immune microenvironment of LINC20154. We performed LINC20154-related drug sensitivity analyses. We also investigated the cellular function of LINC02154 in the ACHN cell line and performed CCK-8 assay, EdU, wound-healing assay, and Transwell assay. The essential genes FDX1 and DLST of cuproptosis were detected by western blot.

RESULTS

We demonstrated that LINC02154's impact on outcomes was statistically significant. We also demonstrated the association of different ages, genders, stages, and grades with LINC02154 and risk models. The results showed a significant difference in tumor mutation burden between the groups, which was closely related to clinical prognosis. We found differences in immune cells among groups with different levels of LINC02154 expression and significant differences in immune function, immunotherapeutic positive markers, and critical steps of the immune cycle. The sensitivity analysis showed that differential expression of LINC02154 discriminated between sensitivity to axitinib, doxorubicin, gemcitabine, pazopanib, sorafenib, sunitinib, and temsirolimus. This difference was also present in the high-risk group and low-risk group. We demonstrated that the proliferation and migration of t ACHN cells in the LINC02154 knockdown group were inhibited. The western blot results showed that the knockdown of LINC02154 significantly affected the expression of FDX1 and DLST, critical genes of cuproptosis.

CONCLUSION

Finally, we demonstrated that LINC02154 and our constructed risk signature could predict outcomes and have potential clinical value.

Establishment and validation of a redox-related long non-coding RNAs prognostic signature in head and neck squamous cell carcinoma

Reduction and oxidation (redox) reactions occur in living organisms as part of normal cellular metabolism. Here, we established a novel redox-related long non-coding RNAs (rrlncRNAs) signature to predict the prognosis and therapeutic response in Head and neck squamous cell carcinoma (HNSCC). The expression profile and clinical information were obtained from the TCGA project. In total, 10 differently expressed rrlncRNAs associated with prognosis were identified and involved in a prognostic risk score signature by the least absolute shrinkage and selection operator penalized Cox analysis. The area under the receiver operating characteristic curves of the survival rates predicted by the rrlncRNAs signature over one, two, and three years were found to be 0.651, 0.670, and 0.679. Following the completion of the Kaplan-Meier survival study, we discovered that the lower-risk cohort exhibited a much longer overall survival period in contrast with the higher-risk cohort. Univariate and multivariable Cox regression analyses demonstrated that the risk score independently served as a significant predictive factor. GO annotation and KEGG pathway analyses illustrated that the rrlncRNAs signature was strongly associated with immune-related functions as well as signaling pathways. The tumor-infiltrating immune cells, tumor microenvironment, immune-related functions, HLA gene family expression, immune checkpoint genes expression, and somatic variants differed substantially between the low- and high-risk cohorts. Moreover, patients in low-risk group were predicted to present a favorable immunotherapy responsiveness, while in contrast, the high-risk group patients might have a stronger sensitivity to "docetaxel". According to our findings, the rrlncRNAs signature showed an excellent prognosis predictive value and might indicate therapeutic response to immunotherapy in HNSCC.

MNX1-AS1, a c-Myc induced lncRNA, promotes the Warburg effect by regulating PKM2 nuclear translocation

BACKGROUND

Altered glycolysis is the most fundamental metabolic change associated with the Warburg effect. Some glycolytic enzymes such as PKM2, the dominant pyruvate kinase in cancer cells, have been shown to engage in non-glycolytic functions that contribute to tumor metabolism. However, the precise mechanisms are not completely understood.

METHODS

The role of MNX1-AS1 in hepatocellular carcinoma progression was assessed both in vitro and in vivo. Northern blotting, RNA pulldown, mass spectrometry, RNA-binding protein immunoprecipitation, ChIP, luciferase reporter assays, RNA FISH and immunofluorescence staining were used to explore the detail molecular mechanism of MNX1-AS1 in hepatocellular carcinoma (HCC).

RESULTS

Here we dissect how MNX1-AS1, a long non-coding RNA (lncRNA), reinforces the Warburg effect through facilitating the non-glycolytic actions of PKM2 in the cell nucleus. We found that MNX1-AS1 expression was frequently overexpressed in HCC-derived cell lines and tissues compared to their normal hepatic cell counterparts, a finding consistent with its status as pan-cancer expressed lncRNA. In the context of HCC, we show MNX1-AS1 acts as a scaffold to promote interactions between PKM2 and importin α5. In response to EGFR activation, the resulting ternary complex drives the translocation of PKM2 into the nucleus. In consequence, glycolytic pathway components including key mediators of the Warburg effect (LDHA, GLUT1 and PDK1) are upregulated though the coactivator function of PKM2. Manipulating MNX1-AS1 elicited robust effects on glycolysis associated with marked changes in HCC growth in vitro and in xenograft models, indicative of the significant contribution of MNX1-AS1 to tumorigenic phenotypes. Moreover, while MNX1-AS1 expression is driven by c-Myc, its actions associated with PKM2 were shown to be downstream and independent of c-Myc.

CONCLUSIONS

Given the status of MNX1-AS1 as a pan-cancer upregulated lncRNA, this implicitly highlights the potential of targeting MNX1-AS1 to selectively counter the Warburg effect in a range of tumor types.

The prognostic value of tumor mutational burden related 6-gene-based Risk Score in laryngeal cancer patients

Background

Laryngeal cancer (LC) is the second frequent malignant head and neck cancer around world, while LC patients’ prognosis is unsatisfactory. This study aims to investigate the prognostic value of tumor mutation burden (TMB)-related genes in LC.

Methods

LC data was downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. TMB values of all samples were calculated basing on mutation data. The differentially expressed genes (DEGs) between LC samples with distinct TMB were subjected to univariate and LASSO Cox regression analysis to build Risk Score. Immune cell infiltration analysis was conducted in CIBERSORT.

Results

Between high and low TMB LC samples, we identified 210 DEGs. Of which, six optimal genes were included to construct Risk Score, comprising FOXJ1, EPO, FGF5, SPOCK1, KCNF1 and PSG5. High risk LC patients had significantly poorer overall survival than low risk patients. The nomogram model constructed basing on Risk Score and gender showed good performance in predicting LC patients’ survival probability.

Conclusions

The prognostic Risk Score model, basing on six TMB-related genes (FOXJ1, EPO, FGF5, SPOCK1, KCNF1 and PSG5), was a reliable prognostic model to separate LC patients with different prognoses.

