Influencing factors associated with quality of life and depression among COVID-19 survivors during convalescence.

This study aims to investigate influencing factors of quality of life (QoL) and depression among COVID-19 survivors during convalescence. A cross-sectional study was conducted in November 2020 in Wuhan, China. Information on social support, physical activity, QoL and depressive symptoms were assessed using self-administered questionnaires. Multivariate linear regression and multivariate logistic regression were used to assess the risk factors of subdomains of QoL (physical component score (PCS) and mental component score (MCS)) and depression, respectively. A total of 151 COVID-19 survivors (68 males) aged 53.21 (SD: 12.70) years participated in the study. Multivariate linear regression showed that age (ß=-0.241), history of chronic disease (ß=-0.4.774), physical activity (ß = 2.47) and social support (ß = 0.147) were significantly associated with PCS, while having a spouse (ß = 9.571), monthly income (ß = 0.043) and social support (ß = 0.337) were significantly associated with MCS. Logistic regression suggested that participants aged 40-60 years (OR = 10.20, 95%CI: 1.41-73.82) or above 60 years (OR = 15.63, 95%CI: 1.87-131.00), with high school or above education (OR = 5.81, 95%CI: 1.24-27.20), with low/moderate physical activity (low, OR = 2.97, 95%CI: 1.14-7.77; moderate, OR = 3.42, 95%CI: 1.07-10.91) and low/medium social support (low, OR = 4.81, 95% CI: 2.02-11.43; medium, OR = 9.70, 95%CI: 1.17-80.10) were more likely to be depressed, while higher monthly income (≥3000 Yuan RMB/month) was associated with lower risk for depression (OR = 0.27, 95%CI: 0.09-0.82). These findings indicate COVID-19 survivors with older age, having chronic conditions, without a spouse, low monthly income, low level of physical activity and social support had significantly increased risks for poor QoL and depression, and more attention should be given to this population.

The effects of the prognostic biomarker SAAL1 on cancer growth and its association with the immune microenvironment in lung adenocarcinoma.

BACKGROUND: Inhibition of Serum Amyloid A-like 1 (SAAL1) expression could inhibit cancer progression and improve the prognosis of cancer patients. At present, the correlation between SAAL1 and lung adenocarcinoma (LAC) remains unclear. Therefore, this study surveyed the worth and pathway of SAAL1 in LAC progression and immunity. METHODS: Bioinformatics and immunohistochemistry were used to identify the SAAL1 expression in LAC. The roles of SAAL1 expression in the existence values of LAC patients were explored, and the nomograms were constructed. Clinical values of SAAL1 co-expressed genes were evaluated by COX regression, survival, and Receiver operating characteristic (ROC) analysis. EDU and western blotting methods were used to inquiry the functions and pathways of the SAAL1 in cell growths. The correlation between the SAAL1 level and immune microenvironment was visualized using correlation research. RESULTS: SAAL1 level was elevated in LAC tissues, and was observed in cancer tissues of dead patients. SAAL1 overexpression had something to do with shorter overall survival, progression-free interval, and disease-specific survival in LAC. The area under the curve of SAAL1 was 0.902 in normal tissues and cancer tissues. Inhibition of SAAL1 expression could inhibit cancer cell proliferation, which may be related to the decreased expression of cyclin D1 and Bcl-2 proteins. In LAC, SAAL1 level had something to do with stromal, immune, and estimate scores, and correlated with macrophages, T cells, Th2 cells, CD8 T cells, NK CD56dim cells, DC, eosinophils, NK CD56bright cells, pDC, iDC, cytotoxic cells, Tgd, aDC cells, B cells, Tcm, and TFH levels. SAAL1 overexpression had something to do with existence values and the immunity in LAC. CONCLUSIONS: Inhibition of SAAL1 expression could regulate cancer growth via cyclin D1 and Bcl-2. SAAL1 is a promising prognostic biomarker in LAC patients.

Downregulation of T-cell cytotoxic marker IL18R1 promotes cancer proliferation and migration and is associated with dismal prognosis and immunity in lung squamous cell carcinoma.

Immunotherapy can improve the survival of patients with advanced lung squamous cell carcinoma (LUSC). T cytotoxic cells are one of the main members of the immune microenvironment. Herein, we aimed to identify the roles of T-cell cytotoxic markers interleukin 18 (IL18) receptor 1 (IL18R1) in the LUSC progression using bioinformatics, clinical tissue specimen, and cell experiment. We assessed the association between the IL18R1 expression and immune infiltration and IL18R1-related competing RNA network. The IL18R1 expression was downregulated in the LUSC tissues. The IL18R1 expression downregulation was associated with diagnosis and short overall survival and disease-specific survival, and it was also an independent risk factor for dismal survival time in LUSC. IL18R1-related nomograms predicted the survival time of patients with LUSC. IL18R1 overexpression inhibited the proliferation, migration, and invasion of LUSC cells. The IL18R1 expression was significantly associated with the microenvironment (stromal, immune, and estimate scores), immune cells (such as the T cells, cytotoxic cells, CD8 T cells), and immune cell markers (such as the CD8A, PD-1, and CTLA4) in LUSC. AC091563.1 and RBPMS-AS1 downregulation was positively associated with the IL18R1 expression, negatively associated with the miR-128-3p expression, and associated with short disease-specific survival and progression in LUSC. In conclusion, IL18R1 was significantly downregulated and associated with the prognosis and immune microenvironment. IL18R1 overexpression inhibits the growth and migration of cancer cells in LUSC. Furthermore, AC091563.1 and RBPMS-AS1 might compete with IL18R1 to bind miR-128-3p for participating in LUSC progression. These results showed that IL18R1 is a biomarker for evaluating the prognosis of patients with LUSC.

