Traditional TNM Staging System Research Articles

Significance of Pre-Treatment CALLY Score Combined with EBV-DNA Levels for Prognostication in Non-Metastatic Nasopharyngeal Cancer Patients: A Clinical Perspective.

The C-reactive protein-albumin-lymphocyte (CALLY) score is a novel indicator associated with inflammation, immunity, and nutrition, utilized for cancer prognostic stratification. This study aimed to evaluate the integrated prognostic significance of the pre-treatment CALLY score and Epstein-Barr virus (EBV) DNA levels in nasopharyngeal carcinoma (NPC) patients and to develop prognostic models. A total of 1707 NPC patients from September 2015 to December 2017 were retrospectively enrolled. The cut-off point for the CALLY score, determined by maximum selected rank statistics, integrates with the published cut-off point for pre-EBV DNA to develop a comprehensive index. Subsequently, patients were randomly allocated in a 1:1 ratio into training and validation cohorts. Survival analysis was conducted using the Kaplan-Meier method with Log rank tests, and the Cox proportional hazards model was applied to identify independent prognostic factors for constructing predictive nomograms. The predictive ability of the nomograms were assessed through the concordance index (C-index), calibration curves, and decision curve analysis. By integrating CALLY scores and EBV-DNA levels, patients were categorized into three risk clusters. Kaplan-Meier curves reveal significant differences in overall survival (OS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRRFS) outcomes among different risk groups (all P values < 0.05). Multivariate analysis revealed that CALLY-EBV DNA index serves as an independent prognostic factor for the OS, DMFS, and LRRFS. The prognostic nomograms based on the CALLY-EBV DNA index provided accurate predictions for 1-year, 3-year, and 5-year OS, DMFS, and LRRFS. Additionally, compared to the traditional TNM staging system, the nomograms exhibited enhanced discriminatory power, calibration capability, and clinical applicability. All results were in agreement with the validation cohort. The CALLY-EBV DNA index is an independent prognostic biomarker. The nomogram prediction models, constructed based on the CALLY-EBV DNA index, demonstrates superior predictive performance compared to the traditional TNM staging.

Prognostic nomogram in patients with right-sided colon cancer after colectomy: a surveillance, epidemiology, and end results-based study.

This study aimed to develop and validate a nomogram for predicting overall survival (OS) in patients undergoing surgery for right-sided colon cancer (RCC). We collected 25,203 patients with RCC from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided them into 7:3 training and internal validation set. Utilizing the Cox proportional hazards regression model, we constructed a nomogram based on prognostic risk factors. Furthermore, for external validation, we retrospectively followed up with 228 patients from Jiaxing First Hospital and assessed and calibrated the nomogram using the C-index and calibration curves. After identifying independent prognostic factors through univariate and multivariate analyses, a nomogram was developed. The c-index values of this nomogram differed as follows: 0.851 (95% CI: 0.845-0.857) in the training set, 0.860 (95% CI: 0.850-0.870) in the internal validation set, and 0.834 (95% CI: 0.780-0.888) in the external validation set, indicating the model's strong discriminative ability. Calibration curves for 1-year, 3-year, and 5-year overall survival (OS) probabilities exhibited a high level of consistency between predicted and actual survival rates. Furthermore, Decision Curve Analysis (DCA) demonstrated that the new model consistently outperformed the TNM staging system in terms of net benefit. We developed and validated a survival prediction model for patients with RCC. This novel nomogram outperforms the traditional TNM staging system and can guide clinical practitioners in making optimal clinical decisions.

A recurrence-predictive model based on eight genes and tumor mutational burden/microsatellite instability status in Stage II/III colorectal cancer.

