Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. In this study, our objective was to identify differentially regulated proteins in HCC through a quantitative proteomic approach using iTRAQ. More than 600 proteins were quantitated of which 59 proteins were overexpressed and 92 proteins were underexpressed in HCC as compared to adjacent normal tissue. Several differentially expressed proteins were not implicated previously in HCC. A subset of these proteins (six each from upregulated and downregulated groups) was further validated using immunoblotting and immunohistochemical labeling. Some of the overexpressed proteins with no previous description in the context of HCC include fibroleukin, interferon induced 56 kDa protein, milk fat globule-EGF factor 8, and myeloidassociated differentiation marker. Interestingly, all the enzymes of urea metabolic pathway were dramatically downregulated. Immunohistochemical labeling confirmed differential expression of fibroleukin, myeloid associated differentiation marker and ornithine carbamoyl transferase in majority of HCC samples analyzed. Our results demonstrate quantitative proteomics as a robust discovery tool for the identification of differentially regulated proteins in cancers.

Summary Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

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Day-to-day observations reveal numerous medical and social situations where maintaining physical distancing is either not feasible or not practiced during the time of a viral pandemic, such as, the coronavirus disease 2019 (COVID-19). During these close-up, face-to-face interactions, a common belief is that a susceptible person wearing a face mask is safe, at least to a large extent, from foreign airborne sneeze and cough droplets. This study, for the first time, quantitatively verifies this notion. Droplet flow visualization experiments of a simulated face-to-face interaction with a mask in place were conducted using the particle image velocimetry setup. Five masks were tested in a snug-fit configuration (i.e., with no leakage around the edges): N-95, surgical, cloth PM 2.5, cloth, and wetted cloth PM 2.5. Except for the N-95 mask, the findings showed leakage of airborne droplets through all the face masks in both the configurations of (1) a susceptible person wearing a mask for protection and (2) a virus carrier wearing a mask to prevent the spreading of the virus. When the leakage percentages of these airborne droplets were expressed in terms of the number of virus particles, it was found that masks would not offer complete protection to a susceptible person from a viral infection in close (e.g., <6 ft) face-to-face or frontal human interactions. Therefore, consideration must be given to minimize or avoid such interactions, if possible. This study lends quantitative support to the social distancing and mask-wearing guidelines proposed by the medical research community.

BackgroundGastric cancer is highly aggressive disease. Despite advances in diagnosis and therapy, the prognosis is still poor. Various genetic and molecular alterations are found in gastric cancer that underlies the malignant transformation of gastric mucosa during the multistep process of gastric cancer pathogenesis. The detailed mechanism of the gastric cancer development remains uncertain. In present study we investigated the potential role of stathmin1 gene in gastric cancer tumorigenesis and examined the usefulness of RNA interference (RNAi) targeting stathmin1 as a form of gastric cancer treatment.MethodsA lentiviral vector encoding a short hairpin RNA (shRNA) targeted against stathmin1 was constructed and transfected into the packaging cells HEK 293 T and the viral supernatant was collected to transfect MKN-45 cells. The transwell chemotaxis assay and the CCK-8 assay were used to measure migration and proliferation of tumor cells, respectively. Quantitative real-time PCR and western blotting were used to detect the expression levels of stathmin1.ResultsLentivirus mediated RNAi effectively reduced stathmin1 expression in gastric cells. Significant decreases in stathmin1 mRNA and protein expression were detected in gastric cells carrying lentiviral stathmin-shRNA vector and also significantly inhibited the proliferation, migration in gastric cancer cells and tumorigenicity in Xenograft Animal Models.ConclusionsOur findings suggest that stathmin1 overexpression is common in gastric cancer and may play a role in its pathogenesis. Lentivirus mediated RNAi effectively reduced stathmin1 expression in gastric cells. In summary, shRNA targeting of stathmin1 can effectively inhibits human gastric cancer cell growth in vivo and may be a potential therapeutic strategy for gastric cancer.

Even localized ESCC are potential to relapse with poor prognosis. This study demonstrates that STMN-1 level is an independent prognostic factor after Ivor-Lewis esophagectomy. In addition, assessment of STMN-1 level could improve stratification of stage IIA ESCC patients.

Stathmin (STMN1) regulates microtubule dynamics by promoting depolymerization of microtubules and/or preventing polymerization of tubulin heterodimers. Several studies have shown that overexpression of STMN1 has been linked to chemoresistance of paclitaxel and vinblastine in tumor cells. This study aimed to investigate the effects of STMN1 silencing on chemosensitivities of paclitaxel or vinblastine in esophageal squamous cell carcinoma (ESCC). Immunocytochemistry and immunofluorescence assays showed that STMN1 gene was highly expressed in Eca109 and TE-1 cells. We demonstrated that lentiviral-mediated STMN1 short hairpin RNA (shRNA) specifically and efficiently downregulated STMN1 expression in Eca109 and TE-1 cells. The sensitivity of STMN1-silencing shRNA-transfected Eca109 and TE-1 cells increased 191.4- and 179.3-fold to paclitaxel, and 21.3- and 28.4-fold to vincristine, respectively. Flow cytometry and mitotic index assays showed that knockdown of STMN1 in Eca109 and TE-1 cells led to cell cycle arrest in G2/M phase. After treatment with paclitaxel or vincristine, STMN1-silencing shRNA-transfected Eca109 and TE-1 cells were more likely to enter G2 but less likely to enter mitosis than control cells. Therefore, these data suggests that silencing STMN1 gene could increase sensitivity of ESCC to paclitaxel and vincristine through G2/M phase block.

Introduction Since December 2019, severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic in China and worldwide. New drugs for the treatment of COVID-19 are in urgent need. Considering the long development time for new drugs, the identification of promising inhibitors from FDA-approved drugs is an imperative and valuable strategy. Recent studies have shown that the S1 and S2 subunits of the spike protein of SARS-CoV-2 utilize human angiotensin-converting enzyme 2 (hACE2) as the receptor to infect human cells. Methods We combined molecular docking and surface plasmon resonance (SPR) to identify potential inhibitors for ACE2 from available commercial medicines. We also designed coronavirus pseudoparticles that contain the spike protein assembled onto green fluorescent protein or luciferase reporter gene-carrying vesicular stomatitis virus core particles. Results We found that thymoquinone, a phytochemical compound obtained from the plant Nigella sativa , is a potential drug candidate. SPR analysis confirmed the binding of thymoquinone to ACE2. We found that thymoquinone can inhibit SARS-CoV-2, SARS-CoV, and NL63 pseudoparticles infecting HEK293-ACE2 cells, with half-maximal inhibitory concentrations of 4.999, 7.598, and 6.019 μM, respectively. The SARS-CoV-2 pseudoparticle inhibition had half-maximal cytotoxic concentration of 35.100 μM and selection index = 7.020. Conclusion Thymoquinone is a potential broad-spectrum inhibitor for the treatment of coronavirus infections. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-021-00400-2.