Construction of a 10-gene prognostic score model of predicting recurrence for laryngeal cancer

We constructed a prognostic score (PS) model to predict the recurrence risk in patients previously diagnosed with laryngeal cancer (LC). Here the training dataset, consisting of 82 LC samples, was downloaded from The Cancer Genome Atlas (TCGA). The PS model then divided the LC samples into high- and low-risk groups, which predicted well the survival time of LC in three datasets (TCGA dataset: AUC = 0.899; GSE27020: AUC = 0.719; and GSE25727: AUC = 0.662). Therefore, the PS model based on the 10 genes and its nomogram is proposed to help predict the recurrence risk in patients with LC.

Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis

Background

Kidney renal clear cell carcinoma (KIRC) is considered an immunogenic tumor. Cuproptosis is a newly identified copper-induced regulated cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) have emerged as significant players in tumorigenesis and metastasis. However, there is a huge knowledge gap on the prognostic role of cuproptosis-related lncRNAs in KIRC. And, the clinical value of them is still unknown. Here, we aimed to develop a cuproptosis-related lncRNA prognostic signature in KIRC.

Methods

The messenger RNA (mRNA)/lncRNA expression profiles and the clinical information including age, gender, tumor stage, grade, and overall survival (OS) were acquired from The Cancer Genome Atlas (TCGA) database. The included KIRC samples were further randomly assigned into training (n=258) or testing (n=257) data sets. We performed Pearson correlation analysis to identify the cuproptosis-related lncRNAs and then constructed the prognostic signature using Cox regression analysis and LASSO algorithm. Subsequently, Kaplan-Meier survival analysis, a nomogram, and receiver operating characteristic (ROC) curve were performed to assess the predictive performance of the signature. Moreover, the immune characteristics and drug sensitivity related to the signature were also explored.

Results

The signature comprised 7 cuproptosis-related lncRNAs. The patients with a low-risk score had superior OS compared with those with a high-risk score. The survival rates of the high- and low-risk groups were 44.96% and 83.72% (P<0.001). The area under the curve (AUC) value for 1-, 3-, 5-year survival rate reached 0.814, 0.762 and 0.825, respectively. In addition, a nomogram was also generated; the AUC was 0.785 for risk score, higher than that for age (0.593), gender (0.489), grade (0.679), and stage (0.721). The high-risk group had more enriched immune- and tumor-related genes. Patients with low-risk scores were more sensitive to immunotherapy and the small molecular drugs GSK1904529A, tipifarnib, BX-912, FR-180204, and GSK1070916. Meanwhile, the high-risk group tended to be more sensitive to pyrimethamine, MS-275, and CGP-60474.

Conclusions

Collectively, we constructed a cuproptosis-related lncRNA prognostic signature with a higher predictive accuracy compared to multiple clinicopathological parameters, which may provide vital guidance for therapeutic strategies in KIRC. Combination of more prognostic biomarkers may further improve the accuracy.

KIF26B-AS1 Regulates TLR4 and Activates the TLR4 Signaling Pathway to Promote Malignant Progression of Laryngeal Cancer

Laryngeal cancer is one of the highest incidence, most prevalently diagnosed head and neck cancers, making it critically necessary to probe effective targets for laryngeal cancer treatment. Here, real-time quantitative reverse transcription PCR (qRT-PCR) and western blot analysis were used to detect gene expression levels in laryngeal cancer cell lines. Fluorescence in situ hybridization (FISH) and subcellular fractionation assays were used to detect the subcellular location. Functional assays encompassing Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), transwell and wound healing assays were performed to examine the effects of target genes on cell proliferation and migration in laryngeal cancer. The in vivo effects were proved by animal experiments. RNA-binding protein immunoprecipitation (RIP), RNA pulldown and luciferase reporter assays were used to investigate the underlying regulatory mechanisms. The results showed that KIF26B antisense RNA 1 (KIF26B-AS1) propels cell proliferation and migration in laryngeal cancer and regulates the toll-like receptor 4 (TLR4) signaling pathway. KIF26B-AS1 also recruits FUS to stabilize TLR4 mRNA, consequently activating the TLR4 signaling pathway. Furthermore, KIF26B-AS1 plays an oncogenic role in laryngeal cancer via upregulating TLR4 expression as well as the FUS/TLR4 pathway axis, findings which offer novel insight for targeted therapies in the treatment of laryngeal cancer patients.

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma

Background

Several cancers, including lung adenocarcinoma (LUAD), are caused by genes related to necroptosis. However, it is still unknown how necroptosis-related long noncoding RNAs (lncRNAs) may be involved in LUAD. In order to predict the prognosis of LUAD patients and personalize immunotherapy, this study set out to construct a necroptosis-related lncRNA prognostic signature (NLPS).

Methods

The Cancer Genome Atlas (TCGA) database was used to download the LUAD transcriptome data and the associated clinical data. lncRNAs associated with necroptosis were screened using coexpression analysis. An NLPS was built using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The Gene Expression Omnibus (GEO) database's GSE30219 was used to validate the NLPS. The prognostic value of the risk score was assessed using Kaplan-Meier survival, receiver operating characteristic (ROC) Cox regression, multivariate Cox regression, and nomogram analyses. Then, we looked into the differences between the low- and high-risk groups in the tumor immune microenvironment, immunotherapy response, and half-maximal inhibitory concentration (IC50).

Results

The 14 lncRNAs in a novel NLPS were created. With further validation in the GSE30219 dataset, the risk score according to the NLPS was an independent prognostic indicator for LUAD patients. Patients with better overall survival (OS) in the low-risk group, who were characterized by increased immune cell infiltration, tumor mutational burden (TMB), and immunophenoscore (IPS), may have hot tumors and higher immunotherapy response rates. In addition, the risk score was also closely linked to sensitivity to various anticancer medications.

Conclusions

We constructed a novel NLPS that could predict OS and sensitivity to immunotherapy in LUAD patients.

Characterization of fatty acid metabolism-related lncRNAs in lung adenocarcinoma identifying potential novel prognostic targets

Lung adenocarcinoma (LUAD), a malignant respiratory tumor with an extremely poor prognosis, has troubled the medical community all over the world. According to recent studies, fatty acid metabolism (FAM) and long non-coding RNAs (lncRNAs) regulation have shown exciting results in tumor therapy. In this study, the original LUAD patient data was obtained from the TCGA database, and 12 FAM-related lncRNAs (AL390755.1, AC105020.6, TMPO-AS1, AC016737.2, AC127070.2, LINC01281, AL589986.2, GAS6-DT, AC078993.1, LINC02198, AC007032.1, and AL021026.1) that were highly related to the progression of LUAD were finally identified through bioinformatics analysis, and a risk score model for clinical reference was constructed. The window explores the immunology and molecular mechanism of LUAD, aiming to shed the hoping light on LUAD treatment.

