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PubMed2026

Crucial role of telomere maintenance-related genes in survival prediction and subtype identification in colorectal cancer.

BACKGROUND: Telomere maintenance-related genes (TMRGs) are implicated in Colorectal cancer (CRC) development, but their prognostic value and clinical relevance remain insufficiently explored. This study aims to develop a TMRG-based prognostic model and elucidate its clinical utility in CRC management. METHODS: The Cancer Genome Atlas database was utilized to download RNA-seq data from 638 CRC and 51 control samples. Differential expressed genes were screened and intersected with 2086 TMRGs, resulting in the identification of 976 TMRGs. Through univariate and multivariate Cox regression analysis, a prognostic model comprising three telomere maintenance-related biomarkers (PDE1B, TFAP2B, and HSPA1A) was developed and validated using an external dataset. By integrating the model risk score with clinical features, a nomogram was constructed to predict the survival outcomes of CRC patients. Additionally, an in-depth investigation of the immuno-infiltration, functional variation and drug sensitivity analysis were performed in two risk subgroups defined by the prognostic model. Finally, the functional significance of PDE1B in CRC cell lines was investigated through MTT assays, cell colony formation assays, transwell assays and flow cytometry. RESULTS: A total of 976 DE-TMRGs were enriched in telomere/DNA replication pathways. A three-gene signature (PDE1B, TFAP2B, and HSPA1A) stratified patients into high- and low-risk groups with divergent survival (AUC >0.60, validated externally). High-risk patients had advanced N/M stages, elevated M0/M2 macrophages, reduced CD4+ memory T cells, and upregulated immune checkpoints. Nomogram integrating risk score, age, and N/M stage accurately predicted 1-/3-/5-year survival. Low-risk patients showed greater 5-fluorouracil sensitivity. PDE1B expression was significantly reduced in CRC tissues and correlated with advanced stages. Functional assays confirmed PDE1B overexpression suppressed proliferation, migration, invasion, and induced apoptosis in CRC cells. CONCLUSION: This study identifies a moderately predictive telomere maintenance-related gene signature as an independent prognostic predictor in CRC. The risk stratification model effectively discriminates patients with distinct survival patterns, tumor microenvironments, and therapeutic responses, while the integrated nomogram offers additional reference information for survival analysis, albeit with only moderate predictive accuracy. These findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients, PDE1B may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.

PDE1B↗

PubMed2000

Prognosis of follicular lymphoma: a predictive model based on a retrospective analysis of 987 cases. Intergruppo Italiano Linfomi.

Patients (n-987) with a histologically confirmed diagnosis of follicular lymphoma were studied with the aim of developing a prognostic model specifically devised for this type of lymphoma. We collected information on age, sex, Ann Arbor stage, number of extranodal disease sites, bone marrow (BM) involvement, bulky disease, B symptom criteria (fever, night sweats, and weight loss), performance status (PS), serum lactate dehydrogenase (LDH) level, serum albumin level, hemoglobin level, and erythrocyte sedimentation rate (ESR). In the training sample of 429 patients with complete data, multivariate analysis showed that age, sex, number of extranodal sites, B symptoms, serum LDH level, and ESR were factors predictive for overall survival. Using these 6 variables, a prognostic model was devised to identify 3 groups at different risk. The 5- and 10-year survival rate was 90% and 65% for patients at low risk, respectively; 75% and 54% for patients at intermediate risk; and 38% and 11% for those at high risk (log-rank test, 86.62; P <. 0001). The model was also predictive (P =.0001) in the validation sample of 265 patients with complete data only for the 6 variables used in the development of the model and even in the group of 210 patients from the validation sample uniformly treated with doxorubicin-containing regimens (P =.0001). The prognostic model appears to be very useful in identifying patients with follicular lymphoma at low, intermediate, or high risk.

Adult↗

PubMedDate not supplied

Integrating classification trees with local logistic regression in Intensive Care prognosis.

Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.

