Transcriptomic model-based lncRNAs and mRNAs serve as independent prognostic indicators in head and neck squamous cell carcinoma

Role of Long Non-Coding RNA in Regulating ER Stress Response to the Progression of Diabetic Complications

Chronic hyperglycemia damages the nerves and blood vessels, culminating in other vascular complications. Such complications enhance cytokine, oxidative and endoplasmic reticulum (ER) stress. ER is the primary organelle where proteins are synthesised and attains confirmatory changes before its site of destination. Perturbation of ER homeostasis activates signaling sensors within its lumen, the unfolded protein response (UPR) that orchestrates ER stress and is extensively studied. Increased ER stress markers are reported in diabetic complications in addition to lncRNA that acts as an upstream marker inducing ER stress response. This review focuses on the mechanisms of lncRNA that regulate ER stress markers, especially during the progression of diabetic complications. Through this systemic review, we showcase the dysfunctional lncRNAs that act as a leading cause of ER stress response to the progression of diabetic complications.

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value ~10-8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value ~10-6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value ~10-9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-value ~10-15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

LINC00958 and HOXC13-AS as key candidate biomarkers in head and neck squamous cell carcinoma by integrated bioinformatics analysis

Background

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with a strong tendency for metastasis and recurrence. Finding effective biomarkers for the early diagnosis of HNSCC is critical for the early treatment and prognosis of patients.

Methods

RNA sequencing data including long non-coding RNAs (lncRNAs), messenger RNA (mRNAs) and microRNAs (miRNAs) of 141 HNSCC and 44 adjacent normal tissues were obtained from the TCGA. Differentially expressed genes were analyzed using the R package DESeq. GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. A competing endogenous RNAs (ceRNA) network was constructed. The most differentially expressed genes in the main ceRNA network were chosen for nasopharyngeal carcinoma (NPC) cell lines and NPEC2 Bmi-1 cell line verification. A receiver operating characteristic (ROC) curve was constructed for 141 specimens of HNSCC tissues from 44 control samples.

Results

In our study, 79 HNSCC-associated abnormally expressed lncRNAs , 86 abnormally expressed miRNAs and 324 abnormally expressed mRNAs were identified. The public microarray results showed that LINC00958 and HOXC13-AS expression levels were upregulated in HNSCC tissues compared with the adjacent normal tissues in this study (p < 0.0001). LINC00958 and HOXC13-AS expression levels in NPC cell lines were higher than those in the NPEC2 Bmi-1 cell line (p < 0.05). The results showed that the area under the ROC curve (AUC) of LINC00958 reached up to 0.906 at a cutoff value of 7.96, with a sensitivity and specificity of 80.85% and 90.91%, respectively. The AUC of HOXC13-AS reached up to 0.898 at a cutoff value of 0.695, with sensitivity and specificity values of 86.23% and 83.78%, respectively.

Conclusion

The current study indicates that LINC00958 and HOXC13-AS are new candidate diagnostic biomarkers for HNSCC patients.