LncRNA-ENST00000543604 exerts a tumor-promoting effect via miRNA 564/AEG-1 or ZNF326/EMT and predicts the prognosis of and chemotherapeutic effect in colorectal cancer

Objectives

Colorectal cancer(CRC) is a common malignant tumor. Recent studies have found that lncRNAs play an important role in the occurrence and development of colorectal cancer.

Methods

Based on high-throughput sequencing results of fresh CRC tissues and adjacent tissues, we identified lncRNA-ENST00000543604 (lncRNA 604) as the research object by qRT-PCR in CRC tissues and cells. We explored the mechanism of lncRNA 604 action by using luciferin reporter, qRT-PCR and Western blot assays. Kaplan-Meier survival analysis and a Cox regression model were used to analyze the correlation of lncRNA 604 and its regulatory molecules with the prognosis of and chemotherapy efficacy in CRC patients.

Results

In this study, we found that the expression levels of lncRNA 604 were increased in CRC. LncRNA 604 could promote CRC cell proliferation and metastasis through the miRNA 564/AEG-1 or ZNF326/EMT signaling axis in vivo and in vitro. LncRNA 604 could predict the prognosis of CRC and was an independent negative factor. LncRNA 604 exerted a synergistic effect with miRNA 564 or ZNF326 on the prognosis of CRC. LncRNA 604 could improve chemoresistance by increasing the expression of AEG-1, NF-κB, and ERCC1.

Conclusions

Our study demonstrated that lncRNA 604 could promote the progression of CRC via the lncRNA 604/miRNA 564/AEG-1/EMT or lncRNA 604/ZNF326/EMT signaling axis. LncRNA 604 could improve chemoresistance by increasing drug resistance protein expression.

Cuproptosis-related lncRNA: Prediction of prognosis and subtype determination in clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, accounting for approximately 70% of all RCC cases. Cuproptosis, a novel mechanism of cell death, may be a potential target for intervention in tumor development.

Methods: Cuproptosis-related prognostic lncRNAs were identified by co-expression analysis and univariable Cox regression. Five lncRNA profiles were obtained by LASSO regression analysis, and a model with high accuracy was constructed to assess the prognosis of ccRCC patients based on these cuproptosis-related lncRNAs. Survival analysis and time-dependent ROC curves were performed for the α and β groups, and the results confirmed the high accuracy of the model in predicting the prognosis of ccRCC patients. Immunoassay, principal component analysis (PCA), and drug sensitivity analysis were also performed for different risk categories. Finally, we classified ccRCC patients into two different subtypes by consistent class clustering, and performed immune checkpoint activation, tumor microenvironment analysis, PCA, and drug sensitivity analysis for different subtypes.

Results: We developed a prognostic model using five cuproptosis-associated lncRNAs, which was found to be highly accurate in predicting ccRCC patients’ prognosis. Immunotherapy may be more beneficial to the hyper-risk category and the C2 subtype.

Conclusion: The results of this study confirm that five cuproptosis-associated lncRNAs can be used as potential prognostic markers for ccRCC.

Transcriptomic signature associated with RNA-binding proteins for survival stratification of laryngeal cancer

RNA-binding proteins (RBPs) have been suggested as important prognostic indicators in different human cancers. This study was designed to search the prognostic value of RBPs of laryngeal squamous cell carcinoma (LSCC). Differentially expressed RBPs (DERBPs) were screened via The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify prognostic DERBPs. Expression profiling of training cohort were calculated to develop a transcriptomic signature, which was validated by three independent cohorts (TCGA cohort, GSE65858 cohort and GSE27020 cohort). We identified DERBPs and a set of signatures (GTPBP3, KHDRBS3 and RBM38) were confirmed as prognosis-related hub DERBPs in LSCC, which was also tested and verified by bioinformatics method and molecular biology experiment. The role of immune cell infiltration and drug resistance between subgroups was explored. Furthermore, the risk score based on transcriptomic signature was turned out to be an independent prognostic indicator for LSCC. Finally, a nomogram for further clinical application was established. Our study demonstrated that the transcriptomic signature we constructed could serve as a novel therapeutic target and biomarker for LSCC from the perspective of RBPs.

Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all kidney cancers and has a poor prognosis. Recent studies have shown that copper-dependent, regulated cell death differs from previously known death mechanisms (apoptosis, ferroptosis, and necroptosis) and is dependent on mitochondrial respiration (Tsvetkov et al., Science, 2022, 375 (6586), 1254–1261). Studies also suggested that targeting cuproptosis may be a novel therapeutic strategy for cancer therapy. In ccRCC, both cuproptosis and lncRNA were critical, but the mechanisms were not fully understood. The aim of our study was to construct a prognostic profile based on cuproptosis-associated lncRNAs to predict the prognosis of ccRCC and to study the immune profile of clear cell renal cell carcinoma (ccRCC).

Methods: We downloaded the transcriptional profile and clinical information of ccRCC from The Cancer Genome Atlas (TCGA). Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-associated lncRNAs and to construct a risk prognostic model. In addition, the predictive performance of the model was validated and recognized by an integrated approach. We then also constructed a nomogram to predict the prognosis of ccRCC patients. Differences in biological function were investigated by GO, KEGG, and immunoassay. Immunotherapy response was measured using tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores.

Results: We constructed a panel of 10 cuproptosis-associated lncRNAs (HHLA3, H1-10-AS1, PICSAR, LINC02027, SNHG15, SNHG8, LINC00471, EIF1B-AS1, LINC02154, and MINCR) to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. The cuproptosis-associated lncRNA model had higher diagnostic efficiency compared to other clinical features. The analysis of Immune cell infiltration and ssGSEA further confirmed that predictive features were significantly associated with the immune status of ccRCC patients. Notably, the superimposed effect of patients in the high-risk group and high TMB resulted in shorter survival. In addition, the higher TIDE scores in the high-risk group suggested a poorer outcome for immune checkpoint blockade response in these patients.

Conclusion: The ten cuproptosis-related risk profiles for lncRNA may help assess the prognosis and molecular profile of ccRCC patients and improve treatment options, which can be further applied in the clinic.

[Correlation analysis of Poor Prognosis and Immunotherapy of lncRNAs Related with m 6A Modification in Cervical Cancer]

Objective

To study the correlation between N 6-methyladenosine (m 6A)-modification-associated long non-coding RNAs (lncRNAs) and poor prognosis and immunotherapy in cervical cancer based on data mining of The Cancer Genome Atlas (TCGA) cervical cancer dataset, so as to assess effectively the prognosis of cervical cancer patients and the feasibility of immunotherapy.

