Abstract Purpose Colon cancer (CC) is a malignant tumor with high morbidity and mortality. Fatty acid metabolism, has attracted more attention as an essential part of tumor metabolic reprogramming. This study aimed to investigate the relationship between fatty acid metabolism-related genes and clinical survival outcomes in CC. Method We downloaded the mRNA expression profiles and clinical information of CC from the TCGA data portal. Expression of fatty acid metabolism-related genes and survival data of CC samples were extracted. Univariate Cox analysis and LASSO regression analysis were used to identify the fatty acid metabolism-related genes correlated with the prognosis of CC patients. Then, those six prognostic fatty acid metabolism-related genes were used to construct a prognostic model to predict the survival probability of CC patients. Patients were divided into two groups at high and low risk, and the differences in GSEA enrichment, drug sensitivity, immune cell infiltration, the efficacy of immunotherapy, and immune checkpoint expression level between the two groups were discussed. Finally, a novel nomogram integrating the risk score, age, gender, and clinical stages was established to predict the prognosis of CC patients. The Nomogram prediction model's accuracy was evaluated by using calibration plots, ROC curve, and DCA. Result 449 CC and 41 normal samples were included in this study. A prognostic model based on six fatty acid metabolism-related genes was built to evaluate the prognosis of CC patients. Patients in the high-risk group had poorer overall survival than those in the low-risk group (P < 0.001). The expression level of macrophages and T helper cells were higher, and the expression level of Tregs was lower in the high-risk group. The expression levels of PD-1, LAG3, and CTLA4 were higher in high-risk patients, and the high-risk group had a higher TIDE score, indicating a worse response to immunotherapy. The Calibration plots, ROC curve, and DCA have all proved that the Nomogram system can accurately predict the survival rate of CC patients. Conclusion Fatty acid metabolism-related genes can be used as a new therapeutic target for CC and further improve the survival rate of CC patients through individualized therapy.
Secreted protein, acidic and rich in cysteine (SPARC) has been characterized as an oncoprotein in esophageal squamous cell carcinoma (ESCC), but its involvement in the pathological development of esophageal adenocarcinoma (ESAD) remains poorly understood. In this study, we aimed to explore the sources of SPARC in the tumor microenvironment (TME) and its functional role in ESAD. Bioinformatic analysis was conducted using data from The Cancer Genome Atlas (TCGA)-esophageal cancer (ESCA) and Genotype-Tissue Expression (GTEx). ESAD tumor cell line OE33 and OE19 cells were used as in vitro cell models. Results showed that SPARC upregulation was associated with unfavorable disease-specific survival (DSS) in ESAD. ESAD tumor cells (OE33 and OE19) had no detectable SPARC protein expression. In contrast, IHC staining in ESAD tumor tissues suggested that peritumoral stromal cells (tumor-associated fibroblasts and macrophages) were the dominant SPARC source in TME. Exogenous SPARC induced partial epithelial-to-mesenchymal transition of ESAD cells, reflected by reduced CDH1 and elevated ZEB1/VIM expression at both mRNA and protein levels. Besides, exogenous SPARC enhanced tumor cell invasion. When TGFBR2 expression was inhibited, the activation of TGF-β signaling induced by exogenous SPARC was impaired. However, the activating effects were rescued by overexpressing mutant TGFBR2 resistant to the shRNA sequence. Copresence of exogenous SPARC and TGF-β1 induced higher expression of mesenchymal markers and enhanced the invading capability of ESAD cells than TGF-β1 alone. In conclusion, this study suggests a potential cross-talk between ESAD tumor stromal cells and cancer cells via a SPARC-TGF-β1 paracrine network.
We evaluated the prognostic value of m6A-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD).The expression levels of lncRNAs and mRNAs in LUAD and normal adjacent tissues from The Cancer Genome Atlas dataset were analyzed using the limma package. m6A enzyme-related differentially expressed lncRNAs and mRNAs were identified and used to construct a regulatory network. Survival analysis was performed and the correlation between lncRNAs, m6A regulators, and mRNAs was analyzed; followed by functional enrichment analysis.A comparison of LUAD samples and normal tissues identified numerous differentially expressed lncRNAs and mRNAs, demonstrating that a comprehensive network was established. Two lncRNAs and six mRNAs were selected as prognosis related factors including SH3PXD2A-AS1, MAD2L1, CCNA2, and CDC25C. The pathological stage and recurrence status were identified as independent clinical factors (P < 0.05). The expression levels of these RNAs in the different clinical groups were consistent with those in the different risk groups. The interactions of m6A proteins, two lncRNAs, and six mRNAs were predicted, and functional analysis showed that m6A target mRNAs were involved in the cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis pathways.These m6A target lncRNAs and mRNAs may be promising biomarkers for predicting clinical prognosis, and the lncRNA-m6A regulator-mRNA regulatory network could improve our understanding of m6A modification in LUAD progression.
