American journal of cancer research最新文献

Poor oral health is an independent risk factor for upper-aerodigestive tract cancers, including esophageal squamous cell carcinoma (ESCC); thus, good oral health may reduce the risk of ESCC. We previously reported that high expression of Toll-like receptor (TLR) 6, which recognizes peptidoglycan (PGN) from Gram-positive bacteria correlates with a good prognosis after esophagectomy for ESCC. Most beneficial bacteria in the mouth are Gram-positive. We therefore hypothesized that PGN affects cancer cell proliferation and disease progression in ESCC. To test that idea, we assessed the expression of cytokine and chemokine mRNA and protein in eight ESCC cell lines. We also employed a mouse xenograft model to investigate the effect of PGN on ESCC tumor progression in vivo. We then investigated the relationship between the combined expression profiles of TLR6 and C-X-C motif chemokine ligand 10 (CXCL10) in clinical samples and 5-year overall survival (OS) and disease-specific survival (DSS) in ESCC patients after curative esophagectomy. We found that PGN significantly inhibited cell proliferation in six of eight ESCC lines and upregulated CXCL10 production via NF-κB2. In vivo, subcutaneous PGN administration tended to decrease ESCC tumor volume in mice. Combined high expression of TLR6 and CXCL10 correlated with a better prognosis in ESCC patients. This suggests that PGN reduces cell proliferation and tumor progression through a PGN-TLR-CXCL10 cascade, thereby influencing prognosis after esophagectomy for ESCC, and that improving the oral environment could potentially improve the prognosis of ESCC patients after esophagectomy.

This multicenter study explored the survival benefits of upfront primary tumor resection (PTR) followed by first-line cetuximab plus chemotherapy in real-world patients with RAS wild-type metastatic colorectal cancer (mCRC). Treatment options for mCRC include chemotherapy, targeted therapy, immunotherapy, and surgery. The efficacy of upfront PTR in managing mCRC remains unclear. In this retrospective study, we evaluated the outcomes of upfront PTR in 582 patients with synchronous RAS wild-type mCRC who received cetuximab plus chemotherapy as first-line treatment between November 2016 and August 2020. Of these patients, 364 (62.5%) underwent upfront PTR (PTR group) and 218 (37.5%) did not (non-PTR group). Relevant data were collected from 14 medical institutions in Taiwan. No significant differences were discovered between the PTR and non-PTR groups in median overall survival (37.9 vs. 31.7 months; P = 0.079) or progression-free survival (13.70 vs. 13.29 months; P = 0.62). Compared with patients who did not undergo metastasectomy, those who underwent this surgery exhibited significantly longer median overall survival (29.2 vs. 54.18 months; P < 0.001) and progression-free survival (12.8 vs. 15.60 months; P = 0.013). Our findings suggest that upfront PTR may not improve oncological outcomes in patients with synchronous RAS wild-type mCRC. Cetuximab-based targeted therapy plus chemotherapy appears to be suitable as first-line treatment for these patients. This study indicates that upfront PTR should be considered only for patients exhibiting symptoms such as tumor bleeding, perforation, or obstruction.

Objective: To investigate the role of long non-coding RNA (lncRNA) SLC16A1-AS1 in the initiation and progression of colorectal cancer (CRC).

Methods: Cell viability was tested using Cell Counting Kit-8 (CCK-8). Cell invasion and migration were evaluated using Transwell assays, and apoptosis was determined by flow cytometry. Gene expression was tested by qRT-PCR or Western blot. The targeting relationship between miR-515-5p and MAP3K9 was verified using bioinformatics tools, RNA immunoprecipitation (RIP) experiments, and dual-luciferase reporter assays.

Results: Both lncRNA SLC16A1-AS1 and MAP3K9 were upregulated in CRC cells, while miR-515-5p expression was downregulated. Overexpression of miR-515-5p and silencing of lncRNA SLC16A1-AS1 inhibited CRC cell proliferation, suppressed cell invasion and migration, and promoted cell apoptosis. The targeting relationship between lncRNA SLC16A1-AS1 and miR-515-5p, as well as between MAP3K9 and miR-515-5p, were confirmed by bioinformatics, RIP assays, and luciferase reporter assays.

