Tumor Node Metastasis Staging System Research Articles

Predictive potential of preoperative Naples prognostic score-based nomogram model for the prognosis in surgical resected thoracic esophageal squamous cell carcinoma patients: A retrospective cohort study.

The present study aimed to establish an effective prognostic nomogram model based on the Naples prognostic score (NPS) for resectable thoracic esophageal squamous cell carcinoma (ESCC). A total of 277 patients with ESCC, who underwent standard curative esophagectomy and designated as study cohort, were retrospectively analyzed. The patients were divided into different groups, including NPS 0, NPS 1, NPS 2, and NPS 3 or 4 groups, for further analysis, and the results were validated in an external cohort of 122 ESCC patients, who underwent surgery at another cancer center. In our multivariate analysis of the study cohort showed that the tumor-node-metastasis (TNM) stage, systemic inflammation score, and NPS were the independent prognostic factors for the overall survival (OS) and progression-free survival (PFS) durations. In addition, the differential grade was also an independent prognostic factor for the OS in the patients with ESCC after surgery (all P < .05). The area under the curve of receiver operator characteristics for the PFS and OS prediction with systemic inflammation score and NPS were 0.735 (95% confidence interval [CI] 0.676-0.795, P < .001) and 0.835 (95% CI 0.786-0.884, P < .001), and 0.734 (95% CI 0.675-0.793, P < .001) and 0.851 (95% CI 0.805-0.896, P < .001), respectively. The above independent predictors for OS or PFS were all selected in the nomogram model. The concordance indices (C-indices) of the nomogram models for predicting OS and PFS were 0.718 (95% CI 0.681-0.755) and 0.669 (95% CI 0.633-0.705), respectively, which were higher than that of the 7th edition of American Joint Committee on Cancer TNM staging system [C-index 0.598 (95% CI 0.558-0.638) for OS and 0.586 (95% CI 0.546-0.626) for PFS]. The calibration curves for predicting the 5-year OS or PFS showed a good agreement between the prediction by nomogram and actual observation. In the external validation cohort, the nomogram discrimination for OS was better than that of the 7th edition of TNM staging systems [C-index: 0.697 (95% CI 0.639-0.755) vs 0.644 (95% CI 0.589-0.699)]. The calibration curves showed good consistency in predicting the 5-year survival between the actual observation and nomogram predictions. The decision curve also showed a higher potential of the clinical application of predicting the 5-years OS of the proposed nomogram model as compared to that of the 7th edition of TNM staging systems. The preoperative NPS-based nomogram model had a certain potential role for predicting the prognosis of ESCC patients.

Implications of Pretreatment Serum Carcinoembryonic Antigen Levels and Perineural Invasion with Staging, Prognosis, and Management in Stage I-III Colon Cancer after Surgery: A Retrospective Cohort Study in the SEER Database.

Pretreatment levels of serum carcinoembryonic antigen (CEA) and perineural invasion (PNI) are related to poor prognosis in colon cancer. We analyzed the CEA and PNI (defined as incorporation of carcinoembryonic antigen and perineural invasion (CP)-stage), which are included in the Tumor-Node-Metastasis (TNM) staging system of the American Joint Committee on Cancer (AJCC), and evaluated the survival prognosis of patients treated with surgery in I-III stage colon carcinoma. We employed a retrospective study for eligible colon carcinoma patients obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Kaplan-Meier curve and Multivariate Cox regression analysis were used to analyze different TNM-CP stages for the cancer-specific survival (CSS) probabilities in colon cancer. In our study, CEA levels and PNI were significant prognostic factors (p < 0.05), and the newly proposed CP-stage was an independent prognostic index in stage I-III colon carcinoma after surgery. Multivariate Cox regression analyses indicated that CP1-stage was related to a 63.9% increased risk of cancer-specific mortality (hazard ratio (HR): 1.639, 95% confidence interval (CI): 1.544-1.739, p < 0.001), compared with CP0-stage in colon cancer. In respective TNM stages, the CP0-stage had an advantage over the CP1-stage for CSS (p < 0.001). Moreover, CP1-stage patients with node-negative colon cancer were contacted with similar or worse survival in comparison to CP0-stage patients with node-positive. For postoperative patients with stage I-III colon cancer, our study indicated that the CP stage is a significant prognostic factor for CSS, which deserves more clinical attention. It's worth noting that including the CP stage in the AJCC TNM staging system of colon carcinoma is beneficial to the survival prediction and clinical treatment.