Methods

We identified m 6A-modification-associated lncRNAs correlated to the prognosis of cervical cancer by conducting bioinformatics analysis of cervical cancer samples from the TCGA datasets and constructed a prognostic risk model of cervical cancer accordingly.

Results

A total of 343 m 6A-modification-associated lncRNAs were identified from the samples of 304 cervical cancer patients. Univariate Cox regression analysis showed that 26 out of the 343 m 6A-modification-associated lncRNAs were significantly associated with the prognosis of cervical cancer patients. We identified 7 m 6A-modification-associated lncRNAs, including DLEU1, AC099850.4, DDN-AS1, EP300-AS1, AC131159.1, AL441992.2, and AL021707.6 through Lasso regression analysis and then developed a prognostic risk model based on them. According to the Kaplan-Meier survival analysis, cervical cancer patients in the low-risk group exhibited significantly improved overall survival (OS) in comparison with those in the high-risk group ( P<0.001). The area under the curve ( AUC) of receiver operating characteristic (ROC) curve analysis demonstrated the high sensitivity and credibility of the risk model. Multivariate Cox analysis showed that the risk score was an independent prognostic factor of cervical cancer patients. Tumor immune dysfunction and exclusion (TIDE) analysis predicted that the high-risk group would benefit more from immunotherapy. In addition, we found that immune checkpoint PD1 was associated with the expression of m6A-modification-related lncRNAs such as DDN-AS1, and the expression was higher in the high-risk group ( P<0.05).

Conclusion

The prognostic risk model constructed on the basis of the aforementioned 7 m 6A-modification-associated lncRNAs can be used to effectively predict the prognosis of cervical cancer patients and assess the efficacy of immunotherapy targeting PD1.

An immune-related lncRNA pairs signature to identify the prognosis and predict the immune landscape of laryngeal squamous cell carcinoma

Background

Laryngeal squamous cell carcinoma (LSCC) is the most common squamous cell carcinoma. Though significant effort has been focused on molecular pathogenesis, development, and recurrence of LSCC, little is known about its relationship with the immune-related long non-coding RNA (lncRNA) pairs.

Methods

After obtaining the transcriptome profiling data sets and the corresponding clinical characteristics of LSCC patients and normal samples from The Cancer Genome Atlas (TCGA) database, a series of bioinformatic analysis was conducted to select the differently expressed immune-related lncRNAs and build a signature of immune-related lncRNA pairs. Then, the effectiveness of the signature was validated.

Results

A total of 111 LSCC patients and 12 normal samples’ transcriptome profiling data sets were retrieved from TCGA. 301 differently expressed immune-related lncRNAs were identified and 35,225 lncRNA pairs were established. After univariate Cox analysis, LASSO regression and multivariate Cox analysis, 7 lncRNA pairs were eventually selected to construct a signature. The riskscore was computed using the following formula: Riskscore = 0.95 × (AL133330.1|AC132872.3) + (-1.23)  × (LINC01094|LINC02154) + 0.65 × (LINC02575|AC122685.1) + (-1.15)  × (MIR9-3HG|LINC01748) + 1.45 × (AC092687.3|SNHG12) + (-0.87)  × (AC090204.1|AL158166.1) + 0.64 × (LINC01063|Z82243.1). Patients were classified into the high-risk group (> 1.366) and the low-risk group (< 1.366) according to the cutoff value (1.366), which is based on the 5-year riskscore ROC curve. The survival analysis showed that the low-risk group had a better prognosis (P < 0.001). The riskscore was better than other clinical characteristics in prognostic prediction and the area under the curves (AUCs) for the 1-, 3-, and 5-year survivals were 0.796, 0.946, and 0.895, respectively. Combining age, gender, grade, stage, and riskscore, a nomograph was developed to predict survival probability in LSCC patients. Then, the riskscore was confirmed to be related with the content of tumor-infiltration immune cells and the model could serve as a potential predictor for chemosensitivity.

Conclusion

We successfully established a more stable signature of 7 immune-related lncRNA pairs, which has demonstrated a better prognostic ability for LSCC patients and may assist clinicians to precisely prescribe chemo drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09524-1.

LINC02154 promotes the proliferation and metastasis of hepatocellular carcinoma by enhancing SPC24 promoter activity and activating the PI3K-AKT signaling pathway

BACKGROUND

Abnormal expression of long non-coding RNAs (lncRNAs) has been associated with the initiation and progression of hepatocellular carcinoma but, as yet, the clinicopathologic significance and potential role of Linc02154 in HCC remains to be determined. Here, we aimed to investigate the potential role and mode of action of Linc02154 in HCC.

METHODS

The expression of Linc02154 in 20 pairs of HCC/normal tissues and 7 HCC cell lines was detected by qRT-PCR. The localization of Linc02154 in HCC cells was detected using fluorescence in situ hybridization and nuclear-plasma separation assays. MTS, EdU incorporation, colony formation, flow cytometry, scratch wound-healing and transwell assays were performed to assess the role of Linc02154 in HCC cell proliferation, migration and invasion in vitro, and BALB/c nude mice xenografts were used to evaluate its role in vivo. RNA sequencing and Western blotting were used to evaluate the regulatory effect of Linc02154 on SPC24 gene expression. A dual-luciferase reporter assay was used to assess a putative interaction of Linc02154 with the SPC24 promoter.

RESULTS

We identified a new lncRNA, Linc02154, that is highly expressed in HCC cells and tissues of patients with a poor overall survival. Functional experiments revealed that exogenous Linc02154 expression in MHCC-97H and SK-Hep1 cells promoted their proliferation, migration and invasion in vitro and their tumorigenesis in vivo. Using a dual luciferase reporter assay we found that Linc02154 can enhance SPC24 promoter (-500 bp ~ -1000 region) activity. Exogenous over-expression of Linc02154 led to up-regulation of SPC24 by activating PI3K/AKT and its downstream signals, including cell cycle progression and EMT-associated gene expression.

CONCLUSION

Our data suggest that Linc02154 may serve as a valuable biomarker of HCC and as a potential therapeutic target.

LncRNA MNX1-AS1: A novel oncogenic propellant in cancers

To date, recent studies have shown that long non-coding RNAs (lncRNAs) are key players in gene regulation processes involved in cancer pathogenesis. In general, Motor neuron and pancreas homeobox 1-antisense RNA1 (MNX1-AS1) is highly expressed in all cancers as reported so far and exerts oncogenic effects through different mechanisms. In this review, we comprehensively summarize the detailed mechanisms of potential functions of MNX1-AS1 in different cancer types as well as the latest knowledge highlighting the potential of MNX1-AS1 as a therapeutic target for cancer. Aberrant expression of MNX1-AS1 closely correlates with clinicopathological parameters. such as lymphatic metastasis, tumor size, tumor stage, OS and DFS. Thus, MNX1-AS1 can be used as a diagnostic and prognostic biomarker or even a therapeutic prognostic target. This article reviews its function, molecular mechanism and clinical prognosis in various malignancies.