Introduction Immune checkpoint inhibitor (ICI) therapy has been proven to be a highly efficacious treatment for colorectal adenocarcinoma (COAD). However, it is still unclear how to identify those who might benefit the most from ICI therapy. Hypoxia facilitates the progression of the tumor from different aspects, including proliferation, metabolism, angiogenesis, and migration, and improves resistance to ICI. Therefore, it is essential to conduct a comprehensive understanding of the influences of hypoxia in COAD and identify a biomarker for predicting the benefit of ICI. Methods An unsupervised consensus clustering algorithm was used to identify distinct hypoxia-related patterns for COAD patients from TCGA and the GEO cohorts. The ssGSEA algorithm was then used to explore the different biological processes, KEGG pathways, and immune characteristics among distinct hypoxia-related clusters. Some hypoxia-related hub genes were then selected by weighted gene coexpression network analysis (WGCNA). Subsequently, univariate Cox regression analysis, multivariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression were utilized to construct a hypoxia-related gene prognostic index (HRGPI). Finally, validation was also conducted for HRGPI in prognostic value, distinguishing hypoxia-related characteristics and benefits of ICI. Results We identified four hypoxia-related clusters and found that different hypoxia response patterns induced different prognoses significantly. Again, we found different hypoxia response patterns presented distinct characteristics of biological processes, signaling pathways, and immune features. Severe hypoxia conditions promoted activation of some cancer-related signaling pathways, including Wnt, Notch, ECM-related pathways, and remodeled the tumor microenvironment of COAD, tending to present as an immune-excluded phenotype. Subsequently, we selected nine genes (ANO1, HOXC6, SLC2A4, VIP, CD1A, STC2, OLFM2, ATP6V1B1, HMCN2) to construct our HRGPI, which has shown an excellent prognostic value. Finally, we found that HRGPI has an advantage in distinguishing immune and molecular characteristics of hypoxia response patterns, and it could also be an excellent predictive indicator for clinical response to ICI therapy. Conclusion Different hypoxia response patterns activate different signaling pathways, presenting distinct biological processes and immune features. HRGPI is an independent prognostic factor for COAD patients, and it could also be used as an excellent predictive indicator for clinical response to ICI therapy.
Objective: To compare the effects of 2 techniques of semi-hepatic alternating radiotherapy on diffuse hepatic metastasis in patients with breast cancer. Methodology: A total of 68 breast cancer patients with diffuse liver metastasis were randomly divided into Group A (semi-hepatic alternating radiotherapy) and Group B (semi-hepatic sequential radiotherapy). In Group A (semi-hepatic sequential radiotherapy), the liver was divided into the first semi-liver and second semi-liver and alternatively treated with semi-hepatic intensity-modulated radiation therapy (IMRT). The interval between the 2 instances of semi-hepatic radiotherapy was 6 h. The average radiotherapy dose to the semi-livers was both 2 Gy/fraction, once a day, 5 times per week, with a total dose of 30 Gy for 15 days. The total radiation therapy time in Group A was 15 days in Group B (semi-hepatic sequential radiotherapy), the livers were divided into the first semi-liver and second semi-liver and treated with semi-hepatic sequential IMRT, The first semi-liver was first treated in the initial stage of radiation therapy, the average radiotherapy dose to the semi-liver was 2 Gy/fraction, once a day, 5 times per week, with a total dose of 30 Gy for 15 days. The second semi-liver was treated next in the second stage of radiation therapy, the average radiotherapy dose to the semi-liver was 2 Gy/fraction, once a day, 5 times per week, with a total dose of 30 Gy for 15 days. The total radiation therapy time in group B was 30 days. Results: The objective response rate (complete