Conclusion: lncRNA SLC16A1-AS1 contributes to the initiation and progression of CRC by modulating miR-515-5p to regulate MAP3K9 expression, providing potential insights for CRC treatment.

Breast cancer is the second leading cause of cancer deaths among women. Multiple microRNAs (miRNAs) have been reported to be associated with breast cancer progression or metastasis. The purpose of the current study was to identify plasma extracellular miRNAs associated with incident breast cancer. Levels of 166 plasma miRNA were measured using qRT-PCR in 2140 Framingham Heart Study female participants with a median follow up of 15.7 years. Prospective analyses of the associations of miRNAs with the occurrence of 56 new-onset breast cancer events were conducted using proportional hazards regression. The expression levels miR-134-5p (P=0.002) and miR-505-3p (P=0.005) were found to be positively associated with incident breast cancer after adjusting for age, body mass index, and cigarette smoking. These results highlight plasma miRNAs as potential biomarkers of breast cancer risk. Validation of these findings in larger and more diverse cohorts is warranted.

Microsatellite instability-high (MSI-H) is a critical biomarker for immunotherapy, yet primary resistance remains a significant challenge. Current MSI-H detection methods evaluate the proportion of MSI-H loci, termed molecular MSI-H score, which can be affected by intratumoral heterogeneity (ITH). To address this limitation, we propose evaluating MSI-H at the cellular level to improve the prediction of immunotherapy outcomes. Using bulk tissue (TCGA-CRC) and cell line (CCLE-CRC) datasets, we identified genes highly expressed in MSI-H and MSS samples. These signatures were applied to a single-cell RNA sequencing (scCRC) dataset for enrichment analysis, enabling classification of tumor cells into MSI-H, MSS, and microsatellite dual (MSD) clusters using a Gaussian finite mixture model. Validation showed that MSI-H and MSS enrichment scores were higher in mismatch repair-deficient (MMRd) and mismatch repair-proficient (MMRp) patients, respectively. Functional enrichment analysis revealed that MSI-H cells were associated with pathways such as carboxylic acid catabolism, inflammatory responses, and IL-6/JAK2/STAT3 signaling. We developed a cellular MSI-H signature using genes specifically expressed in the MSI-H cell cluster and transformed the scCRC dataset into a cell-type-specific pseudobulk expression matrix. Using this matrix as a reference, we performed reference-based deconvolution on TCGA-CRC data. We defined the deconvolution score of MSI-H cell as cellular MSI-H score. This score strongly correlated with the molecular MSI-H score (R = 0.55, P < 0.001) and showed modest correlations with macrophage (MoMac, R = 0.14) and CD8+ T-cell (R = 0.11). To investigate its potential for clinical application, we applied the cellular MSI-H signature to the BJ-cohort, comprising 97 immunotherapy-treated gastrointestinal patients sequenced with a 395-gene panel. The cellular MSI-H score was significantly higher in responders (P = 0.002), positively correlated with tumor reduction percentage (R = 0.29, P = 0.006), and associated with improved progression-free survival (PFS) (HR: 0.00, 95% CI: 0.00-0.31, P = 0.021). In summary, the cellular MSI-H score reflects the MSI-H cell level within a tumor and demonstrates superior accuracy compared to molecular MSI-H status in predicting immunotherapy response and PFS. This underscores its potential as a more robust biomarker for guiding immunotherapy decisions.

