Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer

LINC01133 promotes pancreatic ductal adenocarcinoma epithelial-mesenchymal transition mediated by SPP1 through binding to Arp3

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with limited treatment methods. Long non-coding RNAs (lncRNAs) have been found involved in tumorigenic and progression. The present study revealed that LINC01133, a fewly reported lncRNA, was one of 16 hub genes that could predict PDAC patients' prognosis. LINC01133 was over-expressed in PDAC tumors compared to adjacent pancreas and could promote PDAC proliferation and metastasis in vitro and in vivo, as well as inhibit PDAC apoptosis. LINC01133 expression positively correlated to secreted phosphoprotein 1 (SPP1) expression, leading to an enhanced epithelial-mesenchymal transition (EMT) process. LINC01133 bound with actin-related protein 3 (Arp3), the complex reduced SPP1 mRNA degradation which increased SPP1 mRNA level, ultimately leading to PDAC proliferation. This research revealed a novel mechanism of PDAC development and provided a potential prognosis indicator that may benefit PDAC patients.

A Novel Pancreatic Cancer Hypoxia Status Related Gene Signature for Prognosis and Therapeutic Responses

Pancreatic cancer (PAC) is a highly fatal and aggressive type of cancer. Hypoxia is a common feature of PAC. The aim of this study was to develop a hypoxia status-related prognostic model for predicting the survival outcomes in PAC. The data sets of PAC from The Cancer Genome Atlas and the International Cancer Genome Consortium were used to construct and validate the signature. A 6 hypoxia status-related differential expression genes prognostic model for predicting the survival outcomes was established. The Kaplan-Meier analysis and Received operating characteristic curve indicated the good performance of the signature at predicting overall survival. Univariate and Multivariate Cox regression revealed that the signature was an independent prognostic factor in PAC. Weighted Gene Co-expression Network Analysis and immune infiltration analysis indicated that Immune-related pathways and immune cell infiltration was mostly enriched in the low-risk group, which presented a better prognosis. We also evaluated the predictive of the signature for immunotherapy and chemoradiotherapy. Risk gene LY6D may be a potential prognostic predictor of PAC. This model can be used as an independent prognostic factor for predicting clinical outcomes and a possible classifier for response to chemotherapy.

ANKRD22 aggravates sepsis-induced ARDS and promotes pulmonary M1 macrophage polarization

Acute respiratory distress syndrome (ARDS) is independently associated with a poor prognosis in patients with sepsis. Macrophage M1 polarization plays an instrumental role in this process. Therefore, the exploration of key molecules affecting acute lung injury and macrophage M1 polarization may provide therapeutic targets for the treatment of septic ARDS. Here, we identified that elevated levels of Ankyrin repeat domain-containing protein 22 (ANKRD22) were associated with poor prognosis and more pronounced M1 macrophage polarization in septic patients by analyzing high-throughput data. ANKRD22 expression was also significantly upregulated in the alveolar lavage fluid, peripheral blood, and lung tissue of septic ARDS model mice. Knockdown of ANKRD22 significantly attenuated acute lung injury in mice with sepsis-induced ARDS and reduced the M1 polarization of lung macrophages. Furthermore, deletion of ANKRD22 in macrophages inhibited M1 macrophage polarization and reduced levels of phosphorylated IRF3 and intracellular interferon regulatory factor 3 (IRF3) expression, while re-expression of ANKRD22 reversed these changes. Further experiments revealed that ANKRD22 promotes IRF3 activation by binding to mitochondrial antiviral-signaling protein (MAVS). In conclusion, these findings suggest that ANKRD22 promotes the M1 polarization of lung macrophages and exacerbates sepsis-induced ARDS.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Identification and validation of a prognostic signature based on six immune-related genes for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a prevalent malignancy with high mortality and morbidity rates. Although the significant efficacy of immunotherapy is well established, it is only beneficial for a limited number of individuals with CRC.

METHODS

Differentially expressed immune-related genes (DE-IRGs) were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ImmPort databases. A prognostic signature comprising DE-IRGs was developed using univariate, LASSO, and multivariate Cox regression analyses. A nomogram integrating the independent prognostic factors was also developed. CIBERSORT was used to assess immune cell infiltration (ICI). Furthermore, wound-healing, colony formation, migration, and invasion assays were performed to study the involvement of ACTG1 in CRC.