Circular RNA COL1A1 promotes Warburg effect and tumor growth in nasopharyngeal carcinoma

ObjectiveCircular RNAs (circRNAs), pivotal in the pathogenesis and progression of nasopharyngeal carcinoma (NPC), remain a significant point of investigation for potential therapeutic interventions. Our research was driven by the objective to decipher the roles and underlying mechanisms of hsa_circ_0044569 (circCOL1A1) in governing the malignant phenotypes and the Warburg effect in NPC.MethodsWe systematically collected samples from NPC tissues and normal nasopharyngeal epithelial counterparts. The expression levels of circCOL1A1, microRNA-370-5p (miR-370-5p), and prothymosin alpha (PTMA) were quantitatively determined using quantitative polymerase chain reaction (qPCR) and Western blotting. Transfections in NPC cell lines were conducted using small interfering RNAs (siRNAs) or vectors carrying the pcDNA 3.1 construct for overexpression studies. We interrogated the circCOL1A1/miR-370-5p/PTMA axis's role in cellular functions through a series of assays: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide for cell viability, colony formation for growth, Transwell assays for migration and invasion, and Western blotting for protein expression profiling. To elucidate the molecular interactions, we employed luciferase reporter assays and RNA immunoprecipitation techniques.ResultsOur investigations revealed that circCOL1A1 was a stable circRNA, highly expressed in both NPC tissues and derived cell lines. A correlation analysis with clinical pathological features demonstrated a significant association between circCOL1A1 expression, lymph node metastasis, and the tumor node metastasis staging system of NPC. Functionally, silencing circCOL1A1 led to substantial suppression of cell proliferation, migration, invasion, and metabolic alterations characteristic of the Warburg effect in NPC cells. At the molecular level, circCOL1A1 appeared to modulate PTMA expression by acting as a competitive endogenous RNA or 'sponge' for miR-370-5p, which in turn promoted the malignant characteristics of NPC cells.ConclusionTo conclude, our findings delineate that circCOL1A1 exerts its oncogenic influence in NPC through the modulation of the miR-370-5p/PTMA signaling axis.

Abstract 5065: Integrating blood based liquid biopsies with TNM stage improves survival prediction model for non-small cell lung cancer

Abstract Introduction:Stage is the most important prognostic factor for survival in non-small cell lung cancer (NSCLC). The current TNM (Tumor, Node, Metastasis) staging system for NSCLC uses physical exam, histology, imaging, and surgical findings to define the extent and spread of a patient’s cancer. Despite advances in molecular testing which can impact treatment and prognosis for NSCLC patients, the current system does not incorporate molecular data into staging. We proposed a novel predictive model for survival analysis which integrates molecular analysis of liquid biopsies (B) into the TNM staging system (1). In this study, we tested this new staging system and our predictive model shows TNM + B (TNMB) provides a better predictive model for survival compared to TNM alone. Methods:176 patients were identified at Atrium Health Wake Forest Baptist with Guardant 360 liquid biopsies sent at diagnosis for NSCLC. Clinical data was obtained through retrospective chart review. Molecular analysis was performed on 81 genes across all Guardant 360 liquid biopsies. Using Cox proportional hazards model, we identified significant gene mutations or genes with significant magnitude of variant allele frequency (VAF) that improved survival prediction. We then designed a novel staging system incorporating whether a patient had a positive liquid biopsy test (B1), defined by the presence of these impact gene variables, to TNM stage. Results: Three impact genes STK11 (p=0.0253), NFE2L2 (p = 0.0025), TP53 (p=0.0129) and one gene with a magnitude of VAF, ARID1A (p=0.0082) was identified which significantly improved the predictive model for survival. Patients with negative liquid biopsy test, B0, had improved survival compared to patients with a positive liquid biopsy test, B1 (p&amp;lt;0.005). In our cohort, TNM staging alone did not show a significant survival difference between stage II and III (p = 0.19). Whereas TNMB staging showed a significant survival difference between stage II, III and IV. Conclusions: While TNM stage remains a major prognostic factor for NSCLC patients, it fails to incorporate molecular data which has a critical impact on management and prognosis. We designed a novel method to analyze molecular data obtained by liquid biopsy to identify significant gene mutations and gene magnitude of VAF that impacts survival prediction. In our cohort, we showed 4 impact genes which we used to create a new TNMB staging system which led to improved survival prediction compared to TNM alone. Acknowledgments: Research partly supported by NIH T32 Grant (T32CA247819) and Cancer Center Support Grant (P30CA012197) Reference: (1) Yang M, Forbes ME, Bitting RL, O'Neill SS, Chou PC, Topaloglu U, Miller LD, Hawkins GA, Grant SC, DeYoung BR, Petty WJ, Chen K, Pasche BC, Zhang W. Incorporating Blood-based Liquid Biopsy Information into Cancer Staging: Time for a TNMB System? Ann Oncol. 2018 Feb 1;29(2):311-323. Citation Format: Lauren Schmalz, Cetin Urtis, Liang Liu, Elizabeth Forbes, Fang-Chi Hsu, Nury Steuerwald, Alberto de Hoyos, Thomas Lycan, Jimmy Ruiz, William Petty, Wei Zhang. Integrating blood based liquid biopsies with TNM stage improves survival prediction model for non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5065.

