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      Identification of 12 immune-related lncRNAs and molecular subtypes for the clear cell renal cell carcinoma based on RNA sequencing data

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          Abstract

          Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Despite the existing extensive research, the molecular and pathogenic mechanisms of ccRCC are elusive. We aimed to identify the immune-related lncRNA signature and molecular subtypes associated with ccRCC. By integrating 4 microarray datasets from Gene Expression Omnibus database, we identified 49 immune-related genes. The corresponding immune-related lncRNAs were further identified in the TCGA dataset. 12-lncRNAs prognostic and independent signature was identified through survival analysis and survival difference between risk groups was further identified based on the risk score. Besides, we identified 3 molecular subtypes and survival analysis result showed that cluster 2 has a better survival outcome. Further, ssGSEA enrichment analysis for the immune-associated gene sets revealed that cluster 1 corresponded to a high immune infiltration level. While cluster 2 and cluster 3 corresponded to low and medium immune infiltration level, respectively. In addition, we validated the 12-lncRNA prognostic signature and molecular subtypes in an external validation dataset from the ICGC database. In summary, we identified a 12-lncRNA prognostic signature which may provide new insights into the molecular mechanisms of ccRCC and the molecular subtypes provided a theoretical basis for personalized treatment by clinicians.

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          Cancer statistics, 2020

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.
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            LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma

            Background Oesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles. Objective To assess the lncRNA expression profile of oesophageal squamous cell carcinoma (OSCC) and identify prognosis-related lncRNAs. Method LncRNA expression profiles were studied by microarray in paired tumour and normal tissues from 119 patients with OSCC and validated by qRT-PCR. The 119 patients were divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random Forest supervised classification algorithm and a nearest shrunken centroid algorithm, then validated in a test group and further, in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by multivariable Cox regression analysis. Results LncRNAs showed significantly altered expression in OSCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1) which classified the patients into two groups with significantly different overall survival (median survival 19.2 months vs >60 months, p 60 months, p=0.0030) and independent cohort (median survival 25.8 months vs >48 months, p=0.0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with OSCC. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions Our results suggest that the three-lncRNA signature is a new biomarker for the prognosis of patients with OSCC, enabling more accurate prediction of survival.
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              Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma.

              Our objective was to compare cancer-specific survival and to examine associations with outcome among the histologic subtypes of renal cell carcinoma (RCC). We studied 2385 patients whose first surgery between 1970 and 2000 was a radical nephrectomy for sporadic, unilateral RCC. All RCC tumors were classified following the 1997 Union Internationale Contre le Cancer and American Joint Committee on Cancer guidelines. There were 1985 (83.2%) patients with clear cell, 270 (11.3%) with papillary, 102 (4.3%) with chromophobe, 6 (0.3%) with collecting duct, 5 (0.3%) with purely sarcomatoid RCC and no underlying histologic subtype, and 17 (0.7%) with RCC, not otherwise specified. Cancer-specific survival rates at 5 years for patients with clear cell, papillary, and chromophobe RCC were 68.9%, 87.4%, and 86.7%, respectively. Patients with clear cell RCC had a poorer prognosis compared with patients with papillary and chromophobe RCC (p <0.001). This difference in outcome was observed even after stratifying by 1997 tumor stage and nuclear grade. There was no significant difference in cancer-specific survival between patients with papillary and chromophobe RCC (p = 0.918). The 1997 TNM stage, tumor size, presence of a sarcomatoid component, and nuclear grade were significantly associated with death from clear cell, papillary, and chromophobe RCC. Histologic tumor necrosis was significantly associated with death from clear cell and chromophobe RCC, but not with death from papillary RCC. Our results demonstrate that there are significant differences in outcome and associations with outcome for the different histologic subtypes of RCC, highlighting the need for accurate subtyping.
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                Author and article information

                Contributors
                yaolin@fjnu.edu.cn
                hjy0602@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                2 September 2020
                2 September 2020
                2020
                : 10
                : 14412
                Affiliations
                [1 ]The Fifth Hospital of Xiamen, Xiamen, 361101 Fujian Province People’s Republic of China
                [2 ]GRID grid.411503.2, ISNI 0000 0000 9271 2478, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, College of Life Sciences, Qishan Campus, , Fujian Normal University, ; Fuzhou, 350117 Fujian Province People’s Republic of China
                [3 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, Xiang’an Branch, The First Affiliated Hospital of Xiamen University, , Xiamen University, ; Xiamen, 361101 Fujian Province People’s Republic of China
                [4 ]GRID grid.12955.3a, ISNI 0000 0001 2264 7233, The First Affiliated Hospital of Xiamen University, , Xiamen University, ; Xiamen, 361003 Fujian Province People’s Republic of China
                Article
                71150
                10.1038/s41598-020-71150-3
                7467926
                32879362
                6dc2e128-4751-4aa0-9178-937a45932136
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 December 2019
                : 20 July 2020
                Funding
                Funded by: Xiamen Medical Advantage Subspecialty Vascular Access Construction Fund
                Award ID: ([2018] 296)
                Award Recipient :
                Funded by: Fujian Science and Technology Plan Guiding Projects
                Award ID: 2019D026
                Award Recipient :
                Funded by: Health Science Research Personnel Training Program of Fujian Province
                Award ID: 2017-CXB-22
                Award Recipient :
                Categories
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                © The Author(s) 2020

                Uncategorized
                computational biology and bioinformatics,immunology,biomarkers,oncology,risk factors
                Uncategorized
                computational biology and bioinformatics, immunology, biomarkers, oncology, risk factors

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