RESULTS

A signature including six DE-IRGs was developed. The overall survival (OS) rate was accurately estimated for TCGA and GSE38832 cohorts. The risk score (RS) of the signature was an independent factor for OS. Moreover, a nomogram encompassing age, RS, and pathological T stage accurately predicted the long-term OS probability of individuals with CRC. The high-risk group had an elevated proportion of patients treated with ICIs, including native B cells, relative to the low-risk group. Additionally, ACTG1 expression was upregulated, which supported the proliferation, migration, and invasion abilities of CRC cells.

CONCLUSIONS

An immune-related prognostic signature was developed for predicting OS and for determining the immune status of individuals with CRC. The present study provides new insights into accurate immunotherapy for individuals with CRC. Moreover, ACTG1 may serve as a new immune biomarker.

Identification of molecular subtypes and a prognostic signature based on m6A/m5C/m1A-related genes in lung adenocarcinoma

Lung cancer, specifically the histological subtype lung adenocarcinoma (LUAD), has the highest global occurrence and fatality rate. Extensive research has indicated that RNA alterations encompassing m6A, m5C, and m1A contribute actively to tumorigenesis, drug resistance, and immunotherapy responses in LUAD. Nevertheless, the absence of a dependable predictive model based on m6A/m5C/m1A-associated genes hinders accurately predicting the prognosis of patients diagnosed with LUAD. In this study, we collected patient data from The Cancer Genome Atlas (TCGA) and identified genes related to m6A/m5C/m1A modifications using the GeneCards database. The "ConsensusClusterPlus" R package was used to produce molecular subtypes by utilizing genes relevant to m6A/m5C/m1A identified through differential expression and univariate Cox analyses. An independent prognostic factor was identified by constructing a prognostic signature comprising six genes (SNHG12, PABPC1, IGF2BP1, FOXM1, CBFA2T3, and CASC8). Poor overall survival and elevated expression of human leukocyte antigens and immune checkpoints were correlated with higher risk scores. We examined the associations between the sets of genes regulated by m6A/m5C/m1A and the risk model, as well as the immune cell infiltration, using algorithms such as ESTIMATE, CIBERSORT, TIMER, ssGSEA, and exclusion (TIDE). Moreover, we compared tumor stemness indices (TSIs) by considering the molecular subtypes related to m6A/m5C/m1A and risk signatures. Analyses were performed based on the risk signature, including stratification, somatic mutation analysis, nomogram construction, chemotherapeutic response prediction, and small-molecule drug prediction. In summary, we developed a prognostic signature consisting of six genes that have the potential for prognostication in patients with LUAD and the design of personalized treatments that could provide new versions of personalized management for these patients.

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BACKGROUND

The recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.

METHODS

We extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan-Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.

RESULTS

Seven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675-0.690), 0.689 (95% CI: 0.677-0.701), and 0.823 (95% CI: 0.786-0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.

CONCLUSION

The nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

Prognostic value of RRM1 and its effect on chemoresistance in pancreatic cancer

PURPOSE

Pancreatic cancer (PC) remains a lethal disease, and gemcitabine resistance is prevalent. However, the biomarkers suggestive of gemcitabine resistance remain unclear.

METHODS

Bioinformatic tools identified ribonucleotide reductase catalytic subunit M1 (RRM1) in gemcitabine-related datasets. A cox regression model revealed the predictive value of RRM1 with clinical features. An external clinical cohort confirmed the prognostic value of RRM1. RRM1 expression was validated in gemcitabine-resistant cells in vitro and in orthotopic PC model. CCK8, flow cytometry, transwell migration, and invasion assays were used to explore the effect of RRM1 on gemcitabine-resistant cells. The CIBERSORT algorithm investigated the impact of RRM1 on immune infiltration.

RESULTS

The constructed nomogram based on RRM1 effectively predicted prognosis and was further validated. Moreover, patients with higher RRM1 had shorter overall survival. RRM1 expression was significantly higher in PC tissue and gemcitabine-resistant cells in vitro and in vivo. RRM1 knockdown reversed gemcitabine resistance, inhibited migration and invasion. The infiltration levels of CD4 + T cells, CD8 + T cells, neutrophils, and plasma cells correlated markedly with RRM1 expression, and communication between tumor and immune cells probably depends on NF-κB/mTOR signaling.

