Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma

Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients

PURPOSE

Individualized medicine has become increasingly important in bladder cancer treatment, whereas useful biomarkers for prognostic prediction are still lacking. The current study, therefore, constructed a novel risk model based on pyroptosis- and immune-related long noncoding RNAs (Pyro-Imm lncRNAs) to evaluate the potential prognosis of bladder cancer.

METHODS

Corresponding data of bladder cancer patients were downloaded from the Cancer Genome Atlas (TCGA) database. The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were employed to establish a predictive signature, which was evaluated by receiver operator characteristic (ROC) analysis and Kaplan-Meier analysis. Furthermore, the immune infiltration, immune checkpoints, and responses to chemotherapeutic drugs were analyzed with this model.

RESULTS

Three Pyro-Imm lncRNAs (MAFG-DT, AC024060.1, AC116914.2) were finally identified. Patients in the low-risk group demonstrated a significant survival advantage. The area under the ROC curve (AUC) at 1, 3, and 5 years was 0.694, 0.709, and 0.736 respectively in the entire cohort. KEGG and GO analyses showed that the Wnt pathway plays a crucial role in the high-risk group. The risk score was significantly related to the degree of infiltration of different immune cells, the expression of multiple immune checkpoint genes, and the sensitivity of various chemotherapeutic drugs.

CONCLUSION

This novel signature provides a theoretical basis for cancer immunology and chemotherapy, which might help develop individualized therapy.

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study

BACKGROUND

Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.

METHODS

This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.

RESULTS

The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.

CONCLUSIONS

This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

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.

Pan-cancer analysis reveals IGFL2 as a potential target for cancer prognosis and immunotherapy

Insulin-like growth factor like family member 2 (IGFL2) is a gene in the IGFL family, located on chromosome 19, whose role in cancer is unclear, and the aim of this study was to investigate the relevance of IGFL2 expression, prognosis, immunity, and mutation in pan-cancer. Obtaining information from The Cancer Genome Atlas and The Genotype-Tissue Expression Project (GTEx) databases for expression analysis and combining with The Gene Expression Profile Interaction Analysis database for prognostic aspects. Analysis of immune cell infiltration by TIMER and CIBERSORT algorithms. Calculation of correlation of immune-related genes with IGFL2 expression and tumor mutational burden and microsatellite instability. Mutations and DNA methylation were analyzed using the cBioPortal database and the UALCAN database, and functional enrichment was performed using Gene set enrichment analysis (GSEA). IGFL2 expression is significantly elevated in tumor tissue and high expression has a worse prognosis in most cancers. In immune correlation analysis, it was associated with most immune cells and immune-related genes. In most cancers, IGFL2 methylation is lower and the group with mutations in IGFL2 has a worse prognosis than the normal group. The GSEA analysis showed that IGFL2 was significantly enriched in signaling and metabolism. IGFL2 may be involved in the development of many types of cancer, influencing the course of cancer with different biological functions. It may also be a biomarker for tumor immunotherapy.

Comprehensive Characterization of Immune Landscape Based on Tumor Microenvironment for Oral Squamous Cell Carcinoma Prognosis

Objective: This study aims to identify an immune-related signature to predict clinical outcomes of oral squamous cell carcinoma (OSCC) patients. Methods: Gene transcriptome data of both tumor and normal tissues from OSCC and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA). Tumor Immune Estimation Resource algorithm (ESTIMATE) was used to calculate the immune/stromal-related scores. The immune/stromal scores and associated clinical characteristics of OSCC patients were evaluated. Univariate Cox proportional hazards regression analyses, least absolute shrinkage, and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses were performed to assess the prognostic prediction capacity. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) function annotation were used to analysis the functions of TME-related genes. Results: Eleven predictor genes were identified in the immune-related signature and overall survival (OS) in the high-risk group was significantly shorter than in the low-risk group. An ROC analysis showed the TME-related signature could predict the total OS of OSCC patients. Moreover, GSEA and GO function annotation proved that immunity and immune-related pathways were mainly enriched in the high-risk group. Conclusions: We identified an immune-related signature that was closely correlated with the prognosis and immune response of OSCC patients. This signature may have important implications for improving the clinical survival rate of OSCC patients and provide a potential strategy for cancer immunotherapy.