Although adjuvant chemotherapy (ACT) is widely used to treat patients with Stage II/III colorectal cancer (CRC), administering ACT to specific patients remains a challenge. The decision to ACT requires an accurate assessment of recurrence risk and absolute treatment benefit. However, the traditional TNM staging system does not accurately assess a patient's individual risk of recurrence. To identify recurrence risk-related genetic factors for Stage II/III CRC patients after radical surgery, we conducted an analysis of whole-exome sequencing of 47 patients with Stage II/III CRC who underwent radical surgery at five institutions. Patients were grouped into non-recurrence group (NR, n = 24, recurrence-free survival [RFS] > 5 years) and recurrence group (R, n = 23, RFS <2 years). The TCGA-COAD/READ cohort was employed as the validation dataset. A recurrence-predictive model (G8plus score) based on eight gene (CUL9, PCDHA12, HECTD3, DCX, SMARCA2, FAM193A, AATK, and SORCS2) mutations and tumor mutation burden/microsatellite instability (TMB/MSI) status was constructed, with 97.87% accuracy in our data and 100% negative predictive value in the TCGA-COAD/READ cohort. For the TCGA-COAD/READ cohort, the G8plus-high group had better RFS (HR = 0.22, p = 0.024); the G8plus-high tumors had significantly more infiltrated immune cell types, higher tertiary lymphoid structure signature scores, and higher immunological signature scores. The G8plus score was also a predict biomarker for immunotherapeutic in advanced CRC in the PUCH cohort. In conclusion, the G8plus score is a powerful biomarker for predicting the risk of recurrence in patients with stage II/III CRC. It can be used to stratify patients who benefit from ACT and immunotherapy.

Immunological role and clinical prognostic significance of P2RY6 in lung adenocarcinoma: a multi-omics studies and single-cell sequencing analysis

BackgroundThere is increasing evidence that recombinant human P2Y purinoceptor 6 (P2RY6) may be involved in inflammatory responses. However, the role of P2RY6 in lung adenocarcinoma (LUAD) remains unknown.MethodsWe used transcriptomic, genomic, single-cell transcriptomic, and methylation sequencing data from The Cancer Genome Atlas database to analyze the aberrant status and prognostic value of P2RY6 in a variety of tumors. The LUAD single-cell sequencing dataset was used to explore the effect of P2RY6 on the tumor microenvironment. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify immune cells in the tumor microenvironment. We also analyzed the correlation of P2RY6 with immune checkpoints and immune regulation-related genes. The correlation of between tumor mutation burden (TMB), microsatellite instability (MSI), and P2RY6 expression was also analyzed simultaneously. Tissue microarray and immunohistochemistry were employed to assess the expression of P2RY6 in internal tumor samples.ResultsOur findings indicate that P2RY6 exhibits significantly higher expression levels in various cancer tissues, particularly in LUAD. High expression of P2RY6 was closely associated with a poor prognosis for patients, and it plays a role in regulating immune-related pathways, such as cytokine-cytokine receptor interaction. Notably, P2RY6 expression is closely linked to the abundance of CD8 + T cells. Furthermore, we have developed a P2RY6-related inflammation prediction model that demonstrates promising results in predicting the prognosis of LUAD patients, with an AUC (area under the curve) value of 0.83. This performance is significantly better than the traditional TNM staging system. Through single-cell transcriptome sequencing analysis, we observed that high P2RY6 expression is associated with increased intercellular communication. Additionally, pathway enrichment analysis revealed that P2RY6 influences antigen presentation and processing pathways within the LUAD microenvironment.ConclusionsThis study suggests that P2RY6 would be a new target for immunotherapy in LUAD.

A Novel Albumin-Related Nutrition Biomarker Predicts Breast Cancer Prognosis in Neoadjuvant Chemotherapy: A Two-Center Cohort Study.

Recently, there has been a growing focus on the prognostic significance of nutrition-related biomarkers. We attempted to explore the association between a novel albumin-related nutrition marker called "lymphocyte × albumin (LA)" and disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). In total, 711 non-metastatic breast cancer patients who underwent NAC at two medical centers were retrospectively analyzed. We performed least absolute shrinkage and selection operator (LASSO) Cox regression analysis as well as multivariate Cox regression analyses to identify the variables associated with DFS and to establish a predictive nomogram. The nomogram incorporated four variables based on the multivariate analysis of DFS in the training cohort: LA, ypN stage, ypT stage, and hormone receptor status. In comparison with the traditional TNM staging system, the nomogram demonstrated superior discrimination, calibration ability, and clinical usefulness in both the training set and internal and external validation sets. Furthermore, patients stratified into different risk groups resulted in significant differences in DFS. LA is an independent prognostic biomarker, and LA-based prognostic nomogram offers a more precise assessment of DFS for breast cancer patients treated with NAC, potentially serving as a valuable tool for personalized prognostic predictions.

