The substitution of the seventeenth amino acid glutamate by lysine in the homologous structural domain of the Akt1 gene pleckstrin is a somatic cellular mutation found in breast, colorectal, and ovarian cancers, named p. Glu17Lys or E17K. In recent years, a growing number of studies have suggested that this mutation may play a unique role in the development of tumors. In this review article, we describe how AKT1(E17K) mutations stimulate downstream signals that cause cells to emerge transformed; we explore the differential regulation and function of E17K in different physiological and pathological settings; and we also describe the phenomenon that E17K impedes tumor growth by interfering with growth-promoting and chemotherapy-resistant AKT1 low QCC generation, an intriguing finding that mutants may prolong tumor patient survival by activating feedback mechanisms and disrupting transcription. This review is intended to provide a better understanding of the role of AKT1(E17K) in cancer and to inform the development of AKT1(E17K)-based antitumor strategies.
CCL17 is an important chemokine that plays a vital immunomodulatory role in the tumor microenvironment (TME). Analysis of lung adenocarcinoma (LUAD) data in Kaplan–Meier plotter databases found that the overall survival of patients in the CCL17 high-expression group was higher than that of the low-expression group, especially for patients with early (stages I and II) LUAD, which has a more positive prognostic value. Expression of CCL17 in LUAD was positively correlated with the proportion of tumor-infiltrating lymphocytes, immunostimulators, and major histocompatibility complexes using the TISIDB databases. Based on the RNA-seq and clinical data of 491 LUAD patients obtained from the TCGA database, 1,455 differential genes were found between the CCL17 high- and low-expression groups. Using WGCNA analysis confirmed that the expression of differential genes in the blue module is negatively correlated with poor survival and clinical stages of LUAD patients, and CCL17 and CCR4 genes belong to the hub genes in the blue module. Further analysis by the ESTIMATE and CIBERSORT algorithm found that the naive B cells and CD8+ T cells in the CCL17 high-expression group have a higher distribution ratio in the early LUAD patients, and the high immune score has a positive relationship with the overall survival rate. Using somatic mutation data of TCGA-LUAD, we found that 1) the tumor mutation burden values of the CCL17 high-expression group were significantly lower than those of the CCL17 low-expression group and 2) the expression levels of CCL17 and the tumor mutation burden values were negatively correlated. Transwell chemotaxis and cytotoxicity assays confirmed that CCL17 contributes to the migration of CCR4-positive lymphocytes into the H1993 LUAD TME and enhances the specific lysis of LUAD cells. In summary, high expression of CCL17 in the LUAD TME promotes local immune cell infiltration and antitumor immune response, which may contribute to the better survival and prognosis of patients with early LUAD.
Recently, cytokine-induced killer (CIK) cells have been shown to possess effective cytotoxic activity against some tumor cells both in vitro and in clinical research. Furthermore, dendritic cell-activated CIK (DC-CIK) cells display significantly increased antitumor activity compared to unstimulated CIK cells. Study findings indicate DC cells can secrete chemokine C-C motif ligand 17 (CCL17) and chemokine C-C motif ligand 22 (CCL22) with a common receptor molecule, C-C chemokine receptor type-4(CCR4). CCL17 and CCL22 levels were measured by ELISA from CIK cell culture supernatants and the expression of CCR4 on CIK and DC-CIK cells was analyzed by flow cytometry. Through Migration and Killing assays, further analyzed the effects of the altered expression levels of CCR4 on the chemotactic ability and the tumor-killing efficiency of CIK cells. We found markedly increased CCL17 and CCL22 in supernatants of DC-CIK co-cultures. Similarly, the expression of CCR4 was also increased on CIK cells in these co-cultures. Further, the stimulation of CCL17 and CCL22 increased expression of the CCR4 and enhanced the migratory capacity and antitumor efficacy of CIK cells. Simultaneously, similar effects had achieved by transfecting the CCR4 gene into CIK cells. DC cells may promote the expression of CCR4 on CIK cells by secreting CCL17 and CCL22, thereby promoting infiltration of DC-CIK cells into the tumor microenvironment, and exerting stronger antitumor activity than CIK cells.
With the rapid development of artificial intelligence and the continuous improvement of machine learning technology, speech recognition technology is also developing rapidly and the recognition accuracy is improving to meet the higher requirements of people for smart home devices, and combining smart home with voice recognition technology is an inevitable trend for future development. This study aims to propose a speech fuzzy enhancement algorithm based on neural network for smart home interactive speech recognition technology, so the study proposes a combination of fuzzy neural network algorithm (FNN) and stacked self-encoder (SAE) to form SAE-FNN algorithm, which has better non-linear characteristics and can better achieve feature learning, thus improving the performance of the whole system. The results show that with the SAE-FNN algorithm, the maximum relative error absolute value, average relative error and root mean square error are 0.355, 0.063 and 0.978, which are significantly higher than the other two individual algorithms, and the noise of the sound signal has little effect on the SAE-FNN algorithm. Therefore, it can be seen that the proposed SAE-FNN algorithm has excellent noise immunity performance. In summary, it can be seen that this neural network-based speech fuzzy enhancement algorithm for smart home interaction is extremely feasible.
<abstract> <p>Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs. Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT and Xcell in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 demonstrated the visible difference in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes from the nine-IRG prognostic model, and the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, the expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 were analyzed between metastatic melanoma and normal samples. Overall, a prognostic model for metastatic melanoma based on the tumor immune microenvironment characteristics was established, which left plenty of space for further studies. It could function well in helping people to understand characteristics of the immune microenvironment in metastatic melanoma.</p> </abstract>
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