SPINK5 is a Prognostic Biomarker Associated With the Progression and Prognosis of Laryngeal Squamous Cell Carcinoma Identified by Weighted Gene Co-Expression Network Analysis

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BACKGROUND

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.

METHODS

Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.

RESULTS

The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.

CONCLUSION

The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Differentially Expressed Genes, miRNAs and Network Models: A Strategy to Shed Light on Molecular Interactions Driving HNSCC Tumorigenesis

Head and neck squamous cell carcinoma (HNSCC) is among the most common cancer worldwide, accounting for hundreds thousands deaths annually. Unfortunately, most patients are diagnosed in an advanced stage and only a percentage respond favorably to therapies. To help fill this gap, we hereby propose a retrospective in silico study to shed light on gene-miRNA interactions driving the development of HNSCC. Moreover, to identify topological biomarkers as a source for designing new drugs. To achieve this, gene and miRNA profiles from patients and controls are holistically reevaluated using protein-protein interaction (PPI) and bipartite miRNA-target networks. Cytoskeletal remodeling, extracellular matrix (ECM), immune system, proteolysis, and energy metabolism have emerged as major functional modules involved in the pathogenesis of HNSCC. Of note, the landscape of our findings depicts a concerted molecular action in activating genes promoting cell cycle and proliferation, and inactivating those suppressive. In this scenario, genes, including VEGFA, EMP1, PPL, KRAS, MET, TP53, MMPs and HOXs, and miRNAs, including mir-6728 and mir-99a, emerge as key players in the molecular interactions driving HNSCC tumorigenesis. Despite the heterogeneity characterizing these HNSCC subtypes, and the limitations of a study pointing to relationships that could be context dependent, the overlap with previously published studies is encouraging. Hence, it supports further investigation for key molecules, both those already and not correlated to HNSCC.

Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers worldwide. Although, the researches that attempted to construct a diagnostic model were deficient in HNSCC. Currently, the gold standard for diagnosing head and neck tumors is pathology, but this requires a traumatic biopsy. There is still a lack of a noninvasive test for such a high-incidence tumor. In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized. The data of HNSCC gene expression was accessed from Gene Expression Omnibus (GEO) database; we selected three datasets totally, and we combined 2 datasets (GSE6631 and GSE55547) for screening differentially expressed genes (DEGs) and chose another dataset (GSE13399) for validation. Firstly, the 6 DEGs (CRISP3, SPINK5, KRT4, MMP1, MAL, SPP1) were screened by RF. Subsequently, ANN was applied to calculate the weights of 6 genes. Besides, we created a diagnostic model and nominated it as neuralHNSCC, and the performance of neuralHNSCC by area under curve (AUC) was verified using another dataset. Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort. Furthermore, we used the Cell-type Identification using Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to investigate the difference in immune cell infiltration between HNSCC and normal tissues initially. The selected 6 DEGs and the constructed novel diagnostic model of HNSCC would make contributions to the diagnosis.

Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are particularly important for tumor cell growth and migration, and recurrence and drug resistance, including head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to explore stemness-related lncRNAs (SRlncRNAs) that could be used for prognosis of patients with HNSCC. HNSCC RNA sequencing data and matched clinical data were obtained from TCGA database, and stem cell characteristic genes related to HNSCC mRNAsi were obtained from the online database by WGCNA analysis, respectively. Further, SRlncRNAs were obtained. Then, the prognostic model was constructed to forecast patient survival through univariate Cox regression and LASSO-Cox method based on SRlncRNAs. Kaplan-Meier, ROC and AUC were used to evaluate the predictive ability of the model. Moreover, we probed the underlying biological functions, signalling pathways and immune status hidden within differences in prognosis of patients. We explored whether the model could guide personalized treatments included immunotherapy and chemotherapy for HNSCC patients. At last, RT-qPCR was performed to analyze the expressions levels of SRlncRNAs in HNSCC cell lines. A SRlncRNAs signature was identified based on 5 SRlncRNAs (AC004943.2, AL022328.1, MIR9-3HG, AC015878.1 and FOXD2-AS1) in HNSCC. Also, risk scores were correlated with the abundance of tumor-infiltrating immune cells, whereas HNSCC-nominated chemotherapy drugs were considerably different from one another. The final finding was that these SRlncRNAs were abnormally expressed in HNSCCCS according to the results of RT-qPCR. These 5 SRlncRNAs signature, as a potential prognostic biomarker, can be utilized for personalized medicine in HNSCC patients.

Cuproptosis-related LncRNA signatures as a prognostic model for head and neck squamous cell carcinoma

Cuproptosis is a novel, distinct form of regulated cell death. However, little is known about the role of cuproptosis-related lncRNAs (CRlncRNAs) in head and neck squamous cell carcinoma (HNSCC). This study aimed to identify a CRlncRNAs signature, explore its prognostic value in HNSCC. RNA-seq data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) database, and cuproptosis-related genes were identified from a search of the relevant candidate-gene literature. Analysis of differentially expressed lncRNAs (DElncRNAs) was performed using the R package "edgeR". The intersection of the lncRNAs between DElncRNAs and CRlncRNAs was obtained using the R package "Venn Diagram". Univariate Cox regression was used to identify cuproptosis-related prognostic lncRNAs. LASSO-Cox method was used to narrow these cuproptosis-related prognostic lncRNAs and construct a prognostic model. Multiple statistical methods were used to evaluate the predictive ability of the model. Moreover, the relationships between the model and immune cell subpopulations, related functions and pathways and drug sensitivity were explored. Then, two risk groups were established according to the risk score calculated by the CRlncRNAs signature included three lncRNAs. In HNSCC patients, the risk score was a better predictor of survival than traditional clinicopathological features. In addition, significant differences in immune cells such as B cells, T cells and macrophages were observed between the two groups. Finally, the high-risk group had a lower IC50 for certain chemotherapeutic agents, such as cisplatin and cetuximab. This 3 CRlncRNAs signature is a powerful prognostic biomarker for predicting clinical outcomes and therapeutic responses in HNSCC patients.