Prognostic significance of a 4-lncRNA glycolysis-related signature in oral squamous cell carcinoma

BACKGROUND/PURPOSE

Oral squamous cell carcinoma (OSCC) is a highly malignant tumor, and the overall survival (OS) time of patients with OSCC varies considerably. This study aimed to identify reliable biomarkers for OSCC and construct a new prognostic signature, which may guide personalized precision treatment.

MATERIALS AND METHODS

Transcriptome array data of 317 patients with OSCC from The Cancer Genome Atlas Project (TCGA) cohort were retrospectively analyzed. Single-sample gene set enrichment analysis (ssGSEA) and univariate Cox regression were performed to identify the prognostic significance of the hallmarks of each tumor in OSCC. Subsequently, lncRNAs related to glycolysis were identified through co-expression analysis. A glycolysis-related prognostic signature was constructed by combining univariate Cox regression, least absolute shrinkage and selection operator (Lasso) regression, and multivariate Cox regression analyses. Additionally, the infiltration of immune cells in OSCC was evaluated based on data from ssGSEA and TIMER databases.

RESULTS

Glycolysis was identified as the main risk factor for OS in a variety of cancer hallmarks. The 4-lncRNA glycolysis prognostic signature could distinguish high and low-risk patients. This risk signature was found to be an independent prognostic risk factor for OSCC, showing good predictive power compared with other clinicopathological indicators. Immune correlation analysis showed that patients in the low-risk group exhibited higher levels of immune cell infiltration.

CONCLUSION

The novel 4-lncRNA prognostic signature can predict the clinical outcome of patients with OSCC well, and it is expected to become a promising prognostic biomarker as well as a potential therapeutic target in the future.

Identification of a Twelve Epithelial-Mesenchymal Transition-Related lncRNA Prognostic Signature in Kidney Clear Cell Carcinoma

Background

Epithelial-mesenchymal transition (EMT) plays a vital role in tumor metastasis and drug resistance. It has been reported that EMT is regulated by several long noncoding RNAs (lncRNAs). We aimed to identify EMT-related lncRNAs and develop an EMT-related lncRNA prognostic signature in kidney renal clear cell carcinoma (KIRC).

Materials and Methods

In total, 530 ccRCC patients with 611 transcriptome profiles were included in this study. We first identified differentially expressed EMT-related lncRNAs. Then, all the samples with transcriptional data and clinical survival information were randomly split into training/test sets at a ratio of 1 : 1. Accordingly, we further developed a twelve differentially expressed EMT-related lncRNA prognostic signature in the training set. Following this, risk analysis, survival analysis, subgroup analysis, and the construction of the ROC curves were applied to verify the efficacy of the signature in the training set, test set, and all patients. Besides, we further investigated the differential immune infiltration, immune checkpoint expression, and immune-related functions between high-risk patients. Finally, we explored the different drug responses to targeted therapy (sunitinib and sorafenib) and immunotherapy (anti-PD1 and anti-CTLA4).

Results

A twelve differentially expressed EMT-related lncRNA prognostic signature performed superior in predicting the overall survival of KIRC patients. High-risk patients were observed with a significantly higher immune checkpoint expression and showed better responses to the targeted therapy and immunotherapy.

Conclusions

Our study demonstrates that the twelve differentially expressed EMT-related lncRNA prognostic signature could act as an efficient prognostic indicator for KIRC, which also contributes to the decision-making of the further treatment.

Immune checkpoints related-LncRNAs can identify different subtypes of lung cancer and predict immunotherapy and prognosis

BACKGROUND

Non-small cell lung cancer is the most common subtype of lung cancer in the world. However, the survival rate of non-small cell lung cancer patients remains low currently. Immune checkpoint and long non-coding RNAs are emerging as critical roles in prognostic significance and the immunotherapeutic response of non-small cell lung cancer. It is critical to discern LncRNAs related with immune checkpoints in patients with Non-small cell lung cancer.

METHODS

In this study, immune checkpoint-linked LncRNAs were determined and achieved by the co-expression analysis. Immune checkpoint-linked LncRNAs with noteworthy prognostic value (P < 0.05) gained were next utilized to separate into two cluster by non-negative matrix factorization (NMF). Univariate and a least absolute shrinkage and selection operator were applied to construct an immune checkpoint-linked LncRNAs model. Kaplan-Meier analysis, Gene Set Enrichment Analysis, and the nomogram were utilized to investigate the LncRNAs model. Lastly, the capability immunotherapy and chemotherapy prediction value of this risk model were also estimated.

RESULTS

The model consisting of ten immune checkpoint-related LncRNAs was acknowledged to be a self-determining predictor of prognosis. Through regrouping the NSCLC patients by this model, difference between them more efficiently on immunotherapeutic response, tumor microenvironment and chemotherapy response could be discovered. This risk model related to the immune checkpoint-based LncRNAs may have an excellent clinical prediction for prognosis and the immunotherapeutic response in patients with NSCLC.

CONCLUSIONS

We performed an integrative analysis of LncRNAs linked with immune checkpoints and emphasized the significance of NSCLC subtypes classification, immune checkpoints related LncRNAs in estimating the tumor microenvironment score, immune cell infiltration of the tumor, immunotherapy, and chemotherapy response.

LncRNA NCK1-AS1 Promotes Malignant Cellular Phenotypes of Laryngeal Squamous Cell Carcinoma via miR-137/NCK1 Axis

Increasing evidence demonstrates that many long noncoding RNAs (lncRNAs) are implicated with the development of laryngeal squamous cell carcinoma (LSCC). As shown by bioinformatics analysis, lncRNA non-catalytic region of tyrosine kinase adaptor protein 1-antisense 1 (NCK1-AS1) is upregulated in tissues of head and neck squamous cell carcinoma. The study aimed to explore the role and mechanism of NCK1-AS1 in LSCC. NCK1-AS1 expression in LSCC cells was evaluated by reverse transcription qPCR. The viability, proliferation, invasion, migration, and apoptosis of LSCC cells with indicated transfection were evaluated by CCK-8 assays, Ethynyl deoxyuridine incorporation assays, Transwell assays, wound healing assays, and TUNEL assays, respectively. Subcellular fractionation assays were performed to evaluate the cellular distribution of NCK1-AS1 and NCK1. NCK1 protein level in LSCC cells with indicated transfection was quantified by western blotting. The binding relation between miR-137 and NCK1-AS1 (or NCK1) were determined using RNA immunoprecipitation assays and luciferase reporter assays. NCK1-AS1 was highly expressed in LSCC cell lines. NCK1-AS1 depletion suppressed LSCC cell viability, proliferation, invasion, and migration while enhancing cell apoptosis. NCK1, an adjacent gene of NCK1-AS1, is also highly expressed in LSCC cells and was positively regulated by NCK1-AS1. Moreover, NCK1-AS1 interact with miR-137 to upregulate NCK1 expression. NCK1 was the downstream target of miR-137 and was negatively correlated to miR-137. In addition, overexpressed NCK1 reversed the suppressive impact of NCK1-AS1 depletion on malignant behaviors of LSCC cells. NCK1-AS1 contributes to LSCC cellular behaviors by upregulating NCK1 via interaction with miR-137.

Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

Development and Validation of an Immune-Related Signature for the Prediction of Recurrence Risk of Patients With Laryngeal Cancer

Objective

Our purpose was to develop and verify an immune-related signature for predicting recurrence risk of patients with laryngeal cancer.

Methods

RNA-seq data of 51 recurrence and 81 non-recurrence laryngeal cancer samples were downloaded from TCGA database, as the training set. Microarray data of 34 recurrence and 75 non-recurrence cancer samples were obtained from GEO dataset, as the validation set. Single factor cox regression was utilized to screen prognosis-related immune genes. After LASSO regression analysis, an immune-related signature was constructed. Recurrence free survival (RFS) between high- and low- recurrence risk patients was presented, followed by ROC. We also evaluated the correlation between immune infiltration and the signature using the CIBERSORT algorithm. The genes in the signature were validated in laryngeal cancer tissues by western blot or RT-qPCR. After RCN1 knockdown, migration and invasion of laryngeal cancer cells were investigated.

Results

Totally, 43 prognosis-related immune genes were identified for laryngeal cancer. Among them, eight genes were used for constructing a prognostic signature. High risk group exhibited a higher recurrence risk than low risk group. The AUC for 1-year was separately 0.803 and 0.715 in the training and verification sets, suggesting its well efficacy for predicting the recurrence. Furthermore, this signature was closely related to distinct immune cell infiltration. RCN1, DNAJA2, LASP1 and IBSP were up-regulated in laryngeal cancer. RCN1 knockdown restrained migrated and invasive abilities of laryngeal cancer cells.

Conclusion

Our findings identify a reliable immune-related signature that can predict the recurrence risk of patients with laryngeal cancer.

Identification and Validation of 17-lncRNA Related to Regulatory T Cell Heterogeneity as a Prognostic Signature for Head and Neck Squamous Cell Carcinoma

Successful eradication of tumors by the immune system depends on generation of antigen-specific T cells that migrate to tumor sites and kill cancerous cells. However, presence of suppressive Treg populations inside tumor microenvironment hinders effector T cell function and decreases antitumor immunity. In this study we independently evaluated and confirmed prognostic signature of 17-Treg-related-lncRNA. Immune cell infiltration analysis using 17-lncRNA signature as a probe, accurately described Treg populations in tumor immune microenvironment. 17-lncRNA signature model predicted prognosis with excellent accuracy in all three cohorts: training cohort (AUC=0.82), testing cohort (AUC=0.61) and total cohort (AUC=0.72). The Kaplan-Meier analysis confirmed that the overall survival of patients in the low-risk group was significantly better than those in the high-risk group(P<0.001). CIBERSORT analysis confirmed that low risk group had higher infiltration of tumor killer CD8 T cells, memory activated CD4 T cells, follicular helper T cells and T cells regulatory (Tregs), and lower expression of M0 macrophages and Mast cells activated. These results indicate that the 17-lncRNA signature is a novel prognostic and support the use of lncRNA as a stratification tool to help guide the course of treatment and clinical decision making in patients at high risk of HNSCC.

Clinical significance of long noncoding RNA MNX1-AS1 in human cancers: a meta-analysis of cohort studies and bioinformatics analysis based on TCGA datasets

MNX1-AS1 expression has been proposed to be abnormally upregulated in multiple human malignancies and be linked with the survival outcome of patients. However, relevant conclusions were yielded based on the limited samples. Therefore, we herein implemented a meta-analysis of the published cohort studies to further decipher the relationship of MNX1-AS1 level to prognosis and clinicopathological features in various cancers. Additionally, using The Cancer Genome Atlas (TCGA) datasets we carried out a bioinformatics analysis to make a further evaluation on the prognostic value of MNX1-AS1 expression. The results of meta-analysis indicated elevated MNX1-AS1 level closely correlated with poorer overall survival (OS) (HR = 1.97, 95% CI, 1.73-2.24; P < 0.00001), and disease-free survival (DFS) (HR = 2.24, 95% CI, 1.48-3.38; P = 0.0001) in cancers, which was confirmed by the bioinformatics analysis. Besides, it was observed the upregulated MNX1-AS1 level was significantly related to invasion depth, disease stage, tumor metastasis, and differentiation. Collectively, high MNX1-AS1 level correlated with poor survival outcome and aggressive clinicopathological characteristics in various cancers, suggesting that MNX1-AS1 may be applied as a prognostic marker and even a therapeutic target. Nevertheless, more high-quality studies designed with a large sample size should be conducted to further determine the clinical role of MNX1-AS1 in specific cancer types.

A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer

Background

Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.

Methods

Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.

Results

We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410–2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.

Conclusions

This risk model was shown to be a novel method for predicting prognosis for GC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08975-2.

Systematic Analysis and Identification of Dysregulated Panel lncRNAs Contributing to Poor Prognosis in Head-Neck Cancer

Head and neck cancer (HNC) is one of the most prevalent cancers worldwide, accounting for approximately 5% of all cancers. While the underlying molecules and their pathogenetic mechanisms in HNC have yet to be well elucidated, recent studies have shown that dysregulation of lncRNAs may disrupt the homeostasis of various biological pathways. However, the understanding of lncRNAs in HNC is still limited by the lack of expression profiling. In the present study, we employed a systematic strategy to identify a panel of lncRNA associated with HNC. A cancer-related lncRNA profile PCR array was screened to explore potential molecules specific for HNC. A total of 55 lncRNAs were found to be dysregulated in HNC cells when compared to normal keratinocytes. Further analysis of the prognostic significance using The Cancer Genome Atlas (TCGA) database revealed 15 lncRNAs highly correlated with overall survival in HNC patients. Additionally, clinical sample expression analysis of the TCGA-HNSC cohort revealed 16 highly dysregulated lncRNAs in HNC, resulting in a combined 31-lncRNA signature panel that could predict prognosis. Validation of these molecules confirmed the considerable level of altered expressions in HNC cells, with XIST, HOXA11-AS, TSIX, MALAT1, WT1-AS, and IPW being the most prominently dysregulated. We further selected a molecule from our panel (XIST) to confirm the validity of these lncRNAs in the regulation of cancer aggressiveness. Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses demonstrated that XIST participated in various cancer-related functions, including cell proliferation and metastasis. XIST silencing with the RNAi technique substantially reduced invasion and migration in several HNC cell lines. Thus, our study defined a 31-lncRNA panel as prognostic signatures in HNC. These perspective results provide a knowledge foundation for further application of these molecules in precision medicine.