A new lymph node ratio-based staging system for rectosigmoid cancer: a retrospective study with external validation.

This study evaluated the clinical value of a new American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging prediction model based on lymph node ratio (LNR) in rectosigmoid cancer (RSC). The analysis included 1444 patients with nonmetastatic RSC diagnosed pathologically between 2010 and 2016 who were collected from the National Cancer Institute Surveillance, Epidemiology, and Results database. The AJCC N-stage was redefined according to the LNR cutoff point, and the ability of the new staging system to predict prognosis was compared with that of the AJCC TNM staging system. Data from 739 patients from our hospital were used for external validation. According to the number of examined lymph nodes and LNR, the N stage was divided into five groups (LNR0-5). The 5-year OS of patients divided according to the new T lymph node ratio M (TLNRM) staging into stage I (T1LNR1, T1LNR2), IIA (T1LNR3, T2LNR1, T2LNR2, T2LNR3, T1LNR4, T3LNR1), IIB (T2LNR4), IIC (T3LNR2, T4a LNR1, T1LNR5), IIIA (T3LNR3, T2LNR5, T4b LNR1, T4a LNR2, T3LNR4), IIIB (T3LNR5, T4a LNR3, T4a LNR4, T4b LNR2), and IIIC (T4b LNR3, T4a LNR5, T4b LNR4, T4b LNR5) was significantly different ( P <0.05). Decision curve analysis showed that the net income of the new TLNRM staging system for different decision thresholds was higher than the prediction line of the traditional eighth TNM staging system. The smaller Akaike information criterion and Bayesian information suggested that the new staging system had a higher sensitivity for predicting prognosis than the traditional staging system. TLNRM II and III patients benefited from adjuvant chemotherapy, while adjuvant chemotherapy did not improve the prognosis of TNM II patients. These findings were confirmed by the external validation data. The new TLNRM staging system was superior to the eighth edition AJCC staging system for staging and predicting the prognosis of patients with RSC and may become an effective tool in clinical practice.

Construction of the survival nomograms for colon cancer patients of different ages based on the SEER database

IntroductionThree nomograms for predicting the outcomes of early- and late-onset colon cancer (COCA) among patients not stratified by age were constructed using data in the Epidemiology and End Results (SEER) database (1975–2019). The accuracy of the nomogram was then assessed.MethodClinical data of 6107 patients with COCA were obtained from the SEER database. The patients were randomly divided into training and validation cohorts in a ratio of 7:3. Univariate and multivariate COX analyses of factors that could independently impact the prognosis of COCA were performed, and the corresponding nomograms for early-onset and late-onset COCA were constructed. Calibration curves, ROC curves, and C-index were used to determine the predictive accuracy. The discriminatory ability of the nomograms to assess their clinical utility, which was compared with the TNM staging system of the 8th edition of AJCC, was verified using survival analysis.ResultTumor primary site, ethnicity, and serum carcinoembryonic antigen (CEA) level significantly impacted the prognosis of colon cancer. Race, brain metastasis, and CEA were independent factors for predicting COCA prognosis. C-index, ROC, and calibration curves demonstrated that the three nomograms were accurate and superior to the traditional TNM staging system. Among the three nomograms, the early-onset COCA nomogram had the highest predictive accuracy, followed by that of colon cancer not stratified by age.ConclusionThree nomograms for patients not stratified by age, early-onset colon cancer, and late-onset colon cancer were constructed. The accuracies of the nomograms were good and were all superior to the conventional TNM staging system. The early- and late-onset COCA nomograms are useful for clinical management and individualized treatment of COCA patients at different ages.

The value of CRP-albumin-lymphocyte index (CALLY index) as a prognostic biomarker in patients with non-small cell lung cancer.