Validation of the 9th edition of the TNM staging system for non-small cell lung cancer with lobectomy in stage IA-IIIA.

The 9th edition of tumour-node-metastasis (TNM) staging for lung cancer was announced by Prof Hisao Asamura at the 2023 World Conference on Lung Cancer in Singapore. The purpose of this study was to externally validate and compare the latest staging of lung cancer. We collected 19193 patients with stage IA-IIIA non-small cell lung cancer (NSCLC) who underwent lobectomy from the Surveillance, Epidemiology and End Results database. Survival analysis by TNM stages was compared using the Kaplan-Meier method and further analysed using univariable and multivariable Cox regression analyses. Receiver operating characteristic curves were used to assess model accuracy, Akaike information criterion, Bayesian information criterion and consistency index were used to compare the prognostic, predictive ability between the current 8th and 9th edition TNM classification. The 9th edition of the TNM staging system can better distinguish between IB and IIA patients on the survival curve (P < 0.0001). In both univariable and multivariable regression analysis, the 9th edition of the TNM staging system can differentiate any 2 adjacent staging patients more evenly than the 8th edition. The 9th and the 8th edition TNM staging have similar predictive power and accuracy for the overall survival of patients with NSCLC [TNM 9th vs 8th, area under the curve: 62.4 vs 62.3; Akaike information criterion: 166182.1 vs 166131.6; Bayesian information criterion: 166324.3 vs 166273.8 and consistency index: 0.650 (0.003) vs 0.651(0.003)]. Our external validation demonstrates that the 9th edition of TNM staging for NSCLC is reasonable and valid. The 9th edition of TNM staging for NSCLC has near-identical prognostic accuracy to the 8th edition.

Development and validation of a nomogram for predicting the impact of tumor size on cancer-specific survival of locally advanced renal cell carcinoma: a SEER-based study.

This study was aimed to integrate tumor size with other prognostic factors into a prognostic nomogram to predict cancer-specific survival (CSS) in locally advanced (≥pT3a Nany M0) renal cell carcinoma (RCC) patients. Based on the Surveillance, Epidemiology, and End Results (SEER) database, 10,800 patients diagnosed with locally advanced RCC were collected. They were randomly divided into a training cohort (n = 7,056) and a validation cohort (n = 3,024). X-tile program was used to identify the optimal cut-off value of tumor size and age. The cut-off of age at diagnosis was 65 years old and 75 years old. The cut-off of tumor size was 54 mm and 119 mm. Univariate and multivariate Cox regression analyses were performed in the training cohort to identify independent prognostic factors for construction of nomogram. Then, the nomogram was used to predict the 1-, 3- and 5-year CSS. The performance of nomogram was evaluated by using concordance index (C-index), area under the Subject operating curve (AUC) and decision curve analysis (DCA). Moreover, the nomogram and tumor node metastasis (TNM) staging system (AJCC 8th edition) were compared. 10 variables were screened to develop the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) indicated satisfactory ability of the nomogram. Compared with the AJCC 8th edition of TNM stage, DCA showed that the nomogram had improved performance. We developed and validated a nomogram for predicting the CSS of patients with locally advanced RCC, which was more precise than the AJCC 8th edition of TNM staging system.

