Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).

High prevalence of hepatocellular carcinoma (HCC) and typically poor prognosis of this disease that lead to late stage diagnosis when potentially curative therapies are least effective; therefore, development of an effective and systematic treatment is an urgent requirement. In this review, several current treatments for HCC patients and their advantages or disadvantages were summarized. Moreover, various recent preclinical and clinical studies about the performances of “two efficient agents, sorafenib or natural killer (NK) cells”, against HCC cells were investigated. In addition, the focus this review was on the chemo-immunotherapy approach, correlation between sorafenib and NK cells and their effects on the performance of each other for better suppression of HCC. It was concluded that combinational therapy with sorafenib and NK cells might improve the outcome of applied therapeutic approaches for HCC patients. Finally, it was also concluded that interaction between sorafenib and NK cells is dose and time dependent, therefore, a careful dose and time optimizing is necessary for development of a combinational immune-cell therapy.

Exosomes, a specific type of extracellular vesicle with diameters ranging from 30 to 150 nm, play a vital role in coordinating various physiological and pathological processes through direct cell-to-cell communication. These vesicles transport a diverse range of molecules, including proteins, mRNAs, miRNAs, and lipids, that originate from parent cells. Exosomes are released by nearly all cell types, including mesenchymal stem cells (MSCs), and possess significant potential due to their regenerative and immunomodulatory properties. In the context of liver disease treatment, these capabilities offer hope. Despite their limited lifespan in the bloodstream and suboptimal targeting efficiency, researchers are exploring the potential of modifying MSC-derived exosomes through genetic or chemical means. This article highlights the promising characteristics and applications of MSC-derived exosomes, presenting new perspectives on the latest advancements in exosome engineering for liver regeneration and therapy.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. Sorafenib is the only FDA approved drug for HCC patients, affecting patient survival by just a few months with significant toxicities in approximately half of HCC patients and the poor prognosis of these patients. Thus, the combination therapy with Sorafenib and Natural killer (NK) cells has been suggested, due to NK cells' distinct effectiveness against HCC. This research examined the main signaling pathways in HCC progression affected by Sorafenib or NK cells in combination or individual treatment. The xenograft model of HCC was created by implanting human HepG2 cells subcutaneously into the flank of 12 nude mice and then divided into four groups: Control, Sorafenib, NK cells, and Sorafenib plus NK cells. Four weeks post tumor implantation, the mice were euthanized, and the levels of liver and kidney enzymes were analysed for safety purposes. Quantitative real-time PCR analysis was used to measure the expression of key effector genes related to Sorafenib and NK cell functions with focused on the signaling pathways involved in the development of HCC. The levels of Aspartate aminotransferase (AST), Alanine transaminase (ALT), Blood urea nitrogen (BUN), and creatinine (Cr) in all groups remained in normal ranges. The expression levels of certain proliferative, anti-apoptotic factors were reduced in groups treated with either Sorafenib or NK cells only, but in combinational treated group showed no significant differences compared to control group. Also, the NK cell effector function related genes were upregulated in NK cell treated group, but inhibited in co administration of both NK cell and Sorafenib. Therefore, combining Sorafenib and NK cells at the prescribed dosage led to a decrease in the anti-cancer efficiency of both and may not be a successful option for HCC treatment.