TRAT1 overexpression delays cancer progression and is associated with immune infiltration in lung adenocarcinoma.

The roles and mechanisms of T-cell receptor (TCR)-associated transmembrane adaptor 1 (TRAT1) in lung adenocarcinoma (LAC) have not yet been reported in the relevant literature. Therefore, this study aimed to understand the roles and mechanisms of TRAT1 in LAC using bioinformatics and in vitro experiments. TRAT1 expression levels in LAC samples were analysed using various databases. TRAT1 co-expressed genes were acquired by the correlation analysis of LAC tissues. The functional mechanisms and protein network of TRAT1 co-expressed genes were analysed using bioinformatics analysis. The expression of TRAT1 was activated in LAC cells, and the roles of TRAT1 overexpression in the growth and migration of cancer cells was investigated using flow cytometry, Cell Counting Kit-8 (CCK-8), and migration and invasion assays. The relationship between TRAT1 overexpression, the immune microenvironment, and RNA modification was evaluated using correlation analysis. TRAT1 expression levels were significantly abnormal at multiple mutation sites and were related to the prognosis of LAC. TRAT1 co-expressed genes were involved in cell proliferation, adhesion, and differentiation, and TRAT1 overexpression significantly inhibited cell viability, migration, and invasion and promoted apoptosis of A549 and H1299 cells, which might be related to the TCR, B cell receptor (BCR), MAPK, and other pathways. TRAT1 expression levels were significantly correlated with the ESTIMATE, immune, and stromal scores in the LAC microenvironment. Additionally, TRAT1 expression levels were significantly correlated with the populations of B cells, CD8 T cells, cytotoxic cells, and other immune cells. TRAT1 overexpression was significantly correlated with the expression of immune cell markers (such as PDCD1, CD2, CD3E) and genes involved in RNA modification (such as ALKBH1, ALKBH3, ALKBH5). In conclusions, TRAT1 overexpression inhibited the growth and migration of LAC cells, thereby delaying cancer progression, and was correlated with the LAC microenvironment and RNA modifications.

Clinical Roles of Risk Model Based on Differentially Expressed Genes in Mesenchymal Stem Cells in Prognosis and Immunity of Non-small Cell Lung Cancer.

The tumor microenvironment (TME) plays an important regulatory role in the progression of non-small cell lung cancer (NSCLC). Mesenchymal stem cells (MSCs) in the TME might contribute to the occurrence and development of cancer. This study evaluates the role of differentially expressed genes (DEGs) of MSCs and the development of NSCLC and develops a prognostic risk model to assess the therapeutic responses. The DEGs in MSCs from lung tissues and from normal tissues were analyzed using GEO2R. The functions and mechanisms of the DEGs were analyzed using the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, the Cancer Genome Atlas (TCGA) database was used to determine the expression levels of the DEGs of MSCs in the NSCLC tissues. The prognostic factors of NSCLC related to MSCs were screened by survival analysis, meta-analysis, Cox regression analysis, and a prognostic risk model and nomogram was developed. The signaling mechanisms and immune roles that risk model participate in NSCLC development were determined via Gene Set Enrichment Analysis and CIBERSORT analysis. Compared to the normal tissues, 161 DEGs were identified in the MSCs of the lung tissues. These DEGs were associated with mechanisms, such as DNA replication, nuclear division, and homologous recombination. The overexpression of DDIT4, IL6, ITGA11, MME, MSX2, POSTN, and TRPA1 were associated with dismal prognosis of NSCLC patients. A high-risk score based on the prognostic risk model indicated the dismal prognosis of NSCLC patients. The nomogram showed that the age, clinical stage, and risk score affected the prognosis of NSCLC patients. Further, the high-risk model was associated with signaling mechanisms, such as the ECM-receptor interaction pathways, cytokine-cytokine receptor interaction, and MAPK pathways, involved in the progression of NSCLC and was also related to the components of the immune system, such as macrophages M0, T follicular helper cells, regulatory T cells. Therefore, the risk model and nomogram that was constructed on the basis of MSC-related factors such as POSTN, TRPA1, and DDIT4 could facilitate the discovery of target molecules that participate in the progression of NSCLC, which might also serve as new candidate markers for evaluating the prognosis of NSCLC patients.