response + partial response) of Group A and Group B were 50.0% and 48.5%, respectively ( p = .903). The median survival time after metastasis (median survival of recurrence) of Group A and Group B was 16.7 months and 16.2 months, respectively ( p = .411). The cumulative survival rates of 6 months, 1 year, 2 years, and 3 years of Group A and Group B were 90.6% (29 of 32) and 84.8% (28 of 33) ( p = .478), 65.6% (21 of 32) and 60.6% (20 of 33) ( p = .675), 31.2% (10 of 32) and 27.3% (9 of 33) ( p = .725), and 15.6% (5 of 32) and 0 (0 of 33) ( p = .018), respectively. The differences between the 2 groups showed no statistical significance in terms of cumulative survival rates in 1 year, 2 years, however, the 3-year survival rate was significantly different. The main toxic reactions were digestive tract reactions, abnormal liver functions, and myelosuppression. The incidence of I to II degree gastrointestinal reactions was 78.13% (25 of 32) in Group A and 72.73% (24 of 33) in Group B ( p = .614). The incidence of I to II abnormal liver function was 53.13% (17 of 32) in Group A and 48.48% (16 of 33) in Group B ( p = .708). The differences between the 2 groups showed no statistical significance. The incidence of I to II myelosuppression was 59.38% (19 of 32) in Group A and 51.52% (17 of 33) in Group B ( p = .524), respectively. The differences between the 2 groups showed no statistical significance in terms of adverse effects. Conclusion: Semi-hepatic alternating IMRT was an effective palliative treatment for diffuse liver metastasis in patients with breast cancer. Semi-hepatic alternating radiotherapy showed a trend of prolonged survival time when compared with semi-hepatic sequential radiotherapy. Compared with the former, the latter showed a trend of lower incidences of side effects without any statistical differences. Moreover, the side effects from the 2 radiotherapy techniques can be controlled through appropriate management, which is worthy of further exploration and applications.
Abstract Purpose Research on bone metastasis in esophageal cancer (EC) is relatively limited. Once bone metastasis occurs in patients, their prognosis is poor, and it severely affects their quality of life. Currently, there is a lack of convenient tumor markers for early identification of bone metastasis in EC. Our research aims to explore whether neutrophil-lymphocyte ratio (NLR) can predict bone metastasis in patients with EC. Methods Retrospective analysis of clinical indicators was performed on 604 patients with EC. They were divided into groups based on whether or not there was bone metastasis, and the patients' coagulation-related tests, blood routine, tumor markers and other indicators were collected. The receiver operating characteristic curve (ROC) were used to determine the predictive ability of parameters such as NLR for bone metastasis in EC, and univariate and multivariate logistic regression analyses were conducted to determine the impact of each indicator on bone metastasis. Using binary logistic regression to obtain the predictive probability of NLR combined with tumor markers. Results ROC curves analysis suggested that the area under the curve (AUC) of the NLR was 0.681, with a sensitivity of 79.2% and a specificity of 52.6%, which can be used as a predictive factor for bone metastasis in EC. Multivariate logistic regression analysis showed that high NLR (odds ratio [OR]: 2.608, 95% confidence interval [CI]: 1.395–4.874, P = 0.003) can function as an independent risk factor for bone metastasis in patients with EC. Additionally, high PT, high APTT, high FDP, high CEA, high CA724, low hemoglobin, and low platelet levels can also predict bone metastasis in EC. When NLR was combined with tumor markers, the area under the curve was 0.760 (95% CI: 0.713–0.807, P < 0.001), significantly enhancing the predictability of bone metastasis in EC. Conclusion NLR, as a convenient, non-invasive, and cost-effective inflammatory indicator, could predict bone metastasis in EC. Combining NLR with tumor markers can significantly improve the diagnostic accuracy of bone metastasis in EC.
