Prognosis Of Lung Cancer Research Articles

Frailty and prognosis in lung cancer: systematic review and meta-analysis

Lung cancer is one of the most common malignant tumours. Patients are frequently at risk of frailty as lung cancer progresses. The meta-analysis aims to explore the impact of frailty...

Limning of HIF-2 and HIF-3 in the Tumor Microenvironment: Developing Concepts for the Treatment of Hypoxic Cancer.

Hypoxia, characterized by insufficient oxygen supply to tissues, is a significant factor in tumor growth and resistance to treatment. The hypoxia-inducible factor (HIF) signaling pathway is activated when oxygen levels decline, influencing cell activities and promoting tumor progression. HIF-1α and HIF-2α are the main targets for therapeutic intervention in tumors. Nevertheless, the significance of HIF-2α is often overlooked. This review examines the physiological role of HIF-2α in tumor growth and its involvement in tumor growth. HIFs, composed of hypoxia-responsive α and oxygeninsensitive β subunits, play a crucial role in controlling gene expression in both normal and solid tumor tissues under low oxygen levels. HIF-3α, formerly considered a detrimental modulator of HIF-regulated genes, exerts a transcriptional regulatory role by inhibiting gene expression through competition with HIF-1α and HIF-2α for binding to transcriptional sites in target genes under hypoxia. Recent research indicates that various HIF-3 variants exhibit distinct and potentially contrasting functionalities. Hypoxia often occurs during the initiation and progression of cancer formation. Recent research has discovered that HIF-2α, also known as endothelial PAS domain protein 1, has a significant impact on tumors. HIF-2α is a significant cancer-causing gene and a crucial predictor of prognosis in non-small cell lung cancer. However, due to limited research investigating the relationship between HIF-2α and small-cell lung cancer, it is not possible to reach a definitive conclusion. HIF-2α plays a vital function in cancer by preserving the stemness of cancer cells. This review provides a comprehensive overview of HIF-2 and the role of HIF-3 in various cancer-related processes, as well as its potential as a targeted therapeutic approach.

Construction of a prognostic model for extensive-stage small cell lung cancer patients undergoing immune therapy in northernmost China and prediction of treatment efficacy based on response status at different time points

