Disease Markers最新文献

Objective: To assess the use of cytotoxic drugs as first-line chemotherapy for nonsquamous non-small-cell lung cancer (NSCLC) with EGFR mutation.

Method: This study uses the network meta-analysis (NMA) method, with the inclusion of prospective randomized control studies related to the treatment of EGFR-positive nonsquamous NSCLC, to compare the efficacy of various EGFR-TKIs. As of September 4, 2022, 16 studies on a total of 4180 patients were included. The retrieved literature was comprehensively evaluated as per the established inclusion and exclusion criteria, and valid data were extracted and included for analysis.

Results: The 6 treatment regimens included cetuximab, CTX (cyclophosphamide), icotinib, gefitinib, afatinib, and erlotinib. All of the 16 studies reported their findings about overall survival (OS), and 15 of them also reported findings about progression-free survival (PFS). The NMA results showed that there was no significant difference in OS among the 6 treatment regimens. It was observed that erlotinib had the highest likelihood of obtaining the best OS, followed by afatinib, gefitinib, icotinib, CTX, and cetuximab, in descending order. This indicates that the highest possibility of achieving the best OS was with erlotinib, while the lowest was with cetuximab. The NMA results also showed that the PFS achieved with treatment using afatinib, erlotinib, and gefitinib were all higher than that with treatment using CTX, with statistically significant differences. The results showed that there was no significant difference in PFS among erlotinib, gefitinib, afatinib, cetuximab, and icotinib. CTX, cetuximab, icotinib, gefitinib, afatinib, and erlotinib were ranked in descending order based on the PFS indicator SUCRA values, which implied that erlotinib had the highest possibility in achieving the best PFS, while CTX had the lowest. Discussion. EGFR-TKIs must be carefully selected for the treatment of different histologic subtypes of NSCLC. For EGFR mutation (+) nonsquamous NSCLC, erlotinib is most likely to achieve the best OS and PFS, which makes it the first choice in the formulation of a treatment plan.

Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.

Background: The most prevalent malignant tumor in a human brain nervous system is called glioma. Peptide is a compound formed by the peptide bond of α-amino acids, and the development of polypeptide drugs has been widely used in many fields. We plan to investigate the underlying peptides with clinical value in glioma.

Method: Based on public databases, we targeted the common genes between glioma differentially expressed genes (DEGs) and peptide genes related to glioma prognosis. Then, these common genes were analyzed by LASSO-Cox analysis, prognostic risk model, and nomogram to identify key prognostic peptide genes and the target gene in this study. Next, the mechanism of target gene in glioma was explored by bioinformatics analysis and functional experiments.

Results: We obtained a total of 26 overlapping genes for the following study. After that, 6 independent prognostic factors (REPIN1, PSD3, RDX, CDK4, FANCI, and ARHGEF9) were obtained and applied to construct the prognostic nomogram, and ARHGEF9 was the target gene in the study. Next, peptide ARHGEF9 was found to inhibit glioma cell development. Through Spearman's correlation analysis, ARHGEF9 had a close relation with PI3K/AKT/mTOR pathway. In functional experiments, peptide ARHGEF9 could suppress the protein expressions of p-PIK3K, p-AKT and p-mTOR, while IGF-1 could reverse this effect.

Conclusion: This study identifies 6 new prognostic biomarkers for glioma patients. Among them, peptide ARHGEF9 gene is an inhibitory gene functioning by targeting PI3K/AKT/mTOR pathway.

Emerging evidence has revealed the significant roles of nicotinamide n-methyltransferase (NNMT) in cancer initiation, development, and progression; however, a pan-cancer analysis of NNMT has not been conducted. In this study, we first thoroughly investigated the expression and prognostic significance of NNMT and the relationship between NNMT and the tumor microenvironment using bioinformatic analysis. NNMT was significantly increased and associated with poor prognosis in many common cancers. NNMT expression correlated with the infiltration levels of cancer-associated fibroblasts and macrophages in pan-cancer. Function enrichment analysis discovered that NNMT related to cancer-promoting and immune pathways in various common cancers, such as colon adenocarcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma, and stomach adenocarcinoma. NNMT expression was positively correlated with tumor-associated macrophages (TAMs), especially M2-like TAMs. The results suggest that NNMT might be a new biomarker for immune infiltration and poor prognosis in cancers, providing new direction on therapeutics of cancers.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common malignant gynecological cancer. The ceRNA networks play important roles in many tumors, while RILPL2-related ceRNA network has been seldom studied in CESC.

Methods: All CESC data was obtained from TCGA database. Differentially expressed RNAs and predicted target RNAs were cross analyzed to construct ceRNA network. RNA and clinicopathological characteristics' influence on overall survival (OS) were determined by univariate and multivariate Cox regression analyses. Lasso regression was used to construct the prediction model. Coexpression analysis was performed to explore the association of gene expression with CESC. This was followed by an experimental validation based on these results.

Results: Between high and low RILPL2 expression CESC patients, totally 1227 DEmRNAs, 39 DEmiRNAs, and 1544 DElncRNAs were identified. After multiple cross analyses, 1 miRNA hsa-miR-1293, 20 mRNAs, and 43 lncRNAs were maintained to construct ceRNA network. CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network were significantly associated with the OS of CESC patients, and patients with low expression of these lncRNAs had worse prognosis. Significant lower expressions of these lncRNAs were also observed in CESC cell line compared with normal cell line.

Conclusion: Low expressions of CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network were probably promising poor prognostic biomarkers for CESC patients. The genes show a prospective research area for CESC-targeted treatment in the future.