Long-term outcomes of intensity-modulated radiotherapy in limited-stage small-cell lung cancer classified according to AJCC 8th tumor node metastasis staging system.

The objective of this study was to assess treatment outcomes of intensity-modulated radiotherapy with concomitant chemotherapy and to identify prognostic factors on survival in patients with limited-stage small-cell lung cancer. A retrospective analysis was conducted on a cohort of seventy-two patients who received curative treatment between December 2011 and January 2023. Several clinical and biochemical parameters were examined as potential prognostic factors. The median age was 63 years, and 79% of them were males. Concomitant chemotherapy was administered in 83% of patients. Prophylactic cranial irradiation was applied in 61% of the cohort. Two and five-year overall survival (OS), disease-free survival (DFS), and local relapse-free survival (LRFS) rates were 50% and 25%, 38% and 24%, and 44% and 25%, respectively. Univariate analysis revealed that older age, comorbid lung disease, advanced tumor-node-metastasis (TNM) stage, radiotherapy (RT) alone, and the absence of prophylactic cranial irradiation (PCI) were adverse factors affecting OS. The advanced TNM stage emerged as a significant prognostic factor for LRFS and DFS, with a notable trend toward affecting OS. The TNM staging system is of significance in cases classified as limited-stage small-cell lung cancer due to its prognostic implications. Our results suggest that patients with more advanced TNM stage exhibit less favorable treatment outcomes, which may require individual tailoring of new systemic therapies.

A prognostic nomogram integrating carcinoembryonic antigen levels for predicting overall survival in elderly patients with stage II-III colorectal cancer.

With the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II-III CRC and construct a nomogram to predict patient survival. A total of 3,016 elderly CRC patients with stage II-III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups. The 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001). We constructed a prediction nomogram for stage II-III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

Nomograms predict survival in elderly women with triple-negative breast cancer: A SEER population-based study.

The effective treatment of breast cancer in elderly patients remains a major challenge. To construct a nomogram affecting the overall survival of triple-negative breast cancer (TNBC) and establish a survival risk prediction model. A total of 5317 TPBC patients with negative expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) who were diagnosed and received systematic treatment from 2010 to 2015 were collected from the American Cancer Surveillance, Epidemiology and End Results (SEER) database. They were randomly divided into training set (n= 3721) and validation set (n= 1596). Univariate and multivariate Cox regression analysis were used to identify prognostic features, and a nomogram was established to predict the probability of 1-year, 3-year and 5-year OS and BCSS. We used consistency index (C-index), calibration curve, area under the curve (AUC) and decision curve analysis (DCA) to evaluate the predictive performance and clinical utility of the nomogram. The C-indices of the nomograms for OS and BCSS in the training cohort were 0.797 and 0.825, respectively, whereas those in the validation cohort were 0.795 and 0.818, respectively. The receiver operating characteristic (ROC) curves had higher sensitivity at all specificity values as compared with the Tumor Node Metastasis (TNM) system. The calibration plot revealed a satisfactory relationship between survival rates and predicted outcomes in both the training and validation cohorts. DCA demonstrated that the nomogram had clinical utility when compared with the TNM staging system. This study provides information on population-based clinical characteristics and prognostic factors for patients with triple-negative breast cancer, and constructs a reliable and accurate prognostic nomogram.

The Relationship between Cancer Stage, Selected Immunological Parameters, Epstein-Barr Virus Infection, and Total Serum Content of Iron, Zinc, and Copper in Patients with Laryngeal Cancer.

(1) Background: the purpose of the study was to assess the relationship between cancer stage, selected immunological parameters, Epstein-Barr virus (EBV) infection, and total serum content of iron, zinc, and copper in patients with laryngeal cancer (LC). (2) Methods: serum Fe, Zn, and Cu were measured in 40 LC patients and 20 controls. Immunophenotyping of peripheral blood lymphocytes was performed by flow cytometry using fluorescent antibodies against CD3, CD4, CD8, CD19, CD25, CD69, and PD-1. Tumor and lymph node lymphocytes were analyzed by flow cytometry. EBV DNA was quantified by real-time PCR, targeting the EBNA-1 gene. Associations between serum elements, immune markers, and cancer grade/stage were evaluated using ANOVA and appropriate nonparametric tests. (3) Results: levels of Fe, Cu, and Zn were lower, while Cu/Zn was statistically higher, in patients with LC than in the control group. Correlation analysis showed a statistically significant association between the levels of these elements and parameters of the TNM (Tumor, Node, Metastasis) staging system, immunophenotype, and the amount of EBV genetic material in patients with LC who survived for more than 5 years. (4) Conclusion: the results suggest that the total serum levels of the determined micronutrients may significantly affect the immunopathogenesis and progression of LC.