Ferroptosis-related lncRNA pairs to predict the clinical outcome and molecular characteristics of pancreatic ductal adenocarcinoma

Ferroptosis is a form of regulated cell death initiated by oxidative perturbations that can be blocked by iron chelators and lipophilic antioxidants, and ferroptosis may be the silver bullet treatment for multiple cancers, including immunotherapy- and chemotherapy-insensitive cancers such as pancreatic ductal adenocarcinoma (PDAC). Numerous studies have noted that long non-coding RNAs (lncRNAs) regulate the biological behaviour of cancer cells by binding to DNA, RNA and protein. However, few studies have reported the role of lncRNAs in ferroptosis processes and the function of ferroptosis-associated lncRNAs. The primary objective of the present study was to identify ferroptosis-related lncRNAs using bioinformatic approaches combined with experimental validation. The second objective was to construct a prognostic model to predict the overall survival of patients with PDAC. The present study identified ferroptosis-related lncRNAs using a bioinformatic approach and validated them in an independent pancreatic cancer cohort from Fudan University Shanghai Cancer Center. The lncRNA SLCO4A1-AS1 was identified as a novel molecule mediating ferroptosis resistance in vitro. A novel algorithm was used to construct a '0 or 1' matrix-based prognosis model, which showed promising diagnostic accuracy for potential clinical translation (area under the curve = 0.89 for the 2-year survival rate). Notably, molecular subtypes classified by the risk scores of the model did not belong to any previously reported subtypes of PDAC. The immune microenvironment, metabolic activities, mutation landscape and ferroptosis sensitivity were significantly distinct between patients with different risk scores. Sensitivity (IC50) to 30 common anticancer drugs was analysed between patients with different risks, and imatinib and axitinib were found to be potential drugs for the treatment of patients with lower risk scores. Overall, we developed an accurate prognostic model based on the expression patterns of ferroptosis lncRNAs, which may contribute greatly to the evaluation of patient prognosis, molecular characteristics and treatment modalities and could be further translated into clinical applications.

The Role of lncRNAs in Rare Tumors with a Focus on HOX Transcript Antisense RNA (HOTAIR)

Rare cancers are identified as those with an annual incidence of fewer than 6 per 100,000 persons and includes both epithelial and stromal tumors from different anatomical areas. The advancement of analytical methods has produced an accurate molecular characterization of most human cancers, suggesting a “molecular classification” that has allowed the establishment of increasingly personalized therapeutic strategies. However, the limited availability of rare cancer samples has resulted in very few therapeutic options for these tumors, often leading to poor prognosis. Long non coding RNAs (lncRNAs) are a class of non-coding RNAs mostly involved in tumor progression and drug response. In particular, the lncRNA HOX transcript antisense RNA (HOTAIR) represents an emergent diagnostic, prognostic and predictive biomarker in many human cancers. The aim of this review is to highlight the role of HOTAIR in rare cancers, proposing it as a new biomarker usable in the management of these tumors.

LncRNA MATN1-AS1 for Prediction of Prognosis in Osteosarcoma Patients and Its Cellular Function

Long non-coding RNAs show essential roles in various cancer processes. This study aimed at the expression features, prognosis significance, and biological effect of lnc MATN1-AS1 in osteosarcoma (OS). Five kinds of cell lines and 117 pairs of tissues were analyzed by qRT-PCR for quantification of lnc MATN1-AS1 and miR-1299 level. Clinical data were analyzed using Chi-Square Tests to show the association with lnc MATN1-AS1 level. Kaplan-Meier analysis and Cox regression were used to judge the prognostic value. Cell counting kit-8 and Transwell assay were conducted, respectively, to analyze the effect of lnc MATN1-AS1 on cell proliferation and metastasis. The target miRNA was predicted. lnc MATN1-AS1 level was significantly elevated in OS cells and tissues and related to Enneking staging, lung metastasis, and histologic type. Patients with high lnc MATN1-AS1 level showed a shorter overall survival and recurrence-free survival. Lnc MATN1-AS1 knockdown inhibited OS cell proliferation, migration, and invasion by sponging miR-1299. Lnc MATN1-AS1 has oncogenic features and prognostic significance in OS and is a novel therapeutic strategy for OS.

Exosomal long non-coding RNAs: Emerging players in cancer metastasis and potential diagnostic biomarkers for personalized oncology

Metastasis is a major challenge in the treatment of cancer. Exosomes are a class of small extracellular vesicles (EVs) that play critical roles in several human diseases, especially cancer, by transferring information (e.g., DNA, RNA, and protein) via cell-to-cell communication. Numerous recent studies have shown that exosomal long non-coding RNAs (lncRNAs) play crucial regulatory roles in cancer metastasis in the tumor microenvironment by altering the expression of several key signaling pathways and molecules. Due to their specificity and sensitivity, exosomal lncRNAs have potential as novel tumor markers and therapeutic targets in the treatment of cancer metastasis. In this review, we aim to summarize the roles of exosomal lncRNAs in cancer metastasis, the mechanisms underlying their roles, and their potential clinical applications.

Exosome-Based Vaccines: History, Current State, and Clinical Trials

Extracellular vesicles (EVs) are released by most cell types as part of an intracellular communication system in crucial processes such as inflammation, cell proliferation, and immune response. However, EVs have also been implicated in the pathogenesis of several diseases, such as cancer and numerous infectious diseases. An important feature of EVs is their ability to deliver a wide range of molecules to nearby targets or over long distances, which allows the mediation of different biological functions. This delivery mechanism can be utilized for the development of therapeutic strategies, such as vaccination. Here, we have highlighted several studies from a historical perspective, with respect to current investigations on EV-based vaccines. For example, vaccines based on exosomes derived from dendritic cells proved to be simpler in terms of management and cost-effectiveness than dendritic cell vaccines. Recent evidence suggests that EVs derived from cancer cells can be leveraged for therapeutics to induce strong anti-tumor immune responses. Moreover, EV-based vaccines have shown exciting and promising results against different types of infectious diseases. We have also summarized the results obtained from completed clinical trials conducted on the usage of exosome-based vaccines in the treatment of cancer, and more recently, coronavirus disease.

