Pan-cancer integrated bioinformatic analysis of RNA polymerase subunits reveal RNA Pol I member CD3EAP regulates cell growth by modulating autophagy.

Transcription is a crucial stage in gene expression. An integrated study of 34 RNA polymerase subunits (RNAPS) in the six most frequent cancer types identified several genetic and epigenetic modification. We discovered nine mutant RNAPS with a mutation frequency of more than 1% in at least one tumor type. POLR2K and POLR2H were found to be amplified and overexpressed, whereas POLR3D was deleted and downregulated. Multiple RNAPS were also observed to be regulated by variations in promoter methylation. 5-Aza-2-deoxycytidine mediated re-expression in cell lines verified methylation-driven inhibition of POLR2F and POLR2L expression in BRCA and NSCLC, respectively. Next, we showed that CD3EAP, a Pol I subunit, was overexpressed in all cancer types and was associated with worst survival in breast, liver, lung, and prostate cancers. The knockdown studies showed that CD3EAP is required for cell proliferation and induces autophagy but not apoptosis. Furthermore, autophagy inhibition rescued the cell proliferation in CD3EAP knockdown cells. CD3EAP expression correlated with S and G2 phase cell cycle regulators, and CD3EAP knockdown inhibited the expression of S and G2 CDK/cyclins. We also identified POLR2D, an RNA pol II subunit, as a commonly overexpressed and prognostic gene in multiple cancers. POLR2D knockdown also decreased cell proliferation. POLR2D is related to the transcription of just a subset of RNA POL II transcribe genes, indicating a distinct role. Taken together, we have shown the genetic and epigenetic regulation of RNAPS genes in most common tumors. We have also demonstrated the cancer-specific function of CD3EAP and POLR2D genes.

Computational and experimental pharmacology to decode the efficacy of Theobroma cacao L. against doxorubicin-induced organ toxicity in EAC-mediated solid tumor-induced mice.

Background and objective: Doxorubicin is extensively utilized chemotherapeutic drug, and it causes damage to the heart, liver, and kidneys through oxidative stress. Theobroma cacao L (cocoa) is reported to possess protective effects against several chemical-induced organ damages and also acts as an anticancer agent. The study aimed to determine whether the administration of cocoa bean extract reduces doxorubicin-induced organ damage in mice with Ehrlich ascites carcinoma (EAC) without compromising doxorubicin efficacy. Methodology: Multiple in vitro methods such as cell proliferation, colony formation, chemo-sensitivity, and scratch assay were carried out on cancer as well as normal cell lines to document the effect of cocoa extract (COE) on cellular physiology, followed by in vivo mouse survival analysis, and the organ-protective effect of COE on DOX-treated animals with EAC-induced solid tumors was then investigated. In silico studies were conducted on cocoa compounds with lipoxygenase and xanthine oxidase to provide possible molecular explanations for the experimental observations. Results: In vitro studies revealed potent selective cytotoxicity of COE on cancer cells compared to normal. Interestingly, COE enhanced DOX potency when used in combination. The in vivo results revealed reduction in EAC and DOX-induced toxicities in mice treated with COE, which also improved the mouse survival time; percentage of lifespan; antioxidant defense system; renal, hepatic, and cardiac function biomarkers; and also oxidative stress markers. COE reduced DOX-induced histopathological alterations. Through molecular docking and MD simulations, we observed chlorogenic acid and 8'8 methylenebiscatechin, present in cocoa, to have the highest binding affinity with lipoxygenase and xanthine oxidase, which lends support to their potential in ameliorating oxidative stress. Conclusion: The COE reduced DOX-induced organ damage in the EAC-induced tumor model and exhibited powerful anticancer and antioxidant effects. Therefore, COE might be useful as an adjuvant nutritional supplement in cancer therapy.

Statistical analysis of the impacts of COVID-19 pandemic on the small and large-scale tourism sectors in developing countries.

The worldwide COVID-19 pandemic has affected the tourism sector by closing borders, reducing both the transportation of tourists and tourist demand. Due to the country-wide lockdown, most activities in the hotel, motel, restaurant, and transportation sectors have been postponed. Consequently, the article investigates four research issues by examining the consequences of global tourism in the private sector before and after COVID-19. As an analytical method, the article suggested qualitative research methodologies to collect information from tourism employees. The opinions of the respondents were gathered through online emails in the questionnaire survey. Further, the article considers people's future desire for specific tourism destinations based on visitor arrivals. Forecasting tourist demand is an essential component of good and efficient tourism management. Consequently, the article proposes an attention-based long short-term memory model for exact demand forecasting. The experimental findings reveal that the model's minimal prediction error accuracy is 0.45%, which indicates that it has a more robust prediction effect, a faster convergence rate, and a greater prediction accuracy. Seasonality has emerged as one of the most distinguishing and defining characteristics of the global tourist business. Accordingly, the article mandated to compare the seasonal and non-seasonal effects of the tourist sector throughout the years 2020-2021. Moreover, Governments must analyse the crises' long-term consequences and, as a result, define the components that constitute government advantages supplied to the tourist sector during the pandemic era. As a result, many governmental policies, especially those about social welfare, may perceive a fresh start during the post-pandemic period, respectively.