Prognostic value of lymph node-to-primary tumor ratio of PET standardized uptake value for nasopharyngeal carcinoma: a recursive partitioning risk stratification analysis.

Induction chemotherapy (IC) combined with concurrent chemoradiotherapy has become the standard treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC). Data on the prognostic value of the lymph node-to-primary tumor ratio (NTR) of positron emission tomography (PET) standardized uptake value (SUV) for patients treated with IC were limited. To evaluate the prognostic value of the SUV NTR for patients with LA-NPC treated with IC. In all, 467 patients with pretreatment 18F-fluorodeoxyglucose PET/computed tomography (CT) scans between September 2017 and November 2020 were retrospectively reviewed. The receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value of SUV NTR. Kaplan-Meier method was used to evaluate survival rates. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The optimal cutoff value of SUV NTR was 0.74. Multivariate analyses showed that SUV NTR and overall stage were independent predictors for distant metastasis-free survival (DMFS) and regional recurrent-free survival (RRFS). Therefore, an RPA model based on the endpoint of DMFS was generated and categorized the patients into three distinct risk groups: RPA I (low risk: SUV NTR < 0.74 and stage III), RPA II (medium risk: SUV NTR < 0.74 and stage IVa, or SUV NTR ⩾ 0.74 and stage III), and RPA III (high risk: SUV NTR ⩾ 0.74 and stage IVa), with a 3-year DMFS of 98.9%, 93.4%, and 84.2%, respectively. ROC analysis showed that the RPA model had superior predictive efficacy than the SUV NTR or overall stage alone. SUV NTR was an independent prognosticator for distant metastasis and regional recurrence in locoregionally advanced NPC. The RPA risk stratification model based on SUV NTR provides improved DMFS and RRFS prediction over the eighth edition of the TNM (Tumor Node Metastasis) staging system.

The clinical value of a nomogram constructed from CEA, CA199, PT, FIB, tumor differentiation and TNM stage in colorectal cancer.

The accurate Tumor-Node-Metastasis (TNM) staging of colorectal cancer (CRC) is of great guiding significance for the judgment of tumor progression and prognosis, and the formulation of treatment strategies. The aim of this study was to construct a recurrence risk scoring (RRS) system and prognostic prediction model to improve the accuracy of staging, prognosis prediction, and clinical decision making in resectable CRC. CRC patients who underwent radical resection were retrospectively enrolled into study. Multivariable Cox regression model was applied to screen for independent prognostic factors. The RRS system is composed of independent prognostic factors which was awarded 1point each. A prognostic model composed of RRS and TNM staging system (RRS-TNM model) was applied to predict postoperative recurrence. TNM stage, tumor differentiation, preoperative elevated Carcinoembryonic Antigen, Carbohydrate Antigen 199, Prothrombin Time and Fibrinogen were the independent prognostic biomarkers. 173 of 540 patients had recurrence. The 5-year cumulative recurrence rate (5-y CRR) and disease-free survival (DFS) of postoperative p-TNM stage I, II, and III were 12.7% and 104.8 months, 26.5% and 89.3 months, and 55.5% and 57.3 months, respectively. The 5-y CRR and DFS of preoperative Low-risk (RRS 0-1score), Middle-risk (RRS 2-3scores), and High-risk (RRS 4-5scores) groups were 13.9% and 101.1 months, 40.9% and 75.5 months, and 70.2% and 41.1 months. The AUC (area under ROC curve) of RRS system was not inferior to that of TNM staging system (0.713 vs. 0.666; P= 0.093). The AUC (0.770) and C-index value (0.721) of RRS-TNM model were significantly better than both RRS and TNM staging system (P< 0.001). The RRS system accurately identifies CRC patients with high-risk recurrence preoperatively. Constructing a nomogram using the RRS system and TNM staging significantly improves the accuracy of staging and prognosis prediction, which is of great clinical significance for individualized clinical treatment and follow-up of CRC.

