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Immune checkpoint inhibitors (ICIs) represent a new hot spot in tumor therapy. Programmed cell death has an important role in the prognosis. We explore a programmed cell death gene prognostic model associated with survival and immunotherapy prediction via computational algorithms. Patient details were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. We used LASSO algorithm and multiple-cox regression to establish a programmed cell death-associated gene prognostic model. Further, we explored whether this model could evaluate the sensitivity of patients to anti-PD-1/PD-L1. In total, 1342 patients were included. We constructed a programmed cell death model in TCGA cohorts, and the overall survival (OS) was significantly different between the high- and low-risk score groups (HR 2.70; 95% CI 1.94–3.75; p < 0.0001; 3-year OS AUC 0.71). Specifically, this model was associated with immunotherapy progression-free survival benefit in the validation cohort (HR 2.42; 95% CI 1.59–3.68; p = 0.015; 12-month AUC 0.87). We suggest that the programmed cell death model could provide guidance for immunotherapy in LUAD patients.

The Golgi apparatus (GA) is a cellular organelle that participates in the packaging, modification, and transport of proteins and lipids from the endoplasmic reticulum to be further fabricated before being presented to other cellular components. Recent studies have demonstrated that GA facilitates numerous cellular processes in cancer development. Therefore, this study aimed to establish a novel lung adenocarcinoma (LUAD) risk evaluation model based on GA gene signatures. In this study, we used TCGA-LUAD (n = 500) as the training cohort and GSE50081 (n = 127), GSE68465 (442), and GSE72094 (398) as the validation cohorts. Two immunotherapy datasets (GSE135222 and GSE126044) were also obtained from a previous study. Based on machine algorithms and bioinformatics methods, a GA gene-related risk score (GARS) was established. We found that the GARS independently predicted the prognosis of LUAD patients and remained effective across stages IA to IIIA. Then, we identified that the GARS was highly correlated with mutations in P53 and TTN. Further, this study identified that GARS is related to multiple immune microenvironmental characteristics. Furthermore, we investigated GSE135222 and GSE126044 and found that a lower GARS may be indicative of an improved therapeutic effect of PD-1/PD-L1 therapy. We also found that high GARS may lead to a better response to multiple anticancer drugs. Finally, we established a nomogram to better guide clinical application. To our knowledge, this is the first study to demonstrate a novel GA signature-based risk score formula to predict clinical prognosis and guide the treatment of LUAD patients.

Homeostatic plasticity encompasses the mechanisms by which neurons stabilize their synaptic strength and excitability in response to prolonged and destabilizing changes in their network activity. Prolonged activity blockade leads to homeostatic scaling of action potential (AP) firing rate in hippocampal neurons in part by decreased activity of N-Methyl-D-Aspartate receptors and subsequent transcriptional down-regulation of potassium channel genes including

Background: PTC is the most common type of thyroid cancer and offers excellent prognosis. However, recurrence PTC remains relatively common, especially PTC with BRAF V600E mutation. PTC is often characterized by RET, RAS or BRAFmutations. RAS, RET and BRAF V600E mutations tend to be mutually exclusive. In this study, we investigated whether these oncogenic driver mutations could propose a molecular classification of PTC into distinct tumor immune microenvironment. Methods: Surgical tissue samples from 252 patients with stage I-III PTC consecutively diagnosed between 07/2020 to 08/2021 at Sun Yat-sen Memorial Hospital (SYMH) were sequenced using a panel targeting 18 cancer-related genes. Clinical and genomic data from SYMH cohort was compared with 499 stage I-III PTC patients (PTCs) from TCGA. Results: Based on the analysis of the genetic alteration profile from SYMH cohort, 252 PTCs can be classed into three major subgroups, BRAFV600E-(83.7%), RAS-(6.0%), and RET-(3.6%) driven PTCs. These three subgroups covered 93.2% of SYMH cohort and 77.6% in TCGA cohort. (Thyroid peroxidase) TPO status was strongly correlated with BRAF V600E (p=0.003), RET (p=0.005) mutations. Median age of RET- driven PTCs was lower than that of BRAFV600E-, RAS- driven PTCs (p=0.034), which was in concordance with TCGA cohort (p=0.0021). In both cohorts, prevalence of lymph node metastasis in RET-driven PTCs (SYMH, 88%; TCGA, 76%) was higher than that in RAS-(SYMH, 20%; TCGA, 21%) driven PTCs (SYMH, RET vs. RAS, P=0.018; TCGA RET vs. RAS, p Conclusion: PTCs can be classified into BRAF V600E-, or RAS-, or RET-driven subgroups with district clinicopathological features and tumor immune microenvironmental differences, which implication for clinical treatment decision. Citation Format: Miaoyun Long, Yunfang Yu, Lili Lin, Yue Zhu, Langping Tan, Yisikandaer Yalikun, Wenhao Ouyang, Shuai Ni, Xi Li, Ting Hou, Wenjie Sun, Zhange Chen. Novelmolecular subtypes of papillary thyroid carcinoma (PTC) driven by BRAFV600E-,RAS- and RET-mutations linked to distinct clinicopathological features and tumor immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5265.

In this paper, two different technologies are adopted to realize the digital campus intelligent chatbot. The robot has three core modules: chat module, campus information module and virtual teaching assistant module. For chat module, this paper uses Encoder-Decoder framework. For the campus information module and virtual teaching assistant module, this paper pursue high precision and adopt retrieval mode. In practice, the two modules implemented by different technical means complement each other, greatly improving the user experience of students.

This paper focuses on the Bloom Filter deformation algorithm. The storage space occupied by Bloom Filter is independent of elements, which has high space efficiency and low time complexity of insert and query operations. However, as the number of added collections increases, the Bloom Filter’s error rate increases, thus filtering out a lot of non-repeating information. In order to solve this problem, the standard Bloom Filter is improved by introducing an algorithm of the structure of the master-slave Bloom Filter. Only when the master-slave Bloom Filter produces errors, the filter is considered as an error. At the same time, by dynamically increasing the number of filters, the growth of misjudgment rate is delayed. This paper also involves an improved Bloom Filter algorithm to reduce the error rate of the repeated data in the judgment filter.