Lung adenocarcinoma (LUAD) is the most predominant pathological subtype of lung cancer, accounting for 40-70% of all lung cancer cases. Although significant improvements have been made in the screening, diagnosis, and precise management in recent years, the prognosis of LUAD remains bleak. This study aimed to investigate the prognostic significance of autophagy-related long non-coding RNAs (lncRNAs) and construct an autophagy-related lncRNA prognostic model in LUAD.The gene expression data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database. All autophagy-related genes were downloaded from the Human Autophagy Database (HADb). Spearman's correlation test was exploited to identify potential autophagy-related lncRNAs. The multivariate Cox regression analysis was used to construct the prognostic signature, which divided LUAD patients into high-risk and low-risk groups. Subsequently, the receiver operating characteristic (ROC) curves were generated to assess the predictive ability of this prognostic model for overall survival (OS) in these individuals. Then, the Gene set enrichment analysis (GSEA) was conducted to execute pathway enrichment analysis. Finally, a multidimensional validation was exploited to verify our findings.A total of 1,144 autophagy-related lncRNAs were identified to construct the co-expression network via Spearman's correlation test (|R2| >0.4 and P≤0.001). Ultimately, a 16 autophagy-related lncRNAs prognostic model was constructed, and the area under the ROC curve (AUC) was 0.775. The results of GSEA enrichment analysis showed that the genes in the high-risk group were mainly enriched in cell cycle and p53 signaling pathways. The results of the multidimensional database validation indicated that the expression level of BIRC5 was significantly correlated with the expression level of TMPO-AS1. Furthermore, both TMPO-AS1 and BIRC5 had a higher expression level in LUAD samples. LUAD patients with high expression levels of TMPO-AS1 and BIRC5 were correlated with advanced disease stage and poor OS.In summary, our results suggested that the prognostic signature of the 16 autophagy-related lncRNAs has significant prognostic value for LUAD patients. Furthermore, TMPO-AS1 and BIRC5 are potential predictors and therapeutic targets in these individuals.
Background: In consideration of the scarceness and importance of histological analysis, the clinic pathological features of pulmonary spindle cell carcinoma (PSCC) were comprehensively analyzed in the present work to improve the treatment and deepen our understanding of the disease.
Abstract Cervical squamous cell carcinoma and endocervical adenocarcinoma(CESC) is one of the more common tumors in women worldwide and has a higher mortality rate. However, there is a paucity of information about specific biomarkers that assist in the diagnosis and prognosis of CESC. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis is a form of programmed cell death, and its different elements are related to the occurrence, invasion and metastasis of tumors. However, the role of pyroptosis in CESC progression has not been clarified. The focus of this study is to use comprehensive bioinformatics to develop pyroptosis prognostic characteristics of CESC, so as to delineate the relationship among this characteristic, tumor microenvironment and immune response of patients. In combination with clinical characteristics, risk score is an independent predictor of OS in patients with CESC. Pyroptosis Genes(PRG) score was significantly correlated with immune score, immune infiltration, immune microenvironment, cancer stem cell (CSC) index, and chemotherapeutic drug sensitivity. These findings may improve our understanding of PRGs in CESC and provide new avenues for assessing prognosis and developing more effective immunotherapeutic strategies.
Introduction: Kidney renal clear cell carcinoma (KIRC), a kind of malignant disease, is a severe threat to public health. Tracking the information of tumor progression and conducting a related dynamic prognosis model are necessary for KIRC. It is crucial to identify hypoxia-immune-related genes and construct a prognostic model due to immune interaction and the influence of hypoxia in the prognosis of patients with KIRC. Methods: The hypoxia and immune status of KIRC patients were identified by utilizing t-SNE and ImmuCellAI for gene expression data. COX and Lasso regression were used to identify some hypoxia-immune-related signature genes and further construct a prognostic risk model based on these genes. Internal and external validations were also conducted to construct a prognostic model. Finally, some potentially effective drugs were screened by the CMap dataset. Results: We found that high-hypoxia and low-immune status tend to induce poor overall survival (OS). Six genes, including PLAUR, UCN, PABPC1L, SLC16A12, NFE2L3, and KCNAB1, were identified and involved in our hypoxia-immune-related prognostic risk model. Internal verification showed that the area under the curve (AUC) for the constructed models for 1-, 3-, 4-, and 5-year OS were 0.768, 0.754, 0.775, and 0.792, respectively. For the external verification, the AUC for 1-, 3-, 4-, and 5-year OS were 0.768, 0.739, 0.763, and 0.643 respectively. Furthermore, the decision curve analysis findings demonstrated excellent clinical effectiveness. Finally, we found that four drugs (including vorinostat, fludroxycortide, oxolinic acid, and flutamide) might be effective and efficient in alleviating or reversing the status of severe hypoxia and poor infiltration of immune cells. Conclusion: Our constructed prognostic model, based on hypoxia-immune-related genes, has excellent effectiveness and clinical application value. Moreover, some small-molecule drugs are screened to alleviate severe hypoxia and poor infiltration of immune cells.
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