Background and purposeRecently, the emergence of immune checkpoint inhibitors has significantly improved the survival of patients with extensive-stage small cell lung cancer. However, not all patients can benefit from immunotherapy; therefore, there is an urgent need for precise predictive markers to screen the population for the benefit of immunotherapy. However, single markers have limited predictive accuracy, so a comprehensive predictive model is needed to better enable precision immunotherapy. The aim of this study was to establish a prognostic model for immunotherapy in ES-SCLC patients using basic clinical characteristics and peripheral hematological indices of the patients, which would provide a strategy for the clinical realization of precision immunotherapy and improve the prognosis of small cell lung cancer patients.MethodsThis research retrospectively collected data from ES-SCLC patients treated with PD-1/PD-L1 inhibitors between March 1, 2019, and October 31, 2022, at Harbin Medical University Cancer Hospital. The study data was randomly split into training and validation sets in a 7:3 ratio. Variables associated with patients’ overall survival were screened and modeled by univariate and multivariate Cox regression analyses. Models were presented visually via Nomogram plots. Model discrimination was evaluated by Harrell’s C index, tROC, and tAUC. The calibration of the model was assessed by calibration curves. In addition, the clinical utility of the model was assessed using a DCA curve. After calculating the total risk score of patients in the training set, patients were stratified by risk using percentile partitioning. The Kaplan–Meier method was used to plot OS and PFS survival curves for different risk groups and response statuses at different milestone time points. Differences in survival time groups were compared using the chi-square test. Statistical analysis software included R 4.1.2 and SPSS 26.ResultsThis study included a total of 113 ES-SCLC patients who received immunotherapy, including 79 in the training set and 34 in the validation set. Six variables associated with poorer OS in patients were screened by Cox regression analysis: liver metastasis (P = 0.001), bone metastasis (P = 0.013), NLR < 2.14 (P = 0.005), LIPI assessed as poor (P < 0.001), PNI < 51.03 (P = 0.002), and LDH ≥ 146.5 (P = 0.037). A prognostic model for immunotherapy in ES-SCLC patients was constructed based on the above variables. The Harrell’s C-index in the training and validation sets of the model was 0.85 (95% CI 0.76–0.93) and 0.88 (95% CI 0.76–0.99), respectively; the AUC values corresponding to 12, 18, and 24 months in the tROC curves of the training set were 0.745, 0.848, and 0.819 in the training set and 0.858, 0.904 and 0.828 in the validation set; the tAUC curves show that the overall tAUC is > 0.7 and does not fluctuate much over time in both the training and validation sets. The calibration plot demonstrated the good calibration of the model, and the DCA curve indicated that the model had practical clinical applications. Patients in the training set were categorized into low, intermediate, and high risk groups based on their predicted risk scores in the Nomogram graphs. In the training set, 52 patients (66%) died with a median OS of 15.0 months and a median PFS of 7.8 months. Compared with the high-risk group (median OS: 12.3 months), the median OS was significantly longer in the intermediate-risk group (median OS: 24.5 months, HR = 0.47, P = 0.038) and the low-risk group (median OS not reached, HR = 0.14, P = 0.007). And, the median PFS was also significantly prolonged in the intermediate-risk group (median PFS: 12.7 months, HR = 0.45, P = 0.026) and low-risk group (median PFS not reached, HR = 0.12, P = 0.004) compared with the high-risk group (median PFS: 6.2 months). Similar results were obtained in the validation set. In addition, we observed that in real-world ES-SCLC patients, at 6 weeks after immunotherapy, the median OS was significantly longer in responders than in non-responders (median OS: 19.5 months vs. 11.9 months, P = 0.033). Similar results were obtained at 12 weeks (median OS: 20.7 months vs 11.9 months, P = 0.044) and 20 weeks (median OS: 20.7 months vs 11.7 months, P = 0.015). Finally, we found that in the real world, ES-SCLC patients without liver metastasis (P = 0.002), bone metastasis (P = 0.001) and a total number of metastatic organs < 2 (P = 0.002) are more likely to become long-term survivors after receiving immunotherapy.ConclusionThis study constructed a new prognostic model based on basic patient clinical characteristics and peripheral blood indices, which can be a good predictor of the prognosis of immunotherapy in ES-SCLC patients; in the real world, the response status at milestone time points (6, 12, and 20 weeks) can be a good indicator of long-term survival in ES-SCLC patients receiving immunotherapy.

The Role of Emphysema on Postoperative Prognosis in Early-Stage Nonsmall Cell Lung Cancer

BackgroundEmphysema is generally considered a poor prognostic factor for patients with nonsmall cell lung cancer; however, whether the poor prognosis is due to highly malignant tumors or emphysema itself remains unclear. This study was designed to determine the prognostic value of emphysema in patients with early-stage nonsmall cell lung cancer.MethodsA total of 721 patients with clinical stage IA nonsmall cell lung cancer who underwent complete resection between April 2007 and December 2018 were retrospectively analyzed regarding clinicopathological findings and prognosis related to emphysema.ResultsThe emphysematous and normal lung groups comprised 197 and 524 patients, respectively. Compared with the normal lung group, lymphatic invasion (23.9% vs. 14.1%, P = 0.003), vascular invasion (37.6% vs. 17.2%, P < 0.001), and pleural invasion (18.8% vs. 10.9%, P = 0.006) were observed more frequently in the emphysema group. Additionally, the 5-year overall survival rate was lower (77.1% vs. 91.4%, P < 0.001), and the cumulative incidence of other causes of death was higher in the emphysema group (14.0% vs. 3.50%, P < 0.001). Multivariable Cox regression analysis of overall survival revealed that emphysema (vs. normal lung, hazard ratio 2.02, P = 0.0052), age > 70 years (vs. < 70 years, hazard ratio 4.03, P < 0.001), and SUVmax > 1.8 (vs. ≤ 1.8, hazard ratio 2.20, P = 0.0043) were independent prognostic factors.ConclusionsEarly-stage nonsmall cell lung cancer with emphysema has a tendency for the development of highly malignant tumors. Additionally, emphysema itself may have an impact on poor prognosis.

