Genetic and epigenetic landscape of leukocyte infiltration identifies an immune prognosticator in lung adenocarcinoma.

BACKGROUND: Leukocyte infiltration plays an critical role in outcome of various diseases including Lung adenocarcinoma (LUAD). OBJECTIVES: To understand the genetic and epigenetic factors affecting leukocyte infiltration and identification and validation of immune based biomarkers. METHOD: Correlation analysis was done to get the associations of the factors. CIBERSORT analysis was done for immune cell infiltration. Genetic and epigenetic analysis were performed. Cox regression was carried out for survival. RESULTS: We categorized the TCGA-LUAD patients based on Leukocyte fraction (LF) and performed extensive immunogenomic analysis. Interestingly, we showed that LF has a negative correlation with copy number variation (CNV) but not with mutational load. However, several individual genetic mutations, including KRAS and KEAP1, were significantly linked with LF. Also, as expected, patients with high LF showed significantly increased expression of genes involved in leukocyte migration and activation. DNA methylation changes also showed a strong association with LF and regulated a significant proportion of genes associated with LF. We also developed and validated an independent prognostic immune signature using the top six prognostic genes associated with LF. CONCLUSION: Together, we have identified clinical, genetic, and epigenetic variations associated with LUAD LF and developed an immune gene-based signature for disease prognostication.

Development and validation of an immune prognostic signature for ovarian carcinoma.

BACKGROUND: Ovarian 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. AIMS: The aim of this study is to identify and validate a novel immune gene-based prognostic signature for OC. METHODS AND RESULTS: Here, 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. CONCLUSION: In 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.

A novel LncRNA-based prognostic score reveals TP53-dependent subtype of lung adenocarcinoma with poor survival.

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.

PILAR1, a novel prognostic LncRNA, reveals the presence of a unique subtype of lung adenocarcinoma patients with KEAP1 mutations.

Lung Adenocarcinoma (LUAD) is the most common cause of lung cancer-related deaths. Long non-coding RNAs (LncRNAs) play an essential role in cancer development and progression. In this study, we identified PILAR1, a prognostic and overexpressed LncRNA, using multiple independent datasets of LUAD patients. Higher expression of PILAR1 was associated with survival in Dhanasekaran et al. (HR = 2.29, p-value = 0.017), TCGA (HR = 1.51, p-value = 0.017) and KM plotter (HR = 2.67, p-value ≤ 0.0001) cohorts. Mutational landscape of LUAD showed that KEAP1 mutation was exclusively present in PILAR1 expressing samples. Further, knockdown of PILAR1 significantly inhibited cell proliferation, colony formation and migration of A549 cells. Importantly, inhibition of PILAR1 made the A549 cells more sensitive to etoposide. Furthermore, pathway analysis using differentially expressed genes in PILAR1 knockdown cells compared to control cells identified enrichment of DNA repair genes suggesting towards the mechanism of PILAR1 mediated etoposide sensitivity. Taken together, we identified a prognostically robust LncRNA, PILAR1, which also regulates cell growth in lung cancer cells. PILAR1 expression identified a novel subtype of LUAD patients with the exclusive KEAP1 mutation.

Immune associated LncRNAs identify novel prognostic subtypes of renal clear cell carcinoma.

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 immune-related 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.

Decoding the LncRNA transcriptome of esophageal cancer: identification of clinically relevant LncRNAs.

AIM: LncRNAs may act as promising biomarkers in esophageal cancer (EC). Here, we illustrate the LncRNA profile and their clinical relevance in EC. PATIENTS & METHODS: In this study, we utilized the Cancer Genome Atlas RNA-sequencing and clinical data from 186 patients and 13 normal samples. Various statistical and gene set enrichment analysis (GSEA) were performed to identify the biomarkers. RESULTS: In a differential expression analysis, we identified a total of 127 LncRNAs with more differentially expressed in EC compared with normal and showed their function using guilt-by-association analysis. We generated a LncRNAs prognostic signature for EC. Using Cox regression analysis, we showed the prognostic ability of LncRNAs' prognostic signature in training and test-cohort (p-value < 0.01). CONCLUSION: In summary, we explored the LncRNA expression profile and their clinical utility in EC patients.