Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor

BACKGROUND

Lactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear.

METHODS

Gene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified.

RESULTS

The immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor's development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells.

CONCLUSION

This study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

LncRNA ACVR2B-as1 interacts with ALDOA to regulate the self-renewal and apoptosis of human spermatogonial stem cells by controlling glycolysis activity

Human spermatogonial stem cells (SSCs) have significant applications in reproductive medicine and regenerative medicine because of their great plasticity. Nevertheless, it remains unknown about the functions and mechanisms of long non-coding RNA (LncRNA) in regulating the fate determinations of human SSCs. Here we have demonstrated that LncRNA ACVR2B-as1 (activin A receptor type 2B antisense RNA 1) controls the self-renewal and apoptosis of human SSCs by interaction with ALDOA via glycolysis activity. LncRNA ACVR2B-as1 is highly expressed in human SSCs. LncRNA ACVR2B-as1 silencing suppresses the proliferation and DNA synthesis and enhances the apoptosis of human SSCs. Mechanistically, our ChIRP-MS and RIP assays revealed that ACVR2B-as1 interacted with ALDOA in human SSCs. High expression of ACVR2B-as1 enhanced the proliferation, DNA synthesis, and glycolysis of human SSCs but inhibited their apoptosis through up-regulation of ALDOA. Importantly, overexpression of ALDOA counteracted the effect of ACVR2B-as1 knockdown on the aforementioned biological processes. Collectively, these results indicate that ACVR2B-as1 interacts with ALDOA to control the self-renewal and apoptosis of human SSCs by enhancing glycolysis activity. This study is of great significance because it sheds a novel insight into molecular mechanisms underlying the fate decisions of human SSCs and it may offer innovative approaches to address the etiology of male infertility.

Therapeutic targeting of TNIK in papillary thyroid carcinoma: a novel approach for tumor growth suppression

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy. The pathology of PTC is far from clear. As a kinase that can be targeted, the role of TNIK in PTC has not been investigated. This study was focused on the effects and molecular mechanisms of TNIK in PTC. Both public datasets and clinical specimens were used to verify TNIK expression. The effects of TNIK were investigated in both cell lines and mice models. Transcriptome analysis was used to explore the underlying mechanism of TNIK. Immunofluorescence, wound healing, and qRT-PCR assays were used to validate the mechanism of TNIK in PTC. The therapeutic effects of TNIK inhibitor NCB-0846 were evaluated by flow cytometry, western blot, and subcutaneous xenografts mice. TNIK expression was upregulated in PTC tissues. TNIK knockdown could suppress cell proliferation and tumor growth in no matter cell models or nude mice. The transcriptome analysis, GO enrichment analysis, and GSEA analysis results indicated TNIK was highly correlated with cytoskeleton, cell motility, and Wnt pathways. The mechanistic studies demonstrated that TNIK regulated cytoskeleton remodeling and promoted cell migration. NCB-0846 significantly inhibited TNIK kinase activity, induced cell apoptosis, and activated apoptosis-related proteins in a dose-dependent manner. In addition, NCB-0846 inhibited tumor growth in tumor-bearing mice. In summary, we proposed a novel regulatory mechanism in which TNIK-mediated cytoskeleton remodeling and cell migration to regulate tumor progression in PTC. TNIK is a therapeutic target in PTC and NCB-0846 would act as a novel targeted drug for PTC therapy.

LncRNAs associated with lymph node metastasis in thyroid cancer based on TCGA database

OBJECTIVE

Long non-coding RNA (lncRNA), especially RNA associated with lymph node metastasis, plays an important role in the development of cancer. Identifying metastasis related lncRNAs and exploring their clinical significance can guide the treatment and prognosis of thyroid cancer patients.

METHODS

RNA expression and clinical data of thyroid cancer was derived from The Cancer Genome Atlas (TCGA) database, while the survival data was obtained from the ULCAN database. R language and SPSS software were used to analyze the correlation between lncRNA and lymph node metastasis of thyroid cancer and the lncRNAs associated with lymph node metastasis were screened.

RESULT

10 lncRNAs showed significant differential expression in thyroid cancer with and without lymph node metastasis. Four lncRNAs (LRRC52-AS1, AP002358.1, AC004847.1, and AC254633.1) were overexpressed in metastatic thyroid cancer, while six lncRNAs (SLC26A4-AS1, LINC01886, LINC01789, AF131216.3, AC062015.1, and AL031710.1) were underexpressed. The expression levels of these lncRNAs were associated with the clinical staging of tumors. Cox regression analysis further showed that elevated expression levels of AP002358.1 and LRRC52-AS1 were associated with poor prognosis in patients with thyroid cancer. In addition, analysis of the UALCAN database indicated that these two lncRNAs were significantly overexpressed in thyroid cancer compared to other cancers, and the expression levels of AF131216.3 and AL031710.1 were associated with progression-free survival in thyroid cancer patients.

CONCLUSION

These lncRNAs may play crucial roles in the development and progression of thyroid cancer and could serve as potential markers for predicting tumor metastasis, clinical stage, and patient prognosis.

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.

