Kidney Renal Clear Cell Carcinoma (KIRC) is a significant cause of cancer-related deaths.Here, we aim to identify the LncRNAs associated with the immune system and characterise their clinical utility in KIRC. A total of 504 patients' data was used from TCGA-GDC. In silico correlation analysis identified 143 LncRNAs associated with immunerelated genes (r > 0.7, P < 0.05). K-means consensus method clustered KIRC samples in three immune clusters, namely cluster C1, C2, and C3 based on the expression of 143 immune-related LncRNAs. Kaplan-Meier analysis showed that C3 patients survived significantly worse than the other two clusters (P < 0.0001). A comparison of TCGA miRNA, mRNA cluster with immune cluster showed the independence and robustness of immune clusters (HR = 2.02 and P = 2.12 × 10 −8 ). The GSEA and CIBERSORT analysis showed high enrichment of poorly activated T-cells in C3 patients. To define LncRNA immune prognostic signature, we randomly divided the TCGA sample into discovery and validation sets. By utilising multivariate Cox regression analysis, we identified and validated a seven LncRNA immune prognostic signature score (LIPS score) (HR = 1.43 and P = 2.73 × 10 −6 ) in KIRC. Comparison of LIPS score with all the clinical factors validated its independence and superiority in KIRC prognosis. In summary, we identified LncRNAs associated with the immune system and showed the presence of prognostic subtypes of KIRC patients based on immune-related LncRNA expression. We also identified a novel immune LncRNA based gene-signature for KIRC patients' prognostication. K E Y W O R D Simmune genes, KIRC, long non-coding RNA, prognostic signature, renal clear cell carcinoma
The prognostic signatures play an essential role in the era of personalised therapy for cancer patients including lung adenocarcinoma (LUAD). Long noncoding RNA (LncRNA), a relatively novel class of RNA, has shown to play a crucial role in all the areas of cancer biology. Here, we developed and validated a robust LncRNA‐based prognostic signature for LUAD patients using three different cohorts. In the discovery cohort, four LncRNAs were identified with 10% false discovery rate and a hazard ratio of >10 using univariate Cox regression analysis. A risk score, generated from the four LncRNAs’ expression, was found to be a significant predictor of survival in the discovery and validation cohort (p = 9.97 × 10 −8 and 1.41 × 10 −3, respectively). Further optimisation of four LncRNAs signature in the validation cohort, generated a three LncRNAs prognostic score (LPS), which was found to be an independent predictor of survival in both the cohorts ( p = 1.00 × 10 −6 and 7.27 × 10 −4, respectively). The LPS also significantly divided survival in clinically important subsets, including Stage I ( p = 9.00 × 10 −4 and 4.40 × 10 −2, respectively), KRAS wild‐type (WT), KRAS mutant ( p = 4.00 × 10 −3 and 4.30 × 10 −2, respectively) and EGFR WT ( p = 2.00 × 10 −4). In multivariate analysis LPS outperformed, eight previous prognosticators. Further, individual members of LPS showed a significant correlation with survival in microarray data sets. Mutation analysis showed that high‐LPS patients have a higher mutation rate and inactivation of the TP53 pathway. In summary, we identified and validated a novel LncRNA signature LPS for LUAD.
BackgroundOvarian cancer (OC) causes a significant proportion of cancer‐related deaths in women. Recently, immunotherapy has emerged as a substantial player in cancer treatment. Lymphocyte infiltration, an important indicator of immune activity and disease aggressiveness, can be identified by gene expression profiling of immune‐related genes of tumours which may prove useful in prognosis of patients.AimsThe aim of this study is to identify and validate a novel immune gene‐based prognostic signature for OC.Methods and resultsHere, we extracted the expression of immune‐related genes and performed the Cox regression analysis and identified five genes with significant correlation with survival in training cohort of patients (n = 286). We utilised regression coefficient and expression level of five genes to calculate immune prognostic signature (IPS) score for OC patients. In univariate and multivariate Cox regression analysis with other clinicopathological factors, we showed that IPS is an independent predictor of survival (P value <0.01). More importantly, we utilised 404 patients from TCGA dataset as the validation cohort and validated the survival capability of IPS in the univariate and multivariate analysis (P value <0.001). Interestingly, KM analysis showed a significant difference in survival of patients with high and low IPS score in both datasets (training dataset P value <0.01, validation dataset P value <0.01). Further, we showed that all the five genes are differentially expressed and involved in immune modulation among other pathways. Interestingly, GSEA analysis showed that high IPS patients had low immune activity and activated EMT and other oncogenic pathways.ConclusionIn summary, we have developed and validated robust immune‐related gene‐based prognostic signature to identify the OC patients with high immune activity who can be taken for immunotherapy.
Thallium-201 scintigraphy is a useful tumour imaging modality in cases of thyroid, breast, brain, lung, soft tissue and bone tumours and lymphomas. A T/B ratio of 1.63 ± 0.38 in 3 h-delayed imaging is suggestive of low-grade tumours. For high-grade tumours a ratio of 2.26 ± 0.41 should be considered significant.
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