Artificial Intelligence↗

PubMed1985

Regression models for prognostic prediction: advantages, problems, and suggested solutions.

Multiple regression models have wide applicability in predicting the outcome of patients with a variety of diseases. However, many researchers are using such models without validating the necessary assumptions. All too frequently, researchers also "overfit" the data by developing models using too many predictor variables and insufficient sample sizes. Models developed in this way are unlikely to stand the test of validation on a separate patient sample. Without attempting such a validation, the researcher remains unaware that overfitting has occurred. When the ratio of the number of patients suffering endpoints to the number of potential predictors is small (say less than 10), data reduction methods are available that can greatly improve the performance of regression models. Regression models can make more accurate predictions than other methods such as stratification and recursive partitioning, when model assumptions are thoroughly examined; steps are taken (ie, choosing another model or transforming the data) when assumptions are violated; and the method of model formulation does not result in overfitting the data.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed2006

Severe head injury and the risk of early death.

BACKGROUND: Severe head injury (SHI) is one of the most important health, social and economic problems in industrialised countries. Unfortunately, none of the neuroprotection trials for traumatic brain injury have shown efficacy. One of the reasons for this failure could be the inclusion of patients with high probability of early death. A population-based, retrospective study was conducted to develop a prognostic model for identification of these patients. METHODS: Between January 1987 and August 1999, a total of 895 patients (> or = 15 years of age) with non-missile SHI were studied, in whom a computed tomography scan was carried out within the first 6 h of injury. The association between early death (first 48 h after injury) and independent prognostic factors was determined by logistic regression analysis. A scoring system was also constructed. RESULTS: The early-death rate was 20%. Independent predictors of early mortality after SHI were non-evacuated mass (odds ratio (OR) 65, 95% confidence interval (CI) 11 to 379), diffuse injury IV (OR 25, 95% CI 5 to 112), diffuse injury III (OR 8, 95% CI 3 to 22), flaccidity (OR 7, 95% CI 3 to 15), non-reactive bilaterally mydriasis (OR 6, 95% CI 3 to 12), evacuated mass (OR 4, 95% CI 1 to 11), age > or = 65 years (OR 4, 95% CI 1 to 9), decerebration (OR 3, 95% CI 2 to 7) and shock (OR 3, 95% CI 2 to 6). The prognostic model correctly identified 93% of the patients. CONCLUSIONS: This prognostic model is based on simple clinical and radiological data readily available during the first 6 h after injury and is useful for identification of early death after SHI.

Adolescent↗

PubMed2003

Diabetic neuropathic foot ulcers: predicting which ones will not heal.

PURPOSE: Neuropathic foot ulcers are a serious complication of diabetes. The purpose of this study was to develop a clinically useful prognostic model for identifying ulcers that are not likely to heal. METHODS: Using an administrative and medical records database from a large wound care system, we designed a cohort study of patients with diabetic neuropathic foot ulcer. Clinicians followed a standard algorithm of good wound care, wound débridement, and wound offloading. The outcome was a healed wound by week 20 of care. For patients with more than one wound, we investigate the wound labeled as the primary wound. We evaluated several prognostic models of varying mathematical complexity. RESULTS: We studied 27630 patients with a diabetic neuropathic foot ulcer, of whom 12983 (47%) healed by week 20 of care. The simplest model counted 1 point each if the wound was older than 2 months, larger than 2 cm(2), or had a grade > or =3 (on a 6-point scale). The likelihood that a wound would not heal was 0.35 for a count of 0, 0.47 for a count of 1, 0.66 for a count of 2, and 0.81 for a count of 3 in the validation data set. CONCLUSION: A simple prognostic model can be developed using prognostic factors that are already part of the wound care examination. Applications of this model could include determining who will do well with standard care and as an aid in the design of clinical trials.

Aged↗

PubMed2006

Mortality after acute renal failure: models for prognostic stratification and risk adjustment.