The C-reactive protein-albumin-lymphocyte (CALLY) index is a new index related to inflammation, immunity, and nutrition. We investigated whether it can predict the prognosis of patients with non-small cell lung cancer (NSCLC) and developed a prognostic model including CALLY index. Data from patients with NSCLC who were followed up in the INSCOC database from May 2013 to December 2018 were retrospectively analyzed. Simple random sampling by splitting these patients into training (n = 1307) and validation cohorts (n = 557) resulted in a sample size ratio of 7:3. Using the results of COX regression analysis of the training cohort, a nomogram model for predicting 3- and 5-year overall survival (OS) was established and validated internally. The calibration and clinical decision curve were used to evaluate the prediction accuracy and clinical application ability of the nomogram and compared with the TNM staging system for lung cancer. Sex, TNM stage, surgical treatment, BMI, CALLY, and HGS were independent risk factors for the prognosis of NSCLC patients. The OS of NSCLC patients with a low CALLY index score was significantly worse than that of patients with a high CALLY index (P < 0.001). The CALLY-based nomogram had a good predictive prognostic power, with a C-index of 0.697. Compared with the traditional TNM staging system, our prognostic nomogram had better resolution and accuracy in predicting the 3-year and 5-year OS. Decision curve analysis showed that this prognostic model has a clinical application value. The CALLY index is a valuable biomarker for evaluating the prognosis of patients with lung cancer. The nomogram based on the CALLY index is highly effective in predicting OS in patients with NSCLC. The results of this study provide a reference tool for clinicians to guide the personalized treatment of patients with lung cancer.

Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm.

The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN). A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated. The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice. The R-signature could be used to stratify patients with GNEN into high and low risk groups. Furthermore, the combined radiomics-clinical nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.

Development and validation of a prognostic nomogram for gastrointestinal stromal tumors in the postimatinib era: A study based on the SEER database and a Chinese cohort.

After the standardization, recording and follow-up of imatinib use that significantly prolongs survival of gastrointestinal stromal tumors (GISTs), a comprehensive reassessment of the prognosis of GISTs is necessary and more conductive to treatment options. A total of 2185 GISTs between 2013 and 2016 were obtained from the Surveillance, Epidemiology, and End Results database and comprised our training (n = 1456) and internal validation cohorts (n = 729). The risk factors extracted from univariate and multivariate analyses were used to establish a predictive nomogram. The model was evaluated and tested in the validation cohort internally and in 159 patients with GIST diagnosed between January 2015 and June 2017 in Xijing Hospital externally. The median OS was 49 months (range, 0-83 months) in the training cohort and 51 months (0-83 months) in the validation cohort. The concordance index (C-index) of the nomogram was 0.777 (95% CI, 0.752-0.802) and 0.7787 (0.7785, bootstrap corrected) in training and internal validation cohorts, respectively, and 0.7613 (0.7579, bootstrap corrected) in the external validation cohort. Receiver operating characteristic curves and calibration curves for 1-, 3-, and 5-year overall survival (OS) showed a high degree of discrimination and calibration. The area under the curve showed that the new model performed better than the TNM staging system. In addition, the model could be dynamically visualized on a webpage. We developed a comprehensive survival prediction model for assessing the 1-, 3- and 5-year OS of patients with GIST in the postimatinib era. This predictive model outperforms the traditional TNM staging system and sheds light on the improvement of the prognostic prediction and the selection of treatment strategies for GISTs.

Prognostic analysis and nomogram establishment in patients with early esophageal cancer receiving endoscopic therapy: a population-based study.

The growing numbers of early esophageal cancer (EEC) have increased the demand for endoscopic therapy. To clarify the influential factors for the prognosis of patients with EEC receiving endoscopic surgery, and to construct a nomogram to evaluate the prognostic value of endoscopic therapy. Prognostic analysis study. Clinical data of EEC patients who received endoscopic therapy between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database and used to construct the nomogram. The prognosis was analyzed by R language; the nomogram was constructed by Cox survival analysis; and the accuracy of the nomogram was verified by C index and the receiver operating characteristic (ROC) and calibration curves. X-Tile software was used to stratify the risk of patients. Our study constructed the nomogram of the prognosis of patients with EEC treated by endoscopic surgery, including 1118 patients and 5 independent prognostic factors of esophageal cancer-specific survival. The C index and the area under the ROC curve (AUC) of the training and verification cohorts were all >0.75. The calibration curve also reflected the good consistency of the model in predicting survival. Significant difference in the risk of patients from different stratifications with the same T staging existed, and the model had a better C index than that of the T staging. Our study reports potential influential factors affecting the prognosis of EEC patients who received endoscopic therapy and establishes a reliable nomogram to predict the risk and prognosis, which has certain advantages compared with traditional TNM staging system.