Development and validation of cuproptosis-related gene signature in the prognostic prediction of liver cancer

Liver cancer is a generic term referring to several cancer types arising from the liver. Every year, liver cancer causes lots of deaths and other burdens to the people all over the world. Though the techniques in the diagnosis and therapy of liver cancer have undergone significant advances, the current status of treating liver cancer is not satisfactory enough. The improvement of techniques for the prognosis of liver cancer patients will be a great supplement for the treatment of liver cancer. Cuproptosis is a newly identified regulatory cell death type, which may have a close connection to liver cancer pathology. Here, we developed a prognostic model for liver cancer based on the cuproptosis-related mRNAs and lncRNAs. This model can not only effectively predict the potential survival of liver cancer patients, but also be applied to evaluate the infiltration of immune cell, tumor mutation burden, and sensitivity to anti-tumor drugs in liver cancer. In addition, this model has been successfully validated in lots of liver cancer patients’ data. In summary, we wish this model can become a helpful tool for clinical use in the therapy of liver cancer.

N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.

Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.

Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.

Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Identification of immune-related lncRNA panel for predicting immune checkpoint blockade and prognosis in head and neck squamous cell carcinoma

Purpose

Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first‐line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non‐coding RNAs (lncRNAs) impact every step of cancer immunity. Therefore, reliable immune‐lncRNAs able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management.

Methods

In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Atlas and identified differentially expressed immune‐related lncRNAs (DEir‐lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir‐lncRNAs to construct optimal risk model. Low‐risk and high‐risk groups were classified based on the optimal cut‐off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan–Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, and chemotherapeutic and immunotherapeutic efficacy between two groups.

Results

In our study, we identified 256 Deir‐lncRNAs in HNSCC. A total of 18 Deir‐lncRNA pairs (consisting of 35 Deir‐lncRNAs) were used to construct a risk model significantly associated with survival of HNSCC. Cox proportional hazard regression analysis confirmed that our risk model could be served as an independent prognostic indicator. Besides, HNSCC patients with low‐risk score significantly enriched of CD8⁺ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity.

Conclusion

Our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

An autophagy-related lncRNA prognostic risk model for thyroid cancer

PURPOSE

Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long non-coding RNA (lncRNA) in thyroid cancer and explored its prognostic value.

METHODS

The data used in this study were all obtained from The Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb). We construct a co-expression network by autophagy-related genes and lncRNA to obtain autophagy-related lncRNAs. After univariate Cox regression analysis and multivariate Cox regression analysis, autophagy-related lncRNAs significantly associated with prognosis were identified. Based on the risk score of lncRNA, thyroid cancer patients are divided into high-risk group and low-risk group.

RESULTS

A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the TCGA database and the HADb, respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1,166 autophagy-associated lncRNAs. Subsequently, we conducted univariate Cox regression analysis and multivariate Cox regression analysis, nine autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471 and AL162231.2) significantly associated with prognosis were identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer.

CONCLUSION

In this study, nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.

Characterization of Immune-Related Long Non-coding RNAs to Construct a Novel Signature and Predict the Prognosis and Immune Landscape of Soft Tissue Sarcoma

Background: Increasing evidence has demonstrated that immune-related long non-coding RNAs (irlncRNAs) are critically involved in tumor initiation and progression and associated with the prognosis of various cancers. However, their role in soft tissue sarcoma (STS) remains significantly uninvestigated.

Materials and Methods: Gene expression profiles were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) for the identification of irlncRNAs. Univariate analysis and modified least absolute shrinkage and selection operator (LASSO) penalized regression analysis were employed to determine differently expressed irlncRNA (DEirlncRNA) pairs of prognostic value, and subsequently, a risk signature based on DEirlncRNA pairs was established. Furthermore, Kaplan–Meier analysis and the area under the receiver operating characteristic curve (AUC) were used to assess survival differences and the predictive accuracy of the risk signature, respectively. Lastly, the correlation of irlncRNAs with immune characteristics and chemosensitivity in patients with STS were further investigated.

Results: A total of 1088 irlncRNAs were identified, and 311 irlncRNAs were distinguished as DEirlncRNAs. A total of 130 DEirlncRNA pairs were further identified as prognostic markers, and 14 pairs were selected for establishing a risk signature. The irlncRNA-based risk signature functioned as an independent prognostic marker for STS. Compared with the patients in the high-risk group, those in the low-risk group exhibited a better prognosis and were more sensitive to several chemotherapeutic agents. In addition, the irlncRNA-based risk signature was significantly associated with immune scores, infiltrating immune cells, and the expression of several immune checkpoints.

Conclusion: In conclusion, our data revealed that the irlncRNA-based risk signature resulted in reliable prognosis, effectively predicted the immune landscape of patients with STS and was significantly correlated with chemosensitivity, thus providing insights into the potential role of irlncRNAs as prognostic biomarkers and novel therapeutic targets for STS.

Aberrant expression of PROS1 correlates with human papillary thyroid cancer progression

Background

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). Considering the important association between cellular immunity and PTC progression, it is worth exploring the biological significance of immune-related signaling in PTC.

Methods

Several bioinformatics tools, such as R software, WEB-based Gene SeT AnaLysis Toolkit (WebGestalt), Database for Annotation, Visualization and Integrated Discovery (DAVID), Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to identify the immune-related hub genes in PTC. Furthermore, in vitro experiments were adopted to identify the proliferation and migration ability of PROS1 knockdown groups and control groups in PTC cells.

Results

The differentially expressed genes (DEGs) of five datasets from Gene Expression Omnibus (GEO) contained 154 upregulated genes and 193 downregulated genes, with Protein S (PROS1) being the only immune-related hub gene. Quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) have been conducted to prove the high expression of PROS1 in PTC. Moreover, PROS1 expression was significantly correlated with lymph nodes classification. Furthermore, knockdown of PROS1 by shRNAs inhibited the cell proliferation and cell migration in PTC cells.

Conclusions

The findings unveiled the clinical relevance and significance of PROS1 in PTC and provided potential immune-related biomarkers for PTC development and prognosis.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.