Simple diagnosis of cancer by detecting CEA and CYFRA 21-1 in saliva using electronic sensors.

One way of early diagnosis of cancer is by detecting the biomarkers that get introduced into easily accessible body fluids. We report the development of portable and rapid electronic biosensors for quantitative detection of two secretive cancer biomarkers-Carcinoembryonic antigen (CEA) and Cytokeratin fragment 19 (CYFRA 21-1). The reduced graphene oxide (rGO)/ melamine (MEL)/antibodies/ bovine serum albumin (BSA) based devices were tested for 1 pg/mL to 800 ng/mL of CEA and CYFRA 21-1. The responses of the sensors ranged from 7.14 to 59.1% and from 6.18 to 64% for 1 pg/mL to 800 ng/mL CEA and CYFRA 21-1 respectively. A read-out circuit was assembled to develop a portable prototype which was used to assess the concentrations of the two antigens present in saliva samples of 14 subjects. The prototype could accurately discriminate between 9 oral squamous cell carcinoma patients and 5 healthy controls.

An epithelial-mesenchymal plasticity signature identifies two novel LncRNAs with the opposite regulation.

The epithelial to mesenchymal transition (EMT) is crucial for cancer progression and chemoresistance. EMT is a dynamic process with multiple phases that change cell migration and invasion activity. We used pan-cancer expression data to find 14-LncRNAs that had a high correlation with the EMT markers VIM, CDH1, FN1, SNAI1, and SNAI2. The expression of 14 EMT-associated LncRNA, which also showed high cancer specificity, was used to calculate the pan-cancer EMT score. The EMT score was then applied to the 32 cancer types to classify them as epithelial, epithelial-mesenchymal, mesenchymal-epithelial, or mesenchymal tumors. We discovered that the EMT score is a poor prognostic predictor and that as tumor mesenchymal nature increased, patient survival decreased. We also showed that the cell of origin did not influence the EMT nature of tumors. Pathway analysis employing protein expression data revealed that the PI3K pathway is the most crucial in determining the EMTness of tumors. Further, we divided CCLE-cell lines into EMT classes and discovered that mesenchymal cells, which exhibited higher PI3K pathway activation, were more sensitive to PI3K inhibitors than epithelial cells. We identified Linc01615 as a mesenchymal LncRNA whose expression significantly correlated with survival in several cancer types. We showed that Linc01615 is regulated by the TGFß-STAT3 pathway in a feedback loop. Knockdown of Linc01615 inhibited cell proliferation and migration by regulating the PI3K pathway and mesenchymal markers. We also identified RP4-568C11.4 as an epithelial cancer marker. We showed that knocking down RP4-568C11.4 decreased cell growth but not migration. In addition, we discovered that ESR1 regulates RP4-5681C11.4 in breast cancer. Taken together, we have developed a pan-cancer EMT signature. Also, we found two new LncRNAs that have different effects on cancer development and EMT.

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 Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.

Cancer stem cells play an essential role in therapy response and aggressiveness of various cancers, including lung adenocarcinoma (LUAD). Interestingly it also shares many features of embryonic stem cells (ESCs). Recently, long non-coding RNAs (lncRNAs) have emerged as a critical regulator of cell physiology. Here, we used expression data of ESCs, LUAD, and normal lung to identify 198 long non-coding hESC-associated lncRNAs (hESC-lncRNAs). Intriguingly, K-means clustering of hESC-associated lncRNAs identified a subgroup of LUAD patients [undifferentiated LUAD (uLUAD)] with high stem cell-like characteristic, decreased differentiation genes expression, and poor survival. We also observed that the uLUAD patients had overexpression of proteins associated with cell proliferation. Interestingly, uLUAD patients were highly enriched with the stemness-related gene sets, and had higher mutation load. A notable result observed was high infiltration of T cells and a higher level of neopeptides in uLUAD patients, making these patients an optimal candidate for immunotherapy. Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs-based classification, and identified a group of patients with high stem cell-like characteristic in the 10 most common cancer types and CCLE cell lines. These results suggest the conventional role of hESC-lncRNAs in stem cell biology. In summary, we identified a novel subgroup of LUAD patients (uLUAD) using a set of hESC-lncRNAs. The uLUAD patients had high stem cell-like characteristic and reduced survival rate and may be referred for immunotherapy. Furthermore, our analysis also showed the importance of lncRNAs in cancer and cancer stem cells.

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.