Gastric cancer with liver metastasis (GCLM) often has a poor prognosis. Therefore, it is crucial to identify risk factors affecting their overall survival (OS) and cancer-specific survival (CSS). This study aimed to construct practical machine learning models to predict survival time and help clinicians choose appropriate treatments. We reviewed the clinical and survival data of GCLM patients from 2010 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) databases and divided the patients into training and testing groups. The risk factors affecting OS and CSS were determined by least absolute shrinkage and selector operator (LASSO), univariate cox regression, best subset regression (BSR) and the stepwise backward regression. Then, five machine learning models, including random survival forest (RSF), Gradient Boosting Machine (GBM), the Cox proportional hazard (CPH), Survival Support Vector Machine (survivalSVM), and eXtreme Gradient Boosting (XGBoost), were built using the identified risk factors. The model with the best predictive ability was determined using concordance index (c-index), area under the curve (AUC), brier score, and decision curve analysis (DCA), and externally verified with data from 233 cases diagnosed with liver metastasis of cancer from The Shijiazhuang People's Hospital, Jinan City People's Hospital, and The Sixth People's Hospital of Huizhou from 2017 to 2018. The study involved a total of 1300 GCLM patients. The prognostic risk factors affecting OS and CSS were the same, including grade, histology, T stage, N stage, surgery, and chemotherapy. The XGBoost model was found to have the best predictive ability for OS, with AUC of 0.891 [95% CI 0.841-0.941], brier score of 0.061 [95% CI 0.046-0.076], and c-index of 0.752 [95% CI 0.742-0.761], as well as for CSS, with AUC of 0.895 [95% CI 0.848-0.942], brier score of 0.064 [95% CI 0.050-0.079], and c-index of 0.746 [95% CI 0.736-0.756]. The AUC score, brier score and c-index all illustrated the accuracy of the model, and the validation using the external datasets further confirmed the reliability of the model. Therefore, the XGBoost model demonstrated significant potential in predicting survival times and selecting appropriate treatment plans.

Non-SMC condensing I complex subunit G (NCAPG) has been implicated in tumor progression. However, its role, potential mechanism and prognostic significance in human non-small cell lung cancer (NSCLC) remain elusive. Through the conjoint analysis of the TCGA and The Gene Expression Omnibus (GEO) databases, we confirmed that NCAPG is an upregulated gene. The prognostic value of NCAPG was elucidated through data analysis. The functional roles and mechanistic insights of NCAPG in NSCLC growth and metastasis were evaluated in vitro and in vivo. NCAPG expression was significantly increased in NSCLC. Multivariate Cox regression analysis demonstrated that NCAPG was an independent prognostic factor in patients with NSCLC. The high expression of NCAPG was significantly correlated with lymphatic metastasis. Additionally, the high expression of NCAPG effectively promoted the growth and metastasis of NSCLC in vitro and in vivo. In terms of mechanism, the interaction between NCAPG and Cyclin-dependent kinase 1 (CDK1) promotes the phosphorylation of Extracellular signal-regulated kinase (ERK). Overall, our results reveal the key role of NCAPG in NSCLC and highlight the regulatory function of the NCAPG/CDK1/ERK axis in regulating the progression of NSCLC, providing potential prognosis and therapeutic targets for the treatment of NSCLC.

Objective: To identify key risk factors for postoperative pulmonary infections (PPIs) in lung cancer (LC), patients undergoing radical surgery and construct a multiparametric nomogram model to improve PPI risk prediction accuracy, guiding individualized interventions.

Methods: A retrospective analysis was conducted on LC patients treated at Yidu Central Hospital of Weifang from March 2020 to May 2023. Among the 1,084 LC cases reviewed, patients were divided into an infected group (n = 131) and an uninfected group (n = 953) based on infection status. Key factors for PPIs were screened using machine learning techniques, including least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). A nomogram prediction model was developed, and its stability and clinical utility were evaluated using calibration curves and decision curve analysis, with internal validation through random case selection.