To adjust adequately for comorbidity and severity of illness in quality improvement efforts and prospective clinical trials, predictors of death after acute renal failure (ARF) must be accurately identified. Most epidemiological studies of ARF in the critically ill have been based at single centers, or have examined exposures at single time points using discrete outcomes (e.g., in-hospital mortality). We analyzed data from the Program to Improve Care in Acute Renal Disease (PICARD), a multi-center observational study of ARF. We determined correlates of mortality in 618 patients with ARF in intensive care units using three distinct analytic approaches. The predictive power of models using information obtained on the day of ARF diagnosis was extremely low. At the time of consultation, advanced age, oliguria, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality. Upon initiation of dialysis for ARF, advanced age, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality; higher blood urea nitrogen and lower serum creatinine were also associated with mortality in logistic regression models. Models incorporating time-varying covariates enhanced predictive power by reducing misclassification and incorporating day-to-day changes in extra-renal organ system failure and the provision of dialysis during the course of ARF. Using data from the PICARD multi-center cohort study of ARF in critically ill patients, we developed several predictive models for prognostic stratification and risk-adjustment. By incorporating exposures over time, the discriminatory power of predictive models in ARF can be significantly improved.

APACHE↗

PubMed1997

A prognostic computer model to predict individual outcome in interventional cardiology. The INTERVENT Project.

It is not yet possible to predict an individual's outcome from percutaneous transluminal coronary angioplasty or alternative/adjunctive coronary interventional techniques. The purpose of the INTERVENT project is to redefine complications associated with coronary interventions, to set up a prognostic computer model to predict individual outcome and to compare the results to those of conventional statistical techniques. 2500 data items were analysed in 455 consecutive patients (mean age: 61.1 +/- 8.3 years; range 33-84 years; 80.4% male, 16.7% unstable angina, 5.1%/10.1% acute/subacute myocardial infarction) undergoing coronary interventions at three university centres. In-lab/out-of-lab complication rates were 0.4%/0.9% (death), 1.8%/0.2% (abrupt vessel closure with myocardial infarction) and 5.5%/4.0% (haemodynamic complications). Computer algorithms derived by applying techniques from artificial intelligence were able (1) to reduce the set of possible relevant risk factors from 2500 to about 40, (2) to predict individual risk with an accuracy of > 95% and (3) to explain the structural relationship between outcome and risk factors. Patient data from two centres were used to construct and test the algorithm. Data from a third centre were used to evaluate the algorithm. The most important predictors-were acute myocardial infarction, heart failure (NYHA class > II), unstable angina, complex lesions, high low density lipoprotein cholesterol and duration of coronary heart disease. Neither age nor gender impaired the percutaneous transluminal coronary angioplasty results in acute ischaemic syndromes; however, for stable angina, procedural risk increased with age. There was little risk from primary percutaneous transluminal coronary angioplasty in acute myocardial infarction in patients with NYHA heart failure classes I-II; however, the risk was high for patients in NYHA classes > II, either with or without additional thrombolysis. Alternative/adjunctive intervention techniques were no predictors for in-lab-, but were predictors for post-procedural complications.

Adult↗

PubMed1995

Primary sclerosing cholangitis: liver transplantation or biliary surgery.

BACKGROUND: Little information is available on the indications for, and the efficacy and timing of, liver transplantation in patients with primary sclerosing cholangitis (PSC). This issue is particularly relevant because prolonged survival has been reported in patients who do not undergo transplantation. METHODS: Long-term results of therapeutic interventions including liver transplantation was assessed in a representative series of 51 patients. Patient survival was compared with that expected from prognostic models. RESULTS: Actuarial symptom-free survival rate in patients treated by nontransplantation biliary surgery (n = 23) was 35% at 10 years. Actuarial survival rate from onset of PSC (56% at 10 years) was identical to that expected from the prognostic model. Actuarial patient (n = 28) survival rate 5 years after transplantation was greater than that expected from prognostic models (89% versus 31%; p < 0.001). Previous abdominal surgery was associated with an increased in-hospital mortality rate (p < 0.05). Cumulative actuarial incidence of cancer 5 and 10 years after the onset of PSC was 13% and 31%, respectively. CONCLUSIONS: Liver transplantation improves the prognosis of patients with PSC. Failure to identify patients who will benefit from nontransplantation therapeutic interventions or in whom a cancer will develop, and the risk associated with previous abdominal surgery, suggest that liver transplantation should be indicated early after onset of symptoms.

