Neoadjuvant Savolitinib targeted therapy stage IIIA-N2 primary lung adenocarcinoma harboring MET Exon 14 skipping mutation: A case report.

MET exon 14 skipping mutation (METex14m) is rare and occurs in approximately 1-4% of all non-small cell lung cancer (NSCLC) patients and approximately 2.8% of resected stage I-III NSCLC patients. Savolitinib is an oral, potent and highly selective type Ib MET inhibitor, which has been shown to be promising activity and acceptable safety profile in patients with advanced NSCLC harboring METex14m. Most recently, many studies have been probing into the feasibility and efficacy of target therapy for perioperative application in NSCLC. Interestingly, there are very few recorded cases of such treatments. Here, we presented that systemic treatment with the MET inhibitor savolitinib before surgery could provide the potential to prolong overall survival (OS) of patients with locally advanced potentially resectable NSCLC. A 49-year-old woman was diagnosed with stage IIIA (T2bN2M0) primary lung adenocarcinoma exhibiting a METex14m by real-time quantitative polymerase chain reaction (RT-qPCR). Given that the tumor load and the size of lymph nodes experienced a significant downstaging after the neoadjuvant treatment of savolitinib with 600mg once a day for 5 weeks, left lower lobectomy and systemic lymphadenectomy were successfully performed. The pathological response was 50% and the final postoperative pathological staging was pT1cN0M0, IA3 (AJCC, 8th edition). The case provides empirical basis for the neoadjuvant treatment with savolitinib in METex14m-positive locally advanced primary lung adenocarcinoma, which will offer some innovative insights and clinical evidence for more effective clinical treatment of neoadjuvant targeted therapy for METex14m-positive NSCLC.

Acute Respiratory Distress Syndrome Caused by Occupational Exposure to Waterproofing Spray: A Case Report and Literature Review.

Background: Acute respiratory distress syndrome (ARDS) is a serious respiratory disease, caused by severe infection, trauma, shock, inhalation of harmful gases and poisons and presented with acute-onset and high mortality. Timely and accurate identification will be helpful to the treatment and prognosis of ARDS cases. Herein, we report a case of ARDS caused by occupational exposure to waterproofing spray. To our knowledge, inhalation of waterproofing spray is an uncommon cause of ARDS, and what makes our case special is that we ruled out concurrent infections with some pathogens by using metagenomic next-generation sequencing (mNGS) as an auxiliary diagnosis, which presents the most comprehensive etiological examination of similar reports. Case Presentation: A previously healthy 25 years old delivery man developed hyperpyrexia, chest tightness, cough and expectoration. The symptoms occurred and gradually exacerbated after exposure to a waterproofing spray. The chest computed tomography (CT) finding showed diffuse ground glass and infiltrative shadows in both lungs. The diagnosis of ARDS related to waterproofing spray was established on the basis of comprehensive differential diagnosis and etiological examination. The patient achieved good curative effect after proper systemic glucocorticoid therapy. Conclusions: The diagnosis and differential diagnosis of acute respiratory failure for outdoor workers, such as delivery drivers or hikers, should be considered whether toxic aerosol exposure exists from daily contacts. The case can educate the public that more attention should be paid to avoid exposure to these chemicals by aerosols/ingestion mode and some preventive strategies should be taken in occupational environment. The treatment effect of glucocorticoids is significant in ARDS patients with general chemical damage caused by inhaling toxic gases and substances.

Associations of Serum Resistin With the Severity and Prognosis in Patients With Community-Acquired Pneumonia.

Background: Resistin is an endogenous ligand of Toll-like receptor 4 that activates several inflammatory signals. But the physiological function of resistin in community-acquired pneumonia (CAP) remains unknown. The goal of this research was to explore the associations between serum resistin and the severity and prognosis in CAP patients through a retrospective cohort study. Methods: All 212 CAP patients and 106 healthy cases were enrolled. Demographic characteristics were extracted. Serum resistin was determined via enzyme-linked immunosorbent assay. The prognosis was tracked in CAP patients. Results: Serum resistin on admission was raised in CAP patients compared with control cases. The level of resistin was gradually increased in parallel with CAP severity scores in CAP patients. Pearson and Spearman analyses revealed that serum resistin was positively correlated with CAP severity scores, white blood cells, urea nitrogen, creatinine, and inflammatory cytokines among CAP patients. There were negative relationships between resistin and hematocrit and albumin in CAP patients. Besides, linear and logistic regression analyses further indicated that serum resistin on admission was positively associated with CAP severity scores among CAP patients. Follow-up research revealed that serum resistin elevation on admission prolonged hospital stay in CAP patients. Conclusion: Serum resistin on admission is positively correlated with the severity and hospital stay in CAP patients, indicating that resistin may be involved in the physiological process of CAP. Serum resistin may be a potential biomarker in the diagnosis and prognosis for CAP.

