Long non-coding RNAs (lncRNAs) play a significant role in maintaining tissue morphology and functions, and their precise regulatory effectiveness is closely related to expression patterns. However, the spatial expression patterns of lncRNAs in humans are poorly characterized. Here, we constructed five comprehensive transcriptomic atlases of human lncRNAs covering thousands of major tissue samples in normal and disease states. The lncRNA transcriptomes exhibited high consistency within the same tissues across resources, and even higher complexity in specialized tissues. Tissue-elevated (TE) lncRNAs were identified in each resource and robust TE lncRNAs were refined by integrative analysis. We detected 1 to 4684 robust TE lncRNAs across tissues; the highest number was in testis tissue, followed by brain tissue. Functional analyses of TE lncRNAs indicated important roles in corresponding tissue-related pathways. Moreover, we found that the expression features of robust TE lncRNAs made them be effective biomarkers to distinguish tissues; TE lncRNAs also tended to be associated with cancer, and exhibited differential expression or were correlated with patient survival. In summary, spatial classification of lncRNAs is the starting point for elucidating the function of lncRNAs in both maintenance of tissue morphology and progress of tissue-constricted diseases.
Humans ; Gene Expression Profiling ; Neoplasms/genetics* ; Organ Specificity ; RNA, Long Noncoding/genetics* ; Transcriptome

Objective     To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. Methods    The patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results     A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion     The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.