Adult↗

PubMed2005

New prognostic scoring model for liver transplantation in patients with non-acetaminophen-related fulminant hepatic failure.

BACKGROUND: Many patients with fulminant hepatic failure die before receiving liver transplantation because of the difficulty of pinpointing the suitable timing for liver transplantation. The revised King's College criteria are useful for patients with acetaminophen-related fulminant hepatic failure; however, in those with non-acetaminophen-related fulminant hepatic failure, a new prognostic system that can accurately identify the suitable timing for liver transplantation is required. METHODS: Using the first sample consisted of eighty patients with fulminant hepatic failure, we examined 2-week poor prognostic parameters at the time of diagnosis of fulminant hepatic failure (day 1) and on days 4, 8, and 15, respectively, and a 2-week prognostic scoring model was constructed. To confirm the accuracy of this model, validation was performed in the second sample consisting of 26 patients. RESULTS: Cause of fulminant hepatic failure (hepatitis B virus or indeterminate), hepatic coma grade (III or IV), systemic inflammatory response syndrome (yes) and ratio of total to direct bilirubin (> 2.0) were associated with 2-week outcomes during days 1-15. Each of these four parameters was valued at +1. The 2-week survival rate in patients scoring <3 was > or = 80% in contrast to less than 30% in patients scoring > or = 3. When this scoring model was applied to the second sample, the sensitivity, specificity, and positive and negative predictive values were 87.5%, 90.0%, 93.3%, and 81.8%, respectively. CONCLUSIONS: This scoring model may be useful for predicting 2-week outcomes and determining the suitable timing for liver transplantation in patients with non-acetaminophen-related fulminant hepatic failure.

Acetaminophen↗

PubMed2004

Using protein expressions to predict survival in clear cell renal carcinoma.

PURPOSE: An accurate system for predicting survival for patients with solid tumors will allow for better patient selection for both established and novel therapies. We propose a staging system for clear cell variants of renal cell carcinoma (RCC) that includes molecular predictors and standard clinical predictors such as tumor-node-metastasis (TNM) stage, histological grade, and performance status (PS). EXPERIMENTAL DESIGN: A custom tissue array was constructed using clear cell RCC from 318 patients, representing all stages of localized and metastatic RCC, and immunohistochemically stained for molecular markers Ki67, p53, gelsolin, CA9, CA12, PTEN, EpCAM, and vimentin. We present a strategy for evaluating individual candidate markers for prognostic information and integrating informative markers into a multivariate prognostic system. RESULTS: The overall median follow-up and the median follow-up for surviving patients were 28 and 55 months, respectively. A prognostic model based primarily on molecular markers included metastasis status, p53, CA9, gelsolin, and vimentin as predictors and had high discriminatory power: its statistically validated concordance index (C-index) was found to be 0.75. A prognostic model based on a combination of clinical and molecular predictors included metastasis status, T stage, Eastern Cooperative Oncology Group PS, p53, CA9, and vimentin as predictors and had a C-index of 0.79, which was significantly higher (P < 0.05) than that of prognostic models based on grade alone (C = 0.65), TNM stage alone (C = 0.73), or the University of California Los Angeles integrated staging system (C = 0.76). CONCLUSIONS: Protein expressions obtained using widely available technology can complement standard clinical predictors such as TNM stage, histological grade, and PS.