TIPE1 Inhibits Breast Cancer Proliferation by Downregulating ERK Phosphorylation and Predicts a Favorable Prognosis.

TIPE1, which acts as a cell death regulator, has emerged as a tumor suppressor in the process of carcinogenesis. However, our recent research demonstrated that it serves as an oncogene in the pathogenesis of cervical cancer, indicating that the role of TIPE1 in carcinogenesis needs to be further evaluated. In this study, we show that TIPE1 is able to inhibit breast cancer cell growth both in vivo and in vitro. Functionally, TIPE1 inhibits cancer cell proliferation preferentially by downregulating ERK phosphorylation. Furthermore, the expression of TIPE1 is decreased in breast cancer tissues compared to matched adjacent tissues, and its expression is positively correlated with patients' lifespan. These data indicate that TIPE1 suppresses breast cancer proliferation by inhibiting the ERK signaling pathway. This study also suggests that TIPE1 could serve as a potential therapeutic target and a diagnostic biomarker for breast cancer.

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data.

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods; moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods.

LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering.

Feature selection and sample clustering play an important role in bioinformatics. Traditional feature selection methods separate sparse regression and embedding learning. Later, to effectively identify the significant features of the genomic data, Joint Embedding Learning and Sparse Regression (JELSR) is proposed. However, since there are many redundancy and noise values in genomic data, the sparseness of this method is far from enough. In this paper, we propose a strengthened version of JELSR by adding the L1-norm constraint on the regularization term based on a previous model, and call it LJELSR, to further improve the sparseness of the method. Then, we provide a new iterative algorithm to obtain the convergence solution. The experimental results show that our method achieves a state-of-the-art level both in identifying differentially expressed genes and sample clustering on different genomic data compared to previous methods. Additionally, the selected differentially expressed genes may be of great value in medical research.

PCA via joint graph Laplacian and sparse constraint: Identification of differentially expressed genes and sample clustering on gene expression data.

BACKGROUND: In recent years, identification of differentially expressed genes and sample clustering have become hot topics in bioinformatics. Principal Component Analysis (PCA) is a widely used method in gene expression data. However, it has two limitations: first, the geometric structure hidden in data, e.g., pair-wise distance between data points, have not been explored. This information can facilitate sample clustering; second, the Principal Components (PCs) determined by PCA are dense, leading to hard interpretation. However, only a few of genes are related to the cancer. It is of great significance for the early diagnosis and treatment of cancer to identify a handful of the differentially expressed genes and find new cancer biomarkers. RESULTS: In this study, a new method gLSPCA is proposed to integrate both graph Laplacian and sparse constraint into PCA. gLSPCA on the one hand improves the clustering accuracy by exploring the internal geometric structure of the data, on the other hand identifies differentially expressed genes by imposing a sparsity constraint on the PCs. CONCLUSIONS: Experiments of gLSPCA and its comparison with existing methods, including Z-SPCA, GPower, PathSPCA, SPCArt, gLPCA, are performed on real datasets of both pancreatic cancer (PAAD) and head & neck squamous carcinoma (HNSC). The results demonstrate that gLSPCA is effective in identifying differentially expressed genes and sample clustering. In addition, the applications of gLSPCA on these datasets provide several new clues for the exploration of causative factors of PAAD and HNSC.

PCA Based on Graph Laplacian Regularization and P-Norm for Gene Selection and Clustering.

In modern molecular biology, the hotspots and difficulties of this field are identifying characteristic genes from gene expression data. Traditional reconstruction-error-minimization model principal component analysis (PCA) as a matrix decomposition method uses quadratic error function, which is known sensitive to outliers and noise. Hence, it is necessary to learn a good PCA method when outliers and noise exist. In this paper, we develop a novel PCA method enforcing P-norm on error function and graph-Laplacian regularization term for matrix decomposition problem, which is called as PgLPCA. The heart of the method designing for reducing outliers and noise is a new error function based on non-convex proximal P-norm. Besides, Laplacian regularization term is used to find the internal geometric structure in the data representation. To solve the minimization problem, we develop an efficient optimization algorithm based on the augmented Lagrange multiplier method. This method is used to select characteristic genes and cluster the samples from explosive biological data, which has higher accuracy than compared methods.