Advances in systemic immune inflammatory indices in non-small cell lung cancer: A review.

Lung cancer is one of the most prevalent cancers globally, with non-small cell lung cancers constituting the majority. These cancers have a high incidence and mortality rate. In recent years, a growing body of research has demonstrated the intricate link between inflammation and cancer, highlighting that inflammation and cancer are inextricably linked and that inflammation plays a pivotal role in cancer development, progression, and prognosis of cancer. The Systemic Immunoinflammatory Index (SII), comprising neutrophil, lymphocyte, and platelet counts, is a more comprehensive indicator of the host's systemic inflammation and immune status than a single inflammatory index. It is widely used in clinical practice due to its cost-effectiveness, simplicity, noninvasiveness, and ease of acquisition. This paper reviews the impact of SII on the development, progression, and prognosis of non-small cell lung cancer.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models.

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied. We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis. We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.

Risk and prognosis of secondary lung cancer after radiation therapy for thoracic malignancies.

Radiation therapy (RT) may increase the risk of second cancer. This study aimed to determine the association between exposure to radiotherapy for the treatment of thoracic cancer (TC) and subsequent secondary lung cancer (SLC). The Surveillance, Epidemiology, and End Results (SEER) database (from 1975 to 2015) was queried for TC. Univariate Cox regression analyses and multiple primary standardized incidence ratios (SIRs) were used to assess the risk of SLC. Subgroup analyses of patients stratified by latency time since TC diagnosis, age at TC diagnosis, and calendar year of TC diagnosis stage were also performed. Overall survival and SLC-related death were compared among the RT and no radiation therapy (NRT) groups by using Kaplan-Meier analysis and competitive risk analysis. In a total of 329 129 observations, 147 847 of whom had been treated with RT. And 6799 patients developed SLC. Receiving radiotherapy was related to a higher risk of developing SLC for TC patients (adjusted HR, 1.25; 95% CI, 1.19-1.32; P < 0.001). The cumulative incidence of developing SLC in TC patients with RT (3.8%) was higher than the cumulative incidence (2.9%) in TC patients with NRT(P). The incidence risk of SLC in TC patients who received radiotherapy was significantly higher than the US general population (SIR, 1.19; 95% CI, 1.14-1.23; P < 0.050). Radiotherapy for TC was associated with higher risks of developing SLC compared with patients unexposed to radiotherapy.

Human epididymis protein 4, a novel potential biomarker for diagnostic and prognosis monitoring of lung cancer.

This study aimed to explore the application value of human epididymis protein 4 (HE4) in diagnosing and monitoring the prognosis of lung cancer. First, TCGA (The Cancer Genome Atlas) databases were used to analyze whey-acidic-protein 4-disulfide bond core domain 2 (WFDC2) gene expression levels in lung cancer tissues. Then, a total of 160 individuals were enrolled, categorized into three groups: the lung cancer group (n = 80), the benign lesions group (n = 40), and the healthy controls group (n = 40). Serum HE4 levels and other biomarkers were quantified using an electro-chemiluminescent immunoassay. Additionally, the expression of HE4 in tissues was analyzed through immunohistochemistry (IHC). In vitro cultures of human airway epithelial (human bronchial epithelial [HBE]) cells and various lung cancer cell lines (SPC/PC9/A594/H520) were utilized to detect HE4 levels via western blot (WB). Analysis of the TCGA and UALCAN (The University of Alabama at Birmingham Cancer Data Analysis Portal) databases showed that WFDC2 gene expression levels were upregulated in lung cancer tissues (p < 0.01). Compared with the control group and the benign group, HE4 was significantly higher in the serum of patients with lung cancer (p < 0.001). Receiver operating characteristic (ROC) analysis confirmed that HE4 had better diagnostic efficacy than classical markers in the differential diagnosis of lung cancer and benign lesions and had the highest diagnostic value in lung adenocarcinoma (area under the ROC curve [AUC] = 0.826). HE4 increased in early lung cancer and positively correlated with poor prognosis (p < 0.001). Moreover, the results of WB and IHC revealed that the expression of HE4 was increased in lung cancer cells (SPC/A549/H520) and lung cancer tissues but decreased in PC9 cells with a lack of exon EGFR19 (p < 0.05). Serum HE4 emerges as a promising novel biomarker for the diagnosis and prognosis assessment of lung cancer.

