Xiaodong Yang

PURPOSE: This study used the Surveillance, Epidemiology, and End Results (SEER) data to investigate the changes in incidence, treatment, and survival of lung cancer from 1973 to 2015. PATIENTS AND METHODS: The clinical and epidemiological data of patients with lung cancer were obtained from the SEER database. Joinpoint regression models were used to estimate the rate changes in lung cancer related to incidence, treatment, and survival. RESULTS: From 1973 to 2015, the average incidence of lung cancer was 59.0/100,000 person-years. The incidence increased initially, reached a peak in 1992, and then gradually decreased. A higher incidence rate was observed in males than in females and in black patients than in other racial groups. Since 1985, adenocarcinoma became the most prevalent histopathological type. The surgical rate for lung cancer was about 25%, and treatment with chemotherapy showed an increasing trend, while the radiotherapy rate was in downward trend. The surgical rate for non-small-cell lung cancer (NSCLC) was higher than that for small cell lung cancer (SCLC), while chemotherapy for SCLC far exceeded that for NSCLC. Treatment with chemotherapy and radiotherapy for advanced stage had higher rate than early stage. The 5-year relative survival rate of lung cancer increased with time, but <21%. CONCLUSION: In the past four decades, the lung cancer incidence increased initially and then gradually decreased. Surgical rate experienced a fluctuant reduction, while the chemotherapy rate was in upward trend. The 5-year relative survival rate increased with years, but was still low.

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

Abstract As an early type of lung adenocarcinoma, ground glass nodule (GGN) has been detected increasingly and now accounts for most lung cancer outpatients. GGN has a satisfactory prognosis and its characteristics are quite different from solid adenocarcinoma (SADC). We compared the GGN adenocarcinoma (GGN-ADC) with SADC using the single-cell RNA sequencing (scRNA-seq) to fully understand GGNs. The tumor samples of five patients with lung GGN-ADCs and five with SADCs underwent surgery were digested to a single-cell suspension and analyzed using 10× Genomic scRNA-seq techniques. We obtained 60,459 cells and then classified them as eight cell types, including cancer cells, endothelial cells, fibroblasts, T cells, B cells, Nature killer cells, mast cells, and myeloid cells. We provided a comprehensive description of the cancer cells and stromal cells. We found that the signaling pathways related to cell proliferation were downregulated in GGN-ADC cancer cells, and stromal cells had different effects in GGN-ADC and SADC based on the analyses of scRNA-seq results. In GGN-ADC, the signaling pathways of angiogenesis were downregulated, fibroblasts expressed low levels of some collagens, and immune cells were more activated. Furthermore, we used flow cytometry to isolate the cancer cells and T cells in 12 GGN-ADC samples and in an equal number of SADC samples, including CD4 + T and CD8 + T cells, and validated the expression of key molecules by quantitative real-time polymerase chain reaction analyses. Through comprehensive analyses of cell phenotypes in GGNs, we provide deep insights into lung carcinogenesis that will be beneficial in lung cancer prevention and therapy.

// Soo Ok Lee 1 , Xiaodong Yang 1 , Shanzhou Duan 1 , Ying Tsai 1 , Laura R. Strojny 1 , Peter Keng 1 , Yuhchyau Chen 1 1 Department of Radiation Oncology, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA Correspondence to: Yuhchyau Chen, e-mail: yuhchyau_chen@urmc.rochester.edu Soo Ok Lee, e-mail: soook_lee@urmc.rochester.edu Keywords: non-small cell lung cancer, IL-6, CD133+, cancer stem cells, self-renewal Received: July 01, 2015&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Accepted: November 21, 2015&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Published: December 12, 2015 ABSTRACT We examined IL-6 effects on growth, epithelial-mesenchymal transition (EMT) process, and metastatic ability of CD133+ and CD133&ndash; cell subpopulations isolated from three non-small cell lung cancer (NSCLC) cell lines: A549, H157, and H1299. We developed IL-6 knocked-down and scramble (sc) control cells of A549 and H157 cell lines by lentiviral infection system, isolated CD133+ and CD133&ndash; sub-populations, and investigated the IL-6 role in self-renewal/growth of these cells. IL-6 showed either an inhibitory or lack of effect in modulating growth of CD133&ndash; cells depending on intracellular IL-6 levels, but there was higher self-renewal ability of IL-6 expressing CD133+ cells than IL-6 knocked down cells, confirming the promoter role of IL-6 in CD133+ cells growth. We then examined tumor growth of xenografts developed from CD133+ cells of A549IL-6si vs. A549sc cell lines. Consistently, there was retarded growth of tumors developed from A549IL-6si, CD133+ cells compared to tumors originating from A549sc, CD133+ cells. The effect of IL-6 in promoting CD133+ self-renewal was due to hedgehog (Hhg) and Erk signaling pathway activation and higher Bcl-2/Bcl-xL expression. We also investigated whether IL-6 regulates the EMT process of CD133&minus; and CD133+ cells differently. Expression of the EMT/metastasis-associated molecules in IL-6 expressing cells was higher than in IL-6 knocked down cells. Together, we demonstrated dual roles of IL-6 in regulating growth of CD133&ndash; and CD133+ subpopulations of lung cancer cells and significant regulation of IL-6 on EMT/metastasis increase in CD133+ cells, not in CD133&ndash; cells.