Objective: To evaluate the effect of wrist dorsiflexion/palmar flexion on median nerve excursion and cross-sectional area in patients with carpal tunnel syndrome.

Methods: From November 2019 to December 2021, 85 patients (110 affected wrists) who presented to our department and were diagnosed with carpal tunnel syndrome were collected and classified by severity as mild to moderate. Twenty-five healthy controls were selected during the same period, with a total of 50 healthy wrists. All patients and healthy volunteers underwent high-frequency ultrasonography to measure the vertical deviation between the median nerve and the transverse carpal ligament during wrist dorsiflexion/palmar flexion and the changes in the cross-sectional area of the median nerve in the pisiform plane. All patients with carpal tunnel syndrome underwent neurophysiological testing to measure median nerve sensory conduction velocity, sensory latency time, and sensorimotor point fluctuation amplitude.

Results: The mean age of the patients was 50 ± 8 years, the proportion of males was 18%, and the disease course was 2.3 ± 1.2 years. In terms of severity grading, 38 patients (34.5%) had mild carpal tunnel syndrome, 30 patients (27.3%) had moderate carpal tunnel syndrome, and 42 patients (38.2%) had severe carpal tunnel syndrome. Compared with the control group, the distance between the proximal median nerve and the transverse carpal ligament, the distance between the distal median nerve and the transverse carpal ligament, and the cross-sectional area were decreased in the carpal tunnel syndrome group compared with those during wrist dorsiflexion, and the differences were statistically significant (P < 0.05). Compared with the control group, there were significant differences in the vertical distance and cross-sectional area between the median nerve and the transverse carpal ligament at the proximal and distal ends in the mild, moderate, and severe groups (P < 0.05). The proximal vertical distance of the median nerve was positively correlated with sensory latency (P < 0.05) and negatively correlated with sensory conduction velocity (P < 0.05). The vertical distance of the distal end of the median nerve was also significantly positively correlated with sensory latency (P < 0.05) and significantly negatively correlated with sensory conduction velocity (P < 0.05).

Conclusion: Wrist dorsiflexion/palmar flexion can affect median nerve deviation and cross-sectional area in patients with carpal tunnel syndrome. High-frequency ultrasound is helpful to detect such an effect and can also help determine the severity of carpal tunnel syndrome, which is worthy of clinical promotion.

Methods: Differentially transcription factors (DETFs) were identified from differentially expressed genes (DEGs) in GSE62232 and transcription factors. Then, they were analyzed by regulatory networks, prognostic risk model, and overall survival analyses to identify the key DETF. Combined with the regulatory networks and binding site analysis, the target mRNA of key DETF was determined, and its prognostic value in HCC was evaluated by survival, clinical characteristics analyses, and experiments. Finally, the expressions and functions of the key DETF on the DEmRNAs were investigated in HCC cells.

Results: Through multiple bioinformatics analyses, ASCL1 was identified as the key DETF, and SLC6A13 was predicted to be its target mRNA with the common binding site of CCAGCAACTGGCC, both downregulated in HCC. In survival analysis, high SLC6A13 was related to better HCC prognosis, and SLC6A13 was differentially expressed in HCC patients with clinical characteristics. Furthermore, cell experiments showed the mRNA expressions of ASCL1 and SLC6A13 were both reduced in HCC, and their overexpressions suppressed the growth, invasion, and migration of HCC cells. Besides, over-ASCL1 could upregulate SLC6A13 expression in HCC cells.

Conclusion: This study identifies two suppressor genes in HCC progression, ASCL1 and SLC6A13, and the key transcription factor ASCL1 suppresses HCC progression by targeting SLC6A13 mRNA. They are both potential treatment targets and prognostic biomarkers for HCC patients, which provides new clues for HCC research.

Objective: We aimed to identify differentially expressed proteins in the plasma of patients with pancreatic cancer and control subjects, which could serve as potential tumor biomarkers.

Methods: Differentially expressed proteins were determined via isostatic labeling and absolute quantification (iTRAQ). Potential protein biomarkers were identified via enzyme-linked immunosorbent assay (ELISA) in 40 patients and 40 control subjects, and those eventually selected were further validated in 40 pancreatic cancer and normal pancreatic tissues.

Results: In total, 30 proteins displayed significant differences in expression among which 21 were downregulated and 9 were upregulated compared with the control group. ELISA revealed downregulation of peroxiredoxin-2 (PRDX2) and upregulation of alpha-1-antitrypsin (AAT), Ras-related protein Rab-2B (RAB2B), insulin-like growth factor-binding protein 2 (IGFBP2), Rho-related GTP-binding protein RhoC (RHOC), and prelamin-A/C (LMNA) proteins in 40 other samples of pancreatic cancer. Notably, only AAT, RAB2B, and IGFBP2 levels were consistent with expression patterns obtained with iTRAQ. Moreover, all three proteins displayed a marked increase in pancreatic cancer tissues. Data from ROC curve analysis indicated that the diagnostic ability of AAT, RAB2B, and IGFBP2 combined with carbohydrate antigen 19-9 (CA19-9) for pancreatic cancer was significantly greater than that of the single indexes (area under the curve (AUC): 90% vs. 75% (CA19-9), 76% (AAT), 71% (RAB2B), and 71% (IGFBP2), all P < 0.01).

Conclusion: AAT, RAB2B, and IGFBP2 could serve as effective biomarkers to facilitate the early diagnosis of pancreatic cancer.