Impact of obstructive sleep apnea risk on prognosis and treatment responses of lung cancer

Aims: Obstructive sleep apnea (OSA) may affect oncogenic processes in a specific way for each tumor type. This study was conducted to reveal the relationship between OSA risk and prognosis and treatment responses in patients with lung cancer. Methods: This prospective study included stage III and IV lung cancer patients aged between 18 and 75 years. Patients with poor performance status, cranial metastasis, congestive heart failure, surgery history, and positive airway pressure device use were excluded. STOP-BANG questionnaire was used to assess the OSA risk. The primary end-point was the differences in the survival and treatment responses of patients at intermediate/high risk of OSA compared with those at low OSA risk. Data from the patients with non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) were analyzed separately. Results: Ninety-eight patients (34 SCLC and 64 NSCLC), mostly male (85.7%), with a mean age of 59.3±8 were included in the analysis. Overall survival was similar in the groups. However, in the SCLC group, those at low OSA risk had a shorter progression-free survival (PFS) than those at intermediate/high risk (105±31.8 days, vs 272±16.2 days, p=0.001). Cox regression analysis showed that low OSA risk was an independent risk factor for PFS in only the SCLC group (HR:4.9 CI:1.6-14.7, p=0.005). Conclusion: IOur results showed that low OSA risk was an independent poor prognostic factor for PFS in SCLC regardless of the tumor stage.

Multi-omics research progress in lung cancer

Lung cancer is a malignant tumor that seriously threatens human health, which has become one of the major health concerns that people care. Clinically, lung cancer is divided into small-cell lung cancer and non-small cell lung cancer, with non-small cell lung cancer accounting for 80% -85% of all pathological types of lung cancer. With the research on lung cancer, it has been found that most cases of lung cancer are already in the advanced stage, so early diagnosis of lung cancer is crucial. With the development of high-throughput technology, multi-omics analysis has provided us with a large amount of data for studying lung cancer. The study has discovered the important roles of genomics, transcriptomics, proteomics, and metabolomics in lung cancer. Based on the comprehensive analysis of multi-omics for lung cancer, we can identify the crucial biomarkers that are beneficial for early diagnosis of lung cancer for providing a better prognosis. This review provides the research progress of various omics technologies in lung cancer, so as to understand the molecular prognosis of lung cancer in clinical practice.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27±3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

The proportion of tumour stroma predicts response to treatment of immune checkpoint inhibitor in combination with chemotherapy in patients with stage IIIB-IV non-small cell lung cancer.

Immunotherapy has brought a new era to cancer treatment, yet we lack dependable predictors for its effectiveness. This study explores the predictive significance of intratumour stroma proportion (iTSP) for treatment success and prognosis in non-small cell lung cancer (NSCLC) patients undergoing treatment with immune check-point inhibitors (ICIs) together with chemotherapy. We retrospectively collected data from patients with unresectable stage IIIB-IV NSCLC who were treated with first-line ICIs and chemotherapy. Each patient received a confirmed pathological diagnosis, and the pathologist evaluated the iTSP on haematoxylin and eosin (H&E)-stained sections of diagnostic tissue slides. Among the 102 H&E-stained biopsy samples, 61 (59.8%) were categorised as stroma-L (less than 50% iTSP), while 41 (40.2%) were classified as stroma-H (more than 50% iTSP). We observed that the stroma-L group exhibited a significantly better objective response rate (ORR) (72.1 versus 51.2%, P = 0.031) and deeper response depth (DpR) (-50.49 ± 28.79% versus -35.83 ± 29.91%, P = 0.015) compared to the stroma-H group. Furthermore, the stroma-L group showed longer median progression-free survival (PFS) (9.6 versus 6.0 months, P = 0.011) and overall survival (OS) (24.0 versus 12.2 months, P = 0.001) compared to the stroma-H group. Multivariate Cox proportional hazards regression analysis indicated that iTSP was a highly significant prognostic factor for both PFS [hazard ratio (HR) = 1.713; P = 0.030] and OS (HR = 2.225; P = 0.003). Our findings indicate that a lower iTSP corresponds to improved clinical outcomes and greater DpR in individuals with stage IIIB-IV NSCLC treated with first-line ICIs and chemotherapy. The iTSP could potentially serve as a predictive biomarker for ICIs therapy response.