CONCLUSION

RRM1 may be a potential marker for prognosis and a target marker for gemcitabine resistance in PC.

Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs

Lung adenocarcinoma (LUAD) is a highly lethal malignant tumor. While the involvement of multiple mRNAs in the progression of LUAD is well established, the potential diagnostic value of immune-related mRNAs (irmRNAs) in LUAD remains largely unexplored. In this study, we utilized RNA-seq, clinical data, and immune-related gene information from LUAD patients to identify differentially expressed immune-related mRNAs (DEirmRNAs) and developed a predictive risk model based on specific DEirmRNA pairs closely linked with patient prognosis. We classified patients into high-risk and low-risk groups and analyzed factors such as survival rate, clinical characteristics, gene enrichment, immune cell infiltration, tumor mutation load, and drug susceptibility. We confirmed the expression levels of these DEirmRNAs in tumor tissues using qRT-PCR assay. Our results showed that the low-risk group had a longer survival time and lower tumor mutation burden (TMB) and microsatellite instability (MSI) compared to the high-risk group. The high-risk group also had a significant reduction in the number of certain immune cells and a lower half-maximum inhibitor concentration (IC50). We identified specific DEirmRNA pairs that were up-regulated or down-regulated in tumor tissues compared to adjacent tissues. Our prognostic risk model based on DEirmRNA pairs could be used to predict the prognosis of LUAD patients and provide reference for better treatment.

Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study

Introduction

Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.

Methods

A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.

Results

After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.

Discussion

This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.

Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer

Background: Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Methods: Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Results: Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. Conclusions: We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.

ANKRD22 promotes M2 polarization in lung adenocarcinoma macrophages via the glycolytic pathway

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with a low 5-year survival rate. ANKRD22 is an ankyrin repeat protein capable of promoting tumor progression, and its mechanism in LUAD remains elusive. Our study aims to investigate the mechanisms underlying the involvement of ANKRD22 in the progression of LUAD. The expression of ANKRD22 in LUAD and its enriched pathway were analyzed by bioinformatics analysis. Meanwhile, the correlation between ANKRD22 and the expression of glycolysis-related genes and M2 macrophage marker genes was analyzed. qRT-PCR was used for determination of the expression of ANKRD22, IL-10 and CCL17, CCK-8 for cell viability, and western blot for expression of ANKRD22, LDHA, HK2, PGK1, and PKM2. Immunofluorescence and flow cytometry were utilized to examine the level of CD163, and kits were used to measure the contents of pyruvic acid, lactate, citrate, and malate. Seahorse XF96 analyzer was employed to determine extracellular acidification rate (ECAR) and oxygen consumption rate (OCR). Mitochondrial membrane potential was assessed using the JC-1 probe. Bioinformatics analysis, qRT-PCR, and western blot showed that ANKRD22 was highly expressed in LUAD, which had a positive connection with M2 marker genes. Knockdown of ANKRD22 considerably attenuated the expression of ANKRD22, IL-10, and CCL17 in M2. ANKRD22 overexpression demonstrated the opposite results. Bioinformatics analysis uncovered that ANKRD22 was enriched in the glycolytic pathway and positively correlated with glycolysis-related genes. The knockdown of ANKRD22 substantially attenuated pyruvic acid, lactate, citrate, malate, and ECAR levels and elevated OCR levels in cells. The knockdown of ANKRD22 also reduced mitochondrial membrane potential. Further, it was discovered that glycolysis-related genes had a positive correlation with M2 marker genes. It was revealed by rescue experiments that the usage of 2-DG, a glycolytic inhibitor, remarkably reversed the facilitating effect of overexpression of ANKRD22 on M2 polarization. This study demonstrates that ANKRD22 can facilitate LUAD M2 polarization through glycolysis, and targeting ANKRD22 to inhibit M2 polarization has the potential to be a new strategy for LUAD treatment.