Results: Thirteen factors - including tumor stage, diabetes history, chronic obstructive pulmonary disease (COPD), operation duration, mechanical ventilation duration, age, C-reactive protein, procalcitonin, high-mobility group box 1, interleukin-6, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune-inflammation index - were identified as significantly associated with PPIs. The nomogram model demonstrated high predictive accuracy in internal validation (C-index = 0.935), strong calibration, and substantial clinical benefit. For two randomly selected cases, the model predicted a 63% infection probability for the infected patient and a 32% probability for the uninfected patient, affirming the model's predictive effectiveness.

Conclusions: The multiparametric nomogram model developed in this study provides a reliable method for PPI risk prediction in LC patients, supporting clinical decision-making and improving postoperative management.

Background: The emergence of immune resistance and a lack of effective therapeutic targets have become significant challenges in immunotherapy, highlighting the urgent need for new molecular markers and treatment targets. Moreover, the significance and mechanisms of PGRN (Progranulin) in gastric cancer remain ambiguous.

Objective: To identify differentially expressed proteins in gastric cancer and elucidate the function and mechanism of PGRN.

Methods: The data-independent acquisition proteomics was used to identify the differentially expressed proteins in gastric adenocarcinoma and the corresponding paraneoplastic tissues, providing a comprehensive dataset of gastric cancer-related proteins. The function and mechanism of PGRN in gastric cancer were further explored using a series of experiments, including RT-qPCR (Real Time-Quantitative Polymerase Chain Reaction), cell transfection, cell viability assays, cell scratch, immunohistochemistry and Transwell assays, Western blot, and a mouse tumor-bearing model. These investigations were combined with bioinformatics analyses to examine the relationship between PGRN expression and clinical-pathological characteristics, confirming its high expression of PGRN in gastric cancer tissues.

Results: We identified a large number of differentially expressed proteins between gastric cancer and adjacent tissues and conducted an initial functional analysis. Further studies on PGRN showed that it was associated with gastric cancer prognosis and lymph node metastasis. The inhibition of PGRN expression led to reduced cell viability, migration, and invasion, with corresponding changes in related genes and proteins. In a mouse tumor-bearing model, the tumor growth of the subcutaneously transplanted tumors in nude mice was reduced after the inhibition of PGRN expression. An in-depth functional analysis of PGRN was performed using bioinformatics to predict protein interactions, miRNA regulation, and relationships with multiple immune cell types. Enrichment analysis indicated that PGRN is involved in multiple signaling pathways, with the MAPK (Mitogen-Activated Protein Kinase) pathway selected for validation. In AGS and HGC27 cells, PGRN inhibition led to increased expression of phosphorylated p38 (p-p38) in the MAPK pathway, suggesting that PGRN may promote gastric cancer development by regulating p-p38.

Conclusions: This study identified significant differences in protein expression between gastric adenocarcinoma and adjacent tissues, with PGRN emerging as a key protein influencing gastric cancer proliferation, migration, and invasion. These findings suggest that PGRN could serve as a potential therapeutic target for gastric cancer.

Although diabetes mellitus (DM) is known to be related to the risk of many cancers, there are few studies on the risk of prostate cancer (PC) depending on the status of hyperglycemia, such as prediabetes and DM. Thus, the objective of this study was to determine the effect of each status of hyperglycemia on the risk of PC. In a Korean National Health Insurance Service database cohort, a total of 560,413 individuals who were followed until 2018 were analyzed. The risk of PC in patients with impaired fasting glucose (IFG) and new onset DM as well as all DM was determined. Associations of metabolic syndrome (MetS) components with the risk of PC according to glycemic status were evaluated. The association of anti-diabetic drugs with the incidence of PC was also analyzed. The presence of new-onset and all DM showed a significant reduction of the risk of PC in adjusted models. There was a trend that the presence of DM reduced the risk of PC regardless of the presence of MetS components. Regarding associations of anti-diabetic drugs with the incidence of PC, DM patients who were taking less than three drugs of oral hypoglycemic agents including metformin showed a reduced risk of PC compared to patients without using metformin. This study supports an inverse relationship between DM and the risk of PC. However, the risk of PC can be different depending on glycemic status and sorts of anti-diabetic drugs.