Prospective Investigation of 18FDG-PET/MRI with Intravoxel Incoherent Motion Diffusion-Weighted Imaging to Assess Survival in Patients with Oropharyngeal or Hypopharyngeal Carcinoma.

To prospectively investigate the prognostic value of 18F-FDG PET/MRI in patients with oropharyngeal or hypopharyngeal squamous cell carcinomas (OHSCC) treated by chemoradiotherapy. The study cohort consisted of patients with OHSCC who had undergone integrated PET/MRI prior to chemoradiotherapy or radiotherapy. Imaging parameters derived from intravoxel incoherent motion (IVIM), dynamic contrast-enhanced MRI (DCE-MRI), and 18F-FDG PET were analyzed in relation to overall survival (OS) and recurrence-free survival (RFS). In multivariable analysis, T classification (p &lt; 0.001), metabolic tumor volume (p = 0.013), and pseudo-diffusion coefficient (p = 0.008) were identified as independent risk factors for OS. The volume transfer rate constant (p = 0.015), initial area under the curve (p = 0.043), T classification (p = 0.018), and N classification (p = 0.018) were significant predictors for RFS. The Harrell's c-indices of OS and RFS obtained from prognostic models incorporating clinical and PET/MRI predictors were significantly higher than those derived from the traditional TNM staging system (p = 0.001). The combination of clinical risk factors with functional parameters derived from IVIM and DCE-MRI plus metabolic PET parameters derived from 18F-FDG PET in integrated PET/MRI outperformed the information provided by traditional TNM staging in predicting the survival of patients with OHSCC.

A prognostic nomogram to predict survival in elderly patients with small-cell lung cancer: a large population-based cohort study and external validation

BackgroundAge is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database.MethodsA total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA).ResultsTen independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks.ConclusionWe constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.

A nomogram and risk classification system for predicting cancer-specific survival in tall cell variant of papillary thyroid cancer: a SEER-based study.

Tall cell variant (TCV) of papillary thyroid cancer (PTC) is the most common aggressive subtype of PTC. The factors that affect survival of patients with TCV remain unclear. We aimed to develop a model to predict the cancer-specific survival (CSS). A total of 1615 patients diagnosed with TCV between 2004 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomized into training and validation cohorts (7:3). A predictive nomogram for predicting CSS was constructed by Cox proportional hazards regression and validated by concordance index (C-index), calibration curve, and decision curve analyses (DCA). A risk classification system was built based on the total nomogram scores of each case. A nomogram was constructed including five independent prognostic factors (age, tumor size, T stage, M stage, and extent of surgery) associated with CSS in TCV patients. Various validations proved that the nomogram model had good consistency and discrimination for TCV prognosis. The risk classification system could perfectly classify TCV patients into three risk groups with significantly different CSS. Compared with traditional AJCC TNM staging system, the nomogram could better predict CSS in TCV patients. A nomogram and corresponding risk classification system were developed for predicting CSS in TCV patients. The model has excellent performance and can be used to help clinicians make accurate prognostic assessment and individualized treatment.

Clinical characteristics and prognostic nomogram for patients with insular thyroid carcinoma: a population-based analysis.