Unsolved Issues in Thymic Epithelial Tumour Stage Classification: The Role of Tumour Dimension.

According to the different classifications now in use, thymic tumours are staged by the extent of local invasiveness, and tumour size is not included as a major determinant for the T category. The aim of this double-site retrospective study is to analyse the correlation between tumour dimension and overall survival (OS) in patients who underwent surgical treatment. From January 2000 to December 2020, patients with thymic epithelial tumours who underwent surgical resection were included in this study. Data from a total of 332 patients were analysed. Five- and ten-year overall survival (5-10 YOS) was 89.26% and 87.08%, respectively, while five- and ten-year disease-free survival (DFS) was 88.12% and 84.2%, respectively. Univariate analysis showed a significant correlation between male sex (p-value 0.02), older age (p-value < 0.01), absence of myasthenia gravis (p-value < 0.01), increase in pTNM (pathological Tumor Node Metastasis) (p-value 0.03) and increase in the number of infiltrated organs (p-value 0.02) with an increase in tumour dimension. Tumour dimension alone was not effective in the prediction of DFS and OS, both when considered as a continuous variable and when considered with a cut-off of 3 and 5 cm. However, with multivariate analysis, it was effective in predicting OS in the aforementioned conditions (p-value < 0.01). Moreover, multivariate analysis was also used in the thymoma and Masaoka I subgroups. In our experience, the role of tumour dimension as a descriptor of the T parameter of the TNM (Tumor Node Metastasis) staging system seemed to be useful in improving this system.

Co-expression pattern of SLC transporter genes associated with the immune landscape and clinical outcomes in gastric cancer.

Solute carrier (SLC) transporters play a dual role in the occurrence and progression of tumours by acting as both suppressors and promoters. However, the overall impact of SLC transcriptome signatures on the tumour microenvironment, biological behaviour and clinical stratification of gastric cancer has not been thoroughly investigated. Therefore, we comprehensively analysed the expression profiles of the SLC transporter family members to identify novel molecular subtypes in gastric cancer. We identified two distinct SLC subtypes, SLC-S1 and SLC-S2, using non-negative matrix factorization. These subtypes were markedly linked with the tumour microenvironment landscape, biological pathway activation and distinct clinical features of gastric cancer. Furthermore, a new scoring model, the SLC score, was developed to quantify the SLC subtypes. High SLC scores indicated a pattern of 'SLC-S2', characterized by stromal infiltration and activation, poor prognosis and insensitivity to chemotherapy and immunotherapy, but high sensitivity to imatinib. The SLC score could serve as a supplement to the Tumour Node Metastasis (TNM) staging system to guide personalized treatment strategies and predict prognosis for patients with gastric cancer.

Lung Cancer Staging: Imaging and Potential Pitfalls.

Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.

Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study

Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p<0.05 for all). The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).

Development of a Survival Nomogram for Esophageal Squamous Cell Carcinoma Patients: a Population-Based Analysis.

In this study, we developed a prognostic nomogram for esophageal squamous cell carcinoma (ESCC) patients. Patients diagnosed with ESCC from the Surveillance, Epidemiology, and End Results (SEER) database (1975-2017) and a local hospital were enrolled in this retrospective cohort study. Prognoses were analyzed using the R language software, and the predictive power of the model was then assessed by the Harrell concordance index (C-index) and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. In total, 2915 ESCC patients from SEER database were divided into training and validation cohorts. Multivariate analysis revealed that sex, marital status, tumor-node-metastasis (TNM) stage, surgery, chemotherapy, and radiation all showed a significant association with overall survival (OS) and cancer-specific survival (CSS) (also with tumor grade). These characteristics were employed to build a nomogram. The C-index of the nomogram for OS and CSS prediction was 0.743 and 0.748 for the training cohort, which were superior to the predictive power of the 7th TNM staging system. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.805 and 0.812, respectively, and the AUCs for CSS were 0.811 and 0.821, respectively. ROC and calibration curves of data from the SEER internal validation set and of data from our hospital showed that this model had good accuracy for predicting the prognosis of ESCC patient. The nomogram developed in this study provides a useful tool for accurately estimating OS and CSS for ESCC patients.

Effect of extracapsular lymph node involvement on the prognosis of patients with esophageal squamous cell carcinoma.