A prognostic nomogram based on competing endogenous RNA network for clear-cell renal cell carcinoma

Background

Clear‐cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts in understanding the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) was involved in the development of varied tumors. However, a comprehensive analysis of the prognostic model based on lncRNA‐miRNA‐mRNA ceRNA regulatory network of ccRCC with large‐scale sample size and RNA‐sequencing expression data is still limited.

Methods

RNA‐sequencing expression data were taken out from GTEx database and TCGA database, a total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC‐specific genes were obtained by WGCNA and differential expression analysis. Following, the communication of mRNAs and lncRNAs with targeted miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was further constructed by univariate Cox regression, Lasso methods, and multivariate Cox regression analysis.

Results

A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dysregulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature including eight genes based on this ceRNA was determined followed by the development of a nomogram predicting 1‐, 3‐, and 5‐year survival probability for ccRCC.

Conclusion

It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

LncRNA MNX1-AS1 drives aggressive laryngeal squamous cell carcinoma progression and serves as a ceRNA to target FoxM1 by sponging microRNA-370

Long non-coding RNA (LncRNA) MNX1 antisense RNA 1(MNX1-AS1) is associated with the pathology of numerous cancers. But, the role and underlying pathways of MNX1-AS1 in the regulation of laryngeal squamous cell carcinoma (LSCC) is not known. We demonstrated remarkably elevated levels of MNX1-AS1 in the LSCC tissues, which was correlated with poor disease prognosis. Moreover, MNX1-AS1-silencing strongly suppressed LSCC cell proliferation, migration, and invasion. We also demonstrated that MNX1-AS1 sequesters that activity of miR-370, thereby releasing Forkhead Box ml (FoxM1) from the inhibitory actions of MNX1-AS1. Furthermore, the positive correlation of MNX1-AS1 and FoxM1 as well as the converse correlation between miR-370 and MNX1-AS1 (or FoxM1) were revealed in LSCC tissues using experiments. Based on rescue assays, FoxM1 overexpression or miR-370 downregulation partially recovered the inhibitory effect of MNX1-AS1 silencing on LSCC cells. Moreover, knockdown of MNX1-AS1 retarded tumor growth in nude mice model. In summary, these findings verified that MNX1-AS1 modulated LSCC progression by competitively binding with miR-370 to regulate FoxM1.

High-throughput sequencing profile of laryngeal cancers: analysis of co-expression and competing endogenous RNA networks of circular RNAs, long non-coding RNAs, and messenger RNAs

Background

Circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) have been recently identified as new classes of non-coding RNAs which participate in carcinogenesis and tumor progression. However, the functions of these non-coding RNAs and gene expression patterns are largely unknown.

Methods

We carried out high-throughput sequencing to analyze the differential expression of RNAs in 5 coupled laryngeal cancer (LC) and corresponding adjacent noncancerous tissues. Bioinformatics analyses were performed to predict the functions of these non-coding RNAs via co-expression, competing endogenous RNA networks and pathway enrichment analysis. The differential expression of the selected RNAs were confirmed using RT-qPCR. The CCK8, EDU, Transwell, and wound healing assays were conducted to validate the biological functions of SNHG29 in LC. Western blot assay was performed to identify the effects of SNHG29 having on the epithelial to mesenchymal transition process. Kaplan-Meier analysis was used to investigate whether the expression level of SNHG29 correlated with survival in LC patients. One-way ANOVA was used to analyze the correlation between the expression of SNHG29 and clinicopathological parameters of the included patients.

Results

Compared to normal laryngeal tissues, 31,763 non-coding RNAs were upregulated and 11,557 non-coding RNAs were downregulated in cancer tissues. SNHG29 expression was low in the LC cell lines and tissues predicting a better clinical prognosis. SNHG29 was also found to inhibit the proliferation, migration, and invasion ability of LC, exerting a suppressive role in the epithelial to mesenchymal transition process as well. SNHG29 downregulation was significantly correlated with differentiation (P=0.026), T-stage (P=0.041), lymphatic metastasis (P=0.044), and clinical stage (P=0.037). We found that the biological functions of differentially expressed transcripts included cell adhesion, biological adhesion, and migration and invasion related to adherens junction pathways.

Conclusions

Our study was the first to describe the non-coding RNA profile of LC, and suggested that dysregulated non-coding RNAs could be involved in LC tumorigenesis. SNHG29 was demonstrated to play crucial roles in inhibiting the pathogenesis and progression of LC. Our findings provide a new approach for further analyses of pathogenetic mechanisms, the detection of novel transcripts, and the identification of valuable biomarkers for this tumor.

Identifying biomolecules and constructing a prognostic risk prediction model for recurrence in osteosarcoma

INTRODUCTION

Osteosarcoma is a high-morbidity bone cancer with an unsatisfactory prognosis. The aim of this study is to develop novel potential prognostic biomarkers and construct a prognostic risk prediction model for recurrence in osteosarcoma.

METHODS

By analyzing microarray data, univariate and multivariate Cox regression analyses were performed to screen prognostic RNA signatures and to build a prognostic model. The RNA signatures were validated using Kaplan-Meier curves. Then, we developed and validated a nomogram combining age, recurrence, metastatic, and Prognostic score (PS) models to predict the individual's overall survival at the 3- and 5-year points. Pathway enrichment of RNA was conducted based on the significant co-expressed RNAs.

RESULTS

A total of 319 mRNAs and 14 lncRNAs were identified in the microarray data. One lncRNA (LINC00957) and six mRNAs (METL1, CA9, B3GALT4, ALDH1A1, LAMB3, and ITGB4) were identified as RNA signatures and showed good performances in survival prediction for both the training and validation cohorts. Cox regression analysis showed that the seven RNA signatures could independently predict overall survival. Furthermore, age, recurrence, metastatic, and PS models were identified as independent prognostic factors via univariate and multivariate Cox analyses (P < 0.05) and included in the prognostic nomogram. The C-index values for the 3- and 5-year overall survival predictions of the nomogram were 0.809 and 0.740, respectively.

CONCLUSIONS

The current study provides the novel potential of seven RNA candidates as prognostic biomarkers. Nomograms were constructed to provide accurate and individualized survival prediction for recurrence in osteosarcoma patients.