Kallikrein-related peptidase 10 predicts prognosis and mediates tumor immunomodulation in colorectal cancer

The incidence and mortality rates of colorectal cancer (CRC) have significantly increased in recent years. It has been shown that early diagnosis of CRC improves the five-year survival of patients compared to late diagnosis, as patients with stage I disease have a five-year survival rate as high as 90 %. Through bioinformatics analysis, we identified Kallikrein 10 (KLK10), a member of the Kallikrein family, as a reliable predictor of CRC progression, particularly in patients with early-stage CRC. Furthermore, single-cell analysis revealed that KLK10 was highly expressed in tumor and partial immune cells. Analysis of the biological functions of KLK10 using the Kyoto encyclopedia of genes and genomes and gene ontology indicated that KLK10 plays a role in the proliferation and differentiation of cancer cells, along with the maintenance of tumor function and immune regulation, explicitly by T cells and macrophages. EdU cell proliferation staining, plate clone formation assay, and cell scratch assay demonstrated that KLK10 inhibition by siRNA affected the proliferation and migration of CRC cells. Cell cycle detection by flow cytometry demonstrated that KLK10 inhibition led to cell cycle arrest in the G1 phase. In addition, the proportion of M1 and M2 macrophages in 45 tumor specimens was analyzed by immunohistochemistry, the proportion of CD4+ T cells and CD8+ T cells in plasma was identified by flow cytometry, and their correlation with KLK10 was analyzed. The effects of KLK10 on T cells and macrophages were verified in independent cell experiments. The results revealed that KLK10 also activates CD4+ T cells, mediating M2-type macrophage polarization.

Screening protective miRNAs and constructing novel lncRNAs/miRNAs/mRNAs networks and prognostic models for triple-negative breast cancer

Triple-negative breast cancer (TNBC) represents 10-20 % of all breast cancer (BC) cases and is characterized by poor prognosis. Given the urgent need to improve prognostication and develop specific therapies for TNBC, the identification of new molecular targets is of great importance. MicroRNA (miRNA) has been reported as a valuable and novel molecular target in the progression of TNBC. However, the expression and function of miRNAs in different tumors are heterogeneous. Herein, we first analyzed miRNA data from The Cancer Genome Atlas (TCGA) and surprisedly found that overexpressed miRNAs were associated with poor survival in all breast cancer patients, but the overexpressed miRNAs were associated with better survival in TNBC patients. Based on the heterogeneity of miRNA expression in TNBC, we conducted further analysis using univariate Cox proportional hazard regression models and identified 17 miRNAs with prognostic potential. Subsequently, a multivariate Cox model was employed to create a 3-miRNA prognostic model for predicting overall survival in TNBC patients. The diagnostic model exhibited an area under the curve (AUC) of 0.727, and multivariable Cox regression indicated that each covariate was associated with survival. These data indicate that this model is relatively accurate and robust for risk assessment, which have a certain value for clinical application. In order to explore the network behind the overexpressed miRNAs in TNBC, we established a novel network consisting of lncRNAs, miRNAs, and mRNAs through complete transcriptome data from matched samples in the TCGA database. In this network, IRS-1 appeared to be the top hub gene. Experimental results demonstrated that miR-15b-5p and miR-148a-3p effectively target IRS-1 in vitro, shedding light on the intricate regulatory mechanisms in TNBC mediated by the heterogeneous miRNAs. Besides, miR-148a-3p significantly inhibited cell migration and viability. Overall, this study may add valuable insights into the molecular landscape of TNBC based on miRNAs and have the potential to contribute to the development of targeted therapies and improved prognostic strategies of TNBC.

Comprehensive analysis of HDAC7 expression and its prognostic value in diffuse large B cell lymphoma: A review

HDAC7 loss or dysregulation may lead to B cell-based hematological malignancies. This study aimed to explore the prognostic value of HDAC7 in patients with diffuse large B cell lymphoma (DLBCL). RNA sequencing data and clinical information for HDAC7 in DLBCL were collected from the cancer genome atlas database and analyzed using R software. Paired t and Mann-Whitney U tests were used to detect differences between DLBCL and adjacent normal tissues, and the pROC software package was used to generate receiver operator characteristic curves to detect cutoff values for HDAC7. Data from paraffin-embedded specimens from the 2 groups were used for validation of external immunohistochemical staining. The tumor immunity estimation resource and integrated repository portal for tumor immune system interactions databases were used to analyze the correlation between HDAC7 and DLBCL immune cell infiltration. Survival analysis of HDAC7 in patients with DLBCL was performed using the PrognoScan database. Compared with that in normal tissues, HDAC7 mRNA was overexpressed in DLBCL. The HDAC7 immunohistochemical staining scores of stage III and IV DLBCL patients were significantly lower than those of stage I and II DLBCL patients, which was associated with shorter overall survival and disease-specific survival. In addition, the higher expression of HDAC7 may play a role in the lower level of immune infiltration in DLBCL. Downregulation of HDAC7 expression was correlated with poor prognosis and immune infiltration in DLBCL patients.