According to the eighth edition of the tumor node metastasis (TNM) staging system for esophageal cancer, it is recommended that extracapsular lymph node involvement (EC-LNI) is included as a registered independent variable for the disease. However, its role in the prognosis has not been clearly explained. To study the value of EC-LNI in the prognosis of esophageal cancer and attempt to explore its molecular mechanism via an enrichment analysis. A retrospective analysis was performed on 544 patients with esophageal squamous cell carcinoma (ESCC) who underwent radical surgery in the department of thoracic surgery of our hospital, focusing on the relationship between EC-LNI and clinicopathological characteristics and its effect on prognosis. Additionally, the mechanism of EC-LNI in esophageal cancer was explored. Among the 271 patients with lymph node metastasis, 125 were EC-LNI (+). The degrees of tumor differentiation, location, TNM stage, vascular tumor thrombus, and nerve invasion were related to the occurrence of EC-LNI. The stage of TNM was considered an independent risk factor for the development of EC-LNI. A significant difference was found in terms of overall survival (OS) and disease-free survival (DFS) between the EC-LNI (+) and EC-LNI (-) groups. A univariate analysis showed that the degrees of tumor differentiation, T stage, N stage, TNM stage, EC-LNI, EC-LNI number, and EC-LNI distance were significantly correlated with prognosis. A multivariate survival analysis showed that tumor differentiation, TNM stage, and EC-LNI were independent prognostic factors for OS, while TNM stage and EC-LNI were independent prognostic factors for DFS. The enrichment analysis identified the molecular targets and signaling pathways that can regulate cell proliferation, differentiation, and apoptosis. Extracapsular LNI has a high prognostic value in patients with esophageal cancer and is closely related to the stage of tumors. Our preliminary molecular mechanism research indicated that the molecular targets of EC-LNI are expected to become a new direction for the treatment of esophageal cancer.

Development and validation of web-based dynamic nomograms predictive of disease-free and overall survival in patients who underwent pneumonectomy for primary lung cancer.

The tumour-node-metastasis (TNM) staging system is insufficient to precisely distinguish the long-term survival of patients who underwent pneumonectomy for primary lung cancer. Therefore, this study sought to identify determinants of disease-free (DFS) and overall survival (OS) for incorporation into web-based dynamic nomograms. The clinicopathological variables, surgical methods and follow-up information of 1,261 consecutive patients who underwent pneumonectomy for primary lung cancer between January 2008 and December 2018 at Sun Yat-sen University Cancer Center were collected. Nomograms for predicting DFS and OS were built based on the significantly independent predictors identified in the training cohort (n = 1,009) and then were tested on the validation cohort (n = 252). The concordance index (C-index) and time-independent area under the receiver-operator characteristic curve (AUC) assessed the nomogram's discrimination accuracy. Decision curve analysis (DCA) was applied to evaluate the clinical utility. During a median follow-up time of 40.5 months, disease recurrence and death were observed in 446 (35.4%) and 665 (52.7%) patients in the whole cohort, respectively. In the training cohort, a higher C-reactive protein to albumin ratio, intrapericardial pulmonary artery ligation, lymph node metastasis, and adjuvant therapy were significantly correlated with a higher risk for disease recurrence; similarly, the independent predictors for worse OS were intrapericardial pulmonary artery and vein ligation, higher T stage, lymph node metastasis, and no adjuvant therapy. In the validation cohort, the integrated DFS and OS nomograms showed well-fitted calibration curves and yielded good discrimination powers with C-index of 0.667 (95% confidence intervals CIs [0.610-0.724]) and 0.697 (95% CIs [0.649-0.745]), respectively. Moreover, the AUCs for 1-year, 3-year, and 5-year DFS were 0.655, 0.726, and 0.735, respectively, and those for 3-year, 5-year, and 10-year OS were 0.741, 0.765, and 0.709, respectively. DCA demonstrated that our nomograms could bring more net benefit than the TNM staging system. Although pneumonectomy for primary lung cancer has brought encouraging long-term outcomes, the constructed prediction models could assist in precisely identifying patients at high risk and developing personalized treatment strategies to further improve survival.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

BackgroundOn average, 5–10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients. MethodsThe nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China. ResultsMultivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682–0.694) in the training cohort and 0.875 (95% CI, 0.816–0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels. ConclusionsThe nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.