Adenocarcinoma, Clear Cell↗

PubMed2006

Mantle cell lymphoma: prognostic capacity of the Follicular Lymphoma International Prognostic Index.

The International Prognostic Index (IPI) is the most commonly used prognostic model for mantle cell lymphoma (MCL). However, the prognostic value of the IPI is limited. The recently published Follicular Lymphoma International Prognostic Index (FLIPI) is built on variables, which are pertinent to MCL. This study was conducted to evaluate the prognostic value of FLIPI in a population-based series of 93 patients with MCL diagnosed in a 7-year period. End points of the study were response to therapy, overall survival, and disease-free survival (DFS) according to the IPI and FLIPI. Applied to the whole series, the FLIPI identified three risk groups with markedly different outcome with 5-year overall survival rates of 65%, 42%, and 8% respectively. Notably, the high-risk group comprised 53% of patients. In contrast, the IPI only allocated 16% of cases to the high-risk group and had a lower overall predictive capacity. When both the FLIPI and IPI were included in a multivariate analysis, only the FLIPI was related to survival. Multivariate analysis of DFS also identified the FLIPI, and not the IPI, as independently significant. Thus, in the present study, the FLIPI was superior as a prognostic model compared with the IPI and can therefore be recommended as a clinical prognostic index for MCL.

Adult↗

PubMed2002

Stage II endometrial carcinoma: prognostic factors and risk classification in 170 patients.

PURPOSE: Factors affecting outcome in patients with surgicopathologic Stage II endometrial cancer are poorly defined. The purpose of this study was to determine prognostic factors in a series of patients treated according to standardized protocols at a single institution. METHODS AND MATERIALS: One hundred and seventy patients referred to Princess Margaret Hospital after hysterectomy between 1984 and 1995 were retrospectively reviewed. One hundred and twenty patients received postoperative external beam radiotherapy and brachytherapy, 18 received external beam radiotherapy alone, five received brachytherapy alone, and 27 had no radiotherapy. RESULTS: With a median follow-up of 5.1 years, overall and disease-free survival (DFS) at 5 years was 77% and 68%, respectively, and 24% of patients had relapsed. Significant independent adverse factors for DFS included age >65 (p = 0.0001), FIGO Stage IIB (p = 0.02), and capillary-lymphatic space (CLS) involvement (p = 0.0007). Prognostic factors for relapse were age (p = 0.0008), histologic grade (p = 0.01), and CLS (p = 0.01). A prognostic model based on the number of adverse prognostic factors (0-3) revealed that the 5-year survival rates for the four groups were as follows: 0%-85%, 1%-77%, 2%-55%, and 3%-11%. Combining the groups with 0 or 1 adverse factors resulted in a three-group variable that was strongly related to DFS (p < 0.0001). CONCLUSIONS: Patient age, stage, and CLS were significant factors for DFS, and age, grade, and CLS predicted time to relapse in Stage II endometrial cancer. A prognostic model for DFS using these factors can provide clinically meaningful outcome predictions.

Adult↗

PubMed2003

Predicting mortality in patients with heart failure: a pragmatic approach.

OBJECTIVE: To develop a comprehensive and easily applicable prognostic model predicting mortality risk in patients with moderate to severe heart failure. DESIGN: Prospective follow up study. SETTING: Seven general hospitals in the Netherlands. PATIENTS: 152 outpatients with heart failure or patients admitted to hospital because of heart failure, who were included in a randomised trial to assess the impact of a pharmacist led intervention to improve drug compliance. Duration of follow up was at least 18 months. MAIN OUTCOME MEASURES: Multivariable logistic regression modelling was used to evaluate information from history, physical examination (for example, blood pressure), drug use, and quality of life questionnaires that independently contributed to the prediction of death. The area under receiver operating characteristic curves (AUC) was used to estimate the predictive ability of the prognostic models. RESULTS: During the 18 months of follow up, 51 patients (34%) died. Independent predictors of mortality were diabetes mellitus, a history of renal dysfunction (or higher creatinine), New York Heart Association (NYHA) functional class III or IV, lower weight or body mass index, lower blood pressure, ankle oedema, and higher scores on a disease specific quality of life questionnaire. The use of beta blockers was predictive of a better prognosis. These factors were used to derive various prediction formulas. A model based on medical history, weight, presence of oedema, and lower blood pressure had an AUC of 0.77. Addition of use of beta blockers to this model improved the AUC to 0.80. Addition of NYHA class increased the AUC to 0.84. Data on quality of life did not improve the AUC further (AUC 0.85). CONCLUSIONS: A prognostic model produced on the basis of easily obtainable information from medical history and physical examination can adequately stratify heart failure patients according to their short term risk of death.