Insular thyroid carcinoma (ITC) is an uncommon poorly differentiated thyroid malignancy. Due to its rarity, its demographic and clinicopathological features and survival remains unclear. The present study aimed to describe the features and survival of ITC, determine its prognostic factors, and establish a prognostic nomogram. Patients with ITC were identified in the Surveillance, Epidemiology, and End Results database from 2004 to 2019. The features and survival of patients with ITC and other thyroid carcinomas were compared after balancing the baseline characteristics using propensity score matching (PSM). Univariate and multivariate Cox analyses were used to identify the prognostic factors for ITC. Moreover, overall survival (OS)- or cancer-specific survival (CSS)-specific nomograms were established to predict ITC prognosis. A total of 206 patients with ITCs were identified. The 1-, 2-, 5-, and 10-year OS rates of 206 patients with ITC were 90.3%, 82.0%, 62.2%, and 42.5%, respectively. The median OS was 93 months (95% CI, 73.0-140.0), while the median CSS was 141 months (95% CI, 93.0-173.0). After PSM analysis, the survival analysis of the matched cohort revealed that ITC had a worse clinical outcome than papillary thyroid cancer and follicular thyroid cancer, and better survival than anaplastic thyroid carcinoma. Multivariate Cox regression analysis demonstrated that age, N stage, M stage, and surgery were independent prognostic factors for both OS and CSS in ITC patients. The C-indices for the OS- and CSS-specific nomograms were 0.778 (95% CI, 0.724-0.832) and 0.808 (95% CI, 0.754-0.862), respectively. The calibration curve and ROC analysis indicated that the nomogram models exhibited a good discriminative ability. Decision curve analysis suggested that the nomogram models had a significant positive net benefit and were better than the traditional TNM staging system at predicting survival. ITC has distinct clinicopathological characteristics and survival compared to other thyroid carcinomas, and the established nomogram could predict the survival probability of patients with ITC accurately with a higher net benefit.

Reclassifying TNM stage I/II colorectal cancer into two subgroups with different overall survival, tumor microenvironment, and response to immune checkpoint blockade treatment

Background: The traditional TNM staging system is often insufficient to differentiate the survival discrepancies of colorectal cancer (CRC) patients at TNM stage I/II. Our study aimed to reclassify stage I/II CRC patients into several subgroups with different prognoses and explore their suitable therapeutic methods. Methods: Single-cell RNA (scRNA) sequencing data, bulk RNA sequencing data, and clinicopathological information of CRC patients were enrolled from the TCGA and GEO databases. The tumor microenvironment of CRC tissues was accessed by the ESTIMATE algorithm. The prognostic genes were identified by Cox regression analysis. GO and KEGG analyses were conducted in the DAVID database. GSEA analysis was performed for annotation of the correlated gene sets. Results: We successfully reclassified stage I/II CRC patients into two subgroups and discovered that patients in cluster-2 underwent worse overall survival than those in cluster-1. GSEA analysis showed that immune-associated gene sets were positively enriched in cluster-2. Besides, the differentially expressed genes (DEGs) between cluster-1 and cluster-2 patients also participated in immune-related biological processes and signaling pathways. Moreover, we found that more immune cells infiltrated the microenvironment of cluster-2 patients compared to that of cluster-1 patients, such as Tregs and tumor-associated macrophages. ScRNA sequencing analysis uncovered that most of the enriched immune-associated signaling in cluster-2 patients was mainly attributed to these upregulated immune cells whose infiltration levels were also high in CRC tissues rather than in normal tissues. In addition, we demonstrated that the expression of immune checkpoint genes was significantly higher in cluster-2 patients compared to cluster-1 patients. ScRNA sequencing analysis revealed that the infiltrated CD8+T cells in CRC were naïve T cells and can be activated into effector T cells after immune checkpoint blockade (ICB) treatment. Conclusion: TNM stage I/II CRC patients can be divided into two subgroups, which have different overall survival rates, tumor microenvironment, and response to ICB therapy.

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

ObjectiveThis study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC).MethodsFor the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.ResultsA Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718–0.735) and 0.721 (95% CI, 0.709–0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691–0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system.ConclusionWe developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

Identification of Fatty Acid Metabolism-Related lncRNAs as Biomarkers for Clinical Prognosis and Immunotherapy Response in Patients With Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with poor prognosis. Fatty acid metabolism is associated with cancer progression and a poor prognosis. We searched for long noncoding RNAs (lncRNAs) associated with fatty acid metabolism to predict the overall survival (OS) of patients with LUAD. We obtained lncRNA expression profiles and clinical follow-up data related to fatty acid metabolism in patients with LUAD from The Cancer Genome Atlas and Molecular Signatures database. Patients were randomly divided into training, experimental, and combination groups. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression models were used to construct fatty acid metabolism-related prognostic markers, Kaplan-Meier analysis was used to compare the prognosis of each group, and receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the prognostic model. We used the pRRophetic algorithm to assess the treatment response based on the half-maximal inhibitory concentration (IC50) of each sample in the Genomics of Cancer Drug Sensitivity (GDSC) database. A fatty acid metabolism-related prognostic marker containing seven lncRNAs was constructed to predict OS in LUAD. In the training, test and combination groups, the patients were divided into high- and low-risk groups according to a formula. K–M analysis showed that patients in the high-risk group had poorer prognosis, with significant differences in the subgroup analysis. ROC analysis showed that the predictive ability of the model was more accurate. A clinical prediction nomogram combining lncRNA and clinical features was constructed to accurately predict OS and had high clinical application value. Therapeutics were screened based on the IC50 values of each sample in the GDSC database. We found that A.443654, AUY922, AZ628, A.770041, AZD.0530, AMG.706, and AG.014699 were more effective in high-risk patients. We constructed a 7-lncRNA prognostic model to predict the OS of patients with LUAD. In addition, the predictive nomogram model based on our established seven fatty acid metabolism-related lncRNA signatures provides better clinical value than that of the traditional TNM staging system in predicting the prognosis of patients with LUAD and presents new insights for personalized treatment.