Lung Cancer Prediction and Classification Using Decision Tree and VGG16 Convolutional Neural Networks

Introduction A malignant abnormal growth that starts in the tissues of the lungs is called Lung Cancer. It ranks among the most common and lethal cancers globally. Lung Cancer is particularly dangerous because of its aggressive nature and how quickly it can extend to other areas of the body. We propose a two-step verification architecture to check the presence of Lung Cancer. The model proposed by this paper first assesses the patient based on a few questions about the patient's symptoms and medical background. Then, the algorithm determines whether the patient has a low, medium, or high risk of developing lung cancer by diagnosing the response using the “Decision Tree” classification at an accuracy of 99.67%. If the patient has a medium or high risk, we further validate the finding by examining the patient's CT scan image using the “VGG16” CNN model at an accuracy of 92.53%. Background One of the key areas of research on Lung Cancer prediction is to identify patients based on symptoms and medical history. Its subjective nature makes it challenging to apply in real-world scenarios. Another research area in this field involves forecasting the presence of cancer cells using CT scan imagery, providing high accuracy. However, it requires physician intervention and is not appropriate for early-stage prediction. Objective This research aims to forecast the severity of Lung Cancer by analyzing the patient with a few questions regarding the symptoms and past medical conditions. If the patient has a medium or a high risk, we further examine their CT scan, validate the result and also predict the type of Lung Cancer. Methodology This paper uses the “Decision Tree” algorithm and the Customised “VGG16” model of CNN for the implementation. The “Decision Tree” algorithm is used to analyze the answers given by the patient to distinguish the severity of Lung Cancer. We further use Convolution Neural Networks with a Customised “VGG16” model to examine the patient's CT scan image, validate the result and categorize the type of Lung Cancer. Results The “Decision Tree” approach for forecasting the severity of lung cancer yields an accuracy of 99.67%. The accuracy of the customized “VGG16” CNN model to indicate the type of Lung Cancer suffered by the patient is 92.53% Conclusion This research indicates that our technique provides greater accuracy than the prior approaches for this problem and has extensive use in the prognosis of Lung Cancer.

Inaugural Scalp Metastasis of Pulmonary Squamous Cell Carcinoma: A Rare Case Report and Literature Review

Distant cutaneous metastasis of primary lung squamous cell carcinoma is an exceedingly rare event, with scalp metastasis as the initial clinical presentation even rarer. Scalp skin metastases are prone to be misdiagnosed as other scalp disorders, yet their appearance signifies the deterioration and poor prognosis of lung cancer. This case report documents a female patient presenting initially with scalp folliculitis in dermatology, who was subsequently diagnosed with malignant lung tumor through radiological imaging and referred to Department of Thoracic Surgery. Pathological examination of the excised lesion from the scalp revealed distant metastasis of lung cancer. A review of similar cases reported in literature was conducted. This article aims to enhance understanding and awareness of skin metastasis in lung cancer, to emphasize the importance of this condition, and to improve early recognition and precise diagnosis. It is crucial to prevent clinical misdiagnosis and ensure appropriate treatment, finally leading to improve the prognosis of the patients. .

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank.

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

BIRC3-HSP90B1 Interaction Inhibits Non-Small Cell Lung Cancer Progression through the Extracellular Signal-Regulated Kinase Pathway.

The long-term prognosis of nonsmall cell lung cancer (NSCLC) remains unsatisfactory, which is a major challenge in lung cancer treatment. BIRC3 is an inhibitor of apoptosis (IAP) protein that contributes to tumor regulation. However, the underlying regulatory mechanisms of BIRC3 in NSCLC remains unknown. We initiated an analysis of BIRC3 expression data in NSCLC tumors and adjacent tissues using the TCGA and GEO databases and examined the variations in prognosis. Further, we conducted overexpression (OE) and knockdown (KD) studies on BIRC3 to evaluate its effects on NSCLC cell proliferation, migration, and invasion. Additionally, through utilization of a nude mouse model, the regulatory effects of BIRC3 on NSCLC were verified in vivo. Co-immunoprecipitation (Co-IP) assay served to pinpoint the proteins with which BIRC3 interacts. The results indicated that BIRC3 is down-regulated in NSCLC tissues and that patients with high BIRC3 expression demonstrate a better prognosis. BIRC3 is a tumor suppressor, inhibiting the proliferation and metastasis of NSCLC. Co-IP results revealed that BIRC3 interacts with HSP90B1, leading to a decrease in HSP90B1 expression and subsequent negative regulation of the ERK signaling pathway. BIRC3 may serve as a prognostic biomarker for NSCLC. It directly interacts with HSP90B1 to negatively regulate the ERK signaling pathway, thereby hindering the progression of NSCLC.

PARP1 promotes EGFR-TKI drug-resistance via PI3K/AKT pathway in non-small-cell lung cancer.

Tyrosine kinase inhibitor (TKI) resistance is the main type of drug resistance in lung cancer patients with epidermal growth factor receptor (EGFR) mutations, but its underlying mechanism remains unclear. The purpose of this work was to investigate the mechanism by which PARP1 regulates EGFR-TKI resistance to identify potential targets for combating drug resistance. The GEO databases, TCGA databases, western blot and qPCR studies were used to investigate the expression of PARP1 in lung cancer cells and tissues and its correlation with the prognosis of lung cancer. The expression of PARP1 in lung cancer TKI resistant cell PC9-ER and TKI sensitive cell PC9 was analyzed by qPCR and western blot. After knocking down of PARP1, CCK-8 assays, colony formation, flow cytometry were used to investigate its impact on erlotinib sensitivity, cell survival, cell cycle, and apoptosis. RNA-seq was used to investigate the mechanism by which PARP1 participates in EGFR-TKI resistance, and the results were validated in vitro and in vivo studies. PARP1 was highly expressed in both lung cancer tissues and cells. Subsequently, increased PARP1 expression was observed in PC9-ER compared with its parental cell line. Knockdown of PARP1 increased erlotinib sensitivity, promoted cell apoptosis, and suppressed cell growth. RNA-seq and previous studies have shown that the PI3K/AKT/mTOR/P70S6K pathway is involved in PARP1-mediated TKI resistance, and these results were confirmed by Western blot in vitro and in vivo. PARP1 may serve as a potential therapeutic target for reversing EGFR-TKI resistance in NSCLC via the PI3K/AKT/mTOR/P70S6K pathway.

Nucleic acid-sensing-related gene signature in predicting prognosis and treatment efficiency of small cell lung cancer patients.

Nucleic acid-sensing (NAS) pathways could induce innate and adaptive immune responses. However, rare evidence exhibited how the core genes of the NAS pathways affected the immune response and prognosis of small cell lung cancer (SCLC) patients. We conducted a comprehensive bioinformatic analysis based on the RNA profiles of 114 SCLC patients, including 79 from cBioPortal, 21 from GSE30219, and 14 from our sequencing data. The multiplex immunohistochemistry (mIHC) was used to characterize the role of NAS related genes in the tumor microenvironment (TME) of SCLC. A prognostic model (7NAS risk model) was constructed based on 7 NAS-related genes which was demonstrated as an independent prognostic index. The low-risk group was identified to have a better prognosis and an immune-activated microenvironment in both the public datasets and our dataset. Intriguingly, mIHC data showed that CD45+ immune cells, CD8+ T lymphocytes, and CD68+ macrophages were prevalently enriched in low-risk SCLC patients and positively correlated with IRF1 expression. Additionally, Patients in the low-risk group might have superior responses to chemotherapy and immunotherapy. Conclusively, this study created a new risk model based on genes associated with NAS pathways which could predict the prognosis and response of treatment in patients with SCLC.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.