Adult↗

PubMed2005

Molecular prognostic factors in diffuse large B-cell lymphoma.

The treatment of patients with diffuse large B-cell lymphoma (DLBCL) has been guided traditionally by clinical parameters such as the Ann Arbor Staging Classification for Hodgkin's disease. Although the International Prognostic Index (IPI) represents the most widely accepted prognostic model, there is still a marked variability in outcome within identical IPI subgroups, reflecting the heterogeneity of this malignancy. Use of DNA microarray, real-time reverse transcription polymerase chain reaction, and tissue array immunohistochemistry methodologies makes the development of new classifications possible based on molecular profiling. The molecular classification of DLBCL may lead to the grouping of specific disease entities sharing similar biologic features, clinical behavior, and outcome. Once tested and validated, this new generation of prognostic models should become an integral part of the daily practice, providing valuable additional information to the currently existing clinically based predictive models. To accomplish these goals and to be in a position in which existing or new prognostic models can be easily tested and validated, there is a strong need to collect frozen and paraffin-embedded material that can be used for RNA extraction and construction of tissue arrays, respectively. Such materials should be gathered as an integral part of any planned study.

Biomarkers, Tumor↗

PubMedDate not supplied

Prognostic factors, models and related statistical problems in the survival of endstage renal disease patients on hemodialysis.

We investigate factors associated with the survival of end-stage renal disease patients on hemodialysis. A model derived for non-diabetics includes five significant prognostic factors: age at initiation of dialysis, arteriosclerotic heart disease, cerebrovascular accident, cancer and chronic obstructive pulmonary disease. For diabetics, age at initiation of dialysis was the only significant prognostic factor. We also show that a simple exponential model adequately represents hemodialysis survival data, the relationship between age and survival hazard is not log-linear and data for diabetics require independent analysis or inclusion of interaction terms. In this paper, we chose the first approach.

Actuarial Analysis↗

PubMed1993

Updating prognosis in primary biliary cirrhosis using a time-dependent Cox regression model. PBC1 and PBC2 trial groups.

BACKGROUND: The precision of current prognostic models in primary biliary cirrhosis (PBC) is rather low, partly because they are based on data from just one time during the course of the disease. The aim of this study was to design a new, more precise prognostic model by incorporating follow-up data in the development of the model. METHODS: We have performed Cox regression analyses with time-dependent variables in 237 PBC patients followed up regularly for up to 11 years. The validity of the obtained models was tested by comparing predicted and observed survival in 147 independent PBC patients followed for up to 6 years. RESULTS: In the obtained model the following time-dependent variables independently indicated a poor prognosis: high bilirubin, low albumin, ascites, gastrointestinal bleeding, and old age. When including histological variables, cirrhosis, central cholestasis, and low immunoglobulin (Ig)M also indicated a poor prognosis. The survival predicted by the models agreed well with the survival observed in the independent PBC patients. The time-dependent models predicted better than our previously published time-fixed model. CONCLUSIONS: Using the time-dependent Cox models, one can estimate a more precise probability of surviving the next 1, 3, or 6 months for any given patient at any time during the course of the disease. This may improve monitoring of PBC patients.

Azathioprine↗

PubMed2026

Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.

BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.

Acral melanoma↗