A novel inflammation-related prognostic biomarker for predicting the disease-free survival of patients with colorectal cancer

BackgroundTo develop and evaluate the prognostic value of a comprehensive inflammatory biomarker for postoperative colorectal cancer (CRC) patients.MethodsA total of 646 CRC patients were recruited between August 2017 and December 2019 from Fujian Medical University Union Hospital, with follow-up data up to 2021. The least absolute shrinkage and selection operator method (LASSO) was used to select inflammation indicators in order to construct a comprehensive biomarker (named NSAP). The Cox regression model was utilized to analyze the association between the NSAP and the disease-free survival (DFS) of CRC. Predictive performance and clinical utility of prognostic models were evaluated by area under the curve (AUC) and decision curve analyses (DCAs).ResultsDuring a median follow-up of 23 months, 95 clinical outcomes were observed, with a 1-year survival rate is 89.47%. A comprehensive inflammatory biomarker (NSAP) was established based on four blood indicators (including neutrophil-to-lymphocyte ratio (NLR), neutrophil×monocyte-to-lymphocyte ratio (SIRI), albumin-to-globulin ratio (AGR), and platelet-to-lymphocytes ratio (PLR)). Patients with a lower NSAP had significantly associated with better DFS of CRC (HR=0.53, 95%CI 0.32–0.89). Moreover, compared to a previously established model, the traditional TNM staging system or/and tumor markers, the nomogram based on NSAP displayed more excellent predictive ability (0.752 vs 0.597, 0.711 and 0.735, P < 0.05). DCAs also demonstrated that the established nomogram had better utility for decision making.ConclusionsOur study suggests that NSAP may be a useful comprehensive prognostic biomarker for predicting the DFS of CRC patients. The nomogram based on NSAP can be considered a valuable tool to estimate the prognosis of patients with CRC.

Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Head and Neck Mucosal Melanoma.

BackgroundAccurate forecasting of the risk of death is crucial for people living with head and neck mucosal melanoma (HNMM). We aimed to establish and validate an effective prognostic nomogram for HNMM.MethodsPatients with HNMM who underwent surgery between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database for model construction. After eliminating invalid and missing clinical information, 288 patients were ultimately identified and randomly divided into a training cohort (199 cases) and a validation cohort (54 cases). Univariate and multivariate Cox proportional hazards regression analyses were performed in the training cohort to identify prognostic variables. Independent influencing factors were used to build the model. Through internal verification (training cohort) and external verification (validation cohort), the concordance indexes (C-indexes) and calibration curves were used to evaluate the predictive value of the nomogram.ResultsFor the training cohort, five independent risk predictors, namely age, location, T stage, N stage, and surgery, were selected, and nomograms with estimated 1- and 3-year overall survival (OS) and cancer-specific survival (CSS) were established. The C-index showed that the predictive performance of the nomogram was better than that of the TNM staging system and was internally verified (through the training queue: OS: 0.764 vs 0.683, CSS: 0.783 vs 0.705) and externally verified (through the verification queue: OS: 0.808 vs 0.644, CSS: 0.823 vs 0.648). The calibration curves also showed good agreement between the prediction based on the nomogram and the observed survival rate.ConclusionThe nomogram prediction model can more accurately predict the prognosis of HNMM patients than the traditional TNM staging system and may be beneficial for guiding clinical treatment.