TRP Channels in Cancer: Signaling Mechanisms and Translational Approaches

Ion channels play a crucial role in a wide range of biological processes, including cell cycle regulation and cancer progression. In particular, the transient receptor potential (TRP) family of channels has emerged as a promising therapeutic target due to its involvement in several stages of cancer development and dissemination. TRP channels are expressed in a large variety of cells and tissues, and by increasing cation intracellular concentration, they monitor mechanical, thermal, and chemical stimuli under physiological and pathological conditions. Some members of the TRP superfamily, namely vanilloid (TRPV), canonical (TRPC), melastatin (TRPM), and ankyrin (TRPA), have been investigated in different types of cancer, including breast, prostate, lung, and colorectal cancer. TRP channels are involved in processes such as cell proliferation, migration, invasion, angiogenesis, and drug resistance, all related to cancer progression. Some TRP channels have been mechanistically associated with the signaling of cancer pain. Understanding the cellular and molecular mechanisms by which TRP channels influence cancer provides new opportunities for the development of targeted therapeutic strategies. Selective inhibitors of TRP channels are under initial scrutiny in experimental animals as potential anti-cancer agents. In-depth knowledge of these channels and their regulatory mechanisms may lead to new therapeutic strategies for cancer treatment, providing new perspectives for the development of effective targeted therapies.

A five necroptosis-related lncRNA signature predicts the prognosis of bladder cancer and identifies hot or cold tumors

Bladder cancer (BC) is a leading cause of male cancer-related deaths globally. Immunotherapy is showing promise as a treatment option for BC. Numerous studies suggested that necroptosis and long noncoding RNAs (lncRNAs) were critical players in the development of cancers and interacting with cancer immunity. However, the prognostic value of necroptosis-related lncRNAs and their impact on immunotherapeutic response in patients with BC have yet to be well examined. Thus, this study aims to find new biomarkers for predicting prognosis and determining immune subtypes of BC to select appropriate patients from a heterogeneous population. The clinicopathology and transcriptome information from The Cancer Genome Atlas (TCGA) was downloaded, and coexpression analysis was performed to identify necroptosis-related lncRNAs. Then LASSO regression was employed to construct a prediction signature. The signature performance was evaluated by Kaplan-Meier (K-M) method, Time-dependent receiver operating characteristics (ROC). The functional enrichment, immune infiltration, immune checkpoint activation, and the half-maximal inhibitory concentration (IC50) of common drugs in risk groups were compared. The consensus clustering analysis based on lncRNAs associated with necroptosis was made to get 2 clusters to identify hot and cold tumors further. Lastly, the immune response between cold and hot tumors was discussed. In this study, a model containing 5 necroptosis-related lncRNAs was constructed. The risk score distribution of these lncRNAs was compared between low- and high-risk groups in the training, testing, and entire sets. K-M analysis showed that the low-risk patients had significantly better prognosis. The area under the ROC curve (AUC) for the 1-, 3-, and 5-year ROC curves in the entire sets were 0.690, 0.709, and 0.722, respectively. High-risk patients were enriched in lncRNAs related to tumor immunity and had better immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 1 and cluster 2, respectively. We developed a necroptosis-related signature based on 5 prognostic lncRNAs, expected to become a new tool for evaluating the prognosis of patients with BC and classifying hot or cold tumors, thus facilitating the development of precision therapy for BC.

Initial clinical and experimental analyses of ALDOA in gastric cancer, as a novel prognostic biomarker and potential therapeutic target

The effect of ALDOA, an important regulator of tumor metabolism and immune cell function, on gastric cancer (GC) immune infiltration has not been elucidated. Hence, we explored the feasibility of using ALDOA combined with immune molecular markers as novel prognostic or therapeutic targets for GC patients. Bioinformatic analyses were initially performed in multiple databases to assess the prognostic prediction values of ALDOA expression in GC. Subsequently, both ALDOA expression and the clinicopathological characteristics of a total of 114 GC patients who underwent curative gastrectomy were collected to demonstrate the potential association between ALDOA expression and the biological behaviors of GC. Next, the expression of ALDOA and its effect on prognosis were determined at the mRNA and protein levels, respectively, using tissue microarrays and cellular experiments. Subsequently, several molecular mechanisms were revealed based on elaborate analyses, indicating that ALDOA expression was potentially involved in the progression of GC and could be considered a promising biomarker for evaluating the prognosis of GC. High ALDOA expression was frequently found in GC cells and GC tissues at the mRNA and protein levels. Based on survival analysis, the expression of ALDOA indicated comparatively poor overall survival (OS) in GC and was identified as an independent prognostic predictor of GC. Correlation analysis showed that ALDOA expression had a positive association with lymph node metastasis in GC patients. Additionally, microRNA-1179 was found to play a key role in inhibiting the expression of ALDOA in the metabolic pathways of GC cells, which might disrupt the expression of various immune molecules and be detrimental to the prognosis of GC. ALDOA should be considered a promising molecular target for evaluating the prognosis of GC, owing to its potential role in immune regulation.

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression

BACKGROUND

HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination.

METHODS

This study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated.

RESULTS

Six factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases.

CONCLUSION

To conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

PI3K/Akt signalling pathway-associated long noncoding RNA signature predicts the prognosis of laryngeal cancer patients

The PI3K/Akt signalling pathway is associated with the occurrence and development of tumours and significantly affects the prognosis of patients. We established a predictive signature based on the PI3K/Akt pathway to predict the prognosis of patients. The RNA-seq and clinical data of laryngeal cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Three lncRNAs (MNX1-AS1, LINC00330, LSAMP-AS1) were selected through univariate, multivariate Cox and log-rank test analysis to establish a prognostic signature. The patients were then divided into high-risk and low-risk groups based on their risk score. In the TCGA training set, the survival time of the high-risk group was shorter than that of the low-risk group (P < 0.01). Follicular helper T cells were lower in the high-risk group (P = 0.022), and CCR, inflammation promotion, parainflammation, and type I IFN immune function were suppressed. The results of the drug sensitivity analysis suggest that the high-risk group is sensitive to AKT inhibitors. The establishment of the signature was also verified based on the clinical data. Three lncRNAs can facilitate the migration, invasion, and vitality of cancer cells in vitro, and vice versa. Moreover, p-AKT (Ser473) and p-PI3K were highly activated in the cells overexpressing the abovementioned three lncRNAs. The PI3K/Akt signalling pathway-associated prognosis signature has a good predictive effect.

Prognostic value and potential molecular mechanism of ITGB superfamily members in hepatocellular carcinoma

We analyzed the prognostic value and potential molecular mechanisms of the members of integrin β (ITGB)superfamily in hepatocellular carcinoma (HCC) using data from The Cancer Genome Atlas (TCGA), cBioPortal, Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) HPA, Search Tool for the Retrieval of Interacting Genes/Proteins, GeneMANIA, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER and Gene set enrichment analysis (GSEA) databases. ITGB4/5 mRNA was upregulated in HCC tissues in contrast to the normal liver tissues, whereas ITGB2/3/8 levels were lower in the former. ITGB4 was the most frequently mutated ITGB gene in HCC. Receiver operating characteristic curve (ROC) analysis showed that the expression levels of ITGB2/3/4/5/7/8 had significant diagnostic value in distinguishing HCC tissues from healthy liver tissues, ITGB8 had the highest diagnostic efficacy. The ITGB1/3/6/8 were also upregulated in the HCC tissues in contrast to healthy liver tissues. The expression of ITGB8 was verified by immunohistochemistry (IHC). Furthermore, ITGB6 and ITGB7 expression levels were strongly associated with the overall survival (OS) of HCC patients. The ITGB superfamily members exhibited homology and interactions in protein structure. In addition, ITGB6 together with ITGB7 were negatively related to the infiltration of multiple immune cell populations. GSEA results showed that ITGB6 was enriched in HCC migration and recurrence, whereas ITGB7 was significantly enriched in HIPPO, TOLL and JAK-STAT signaling pathways. In conclusion, ITGB6 and ITGB7 genes are possible to be prognostic biomarkers for HCC.

Establishment and validation of a nomogram based on coagulation parameters to predict the prognosis of pancreatic cancer

BACKGROUND

In recent years, multiple coagulation and fibrinolysis (CF) indexes have been reported to be significantly related to the progression and prognosis of some cancers.

OBJECTIVE

The purpose of this study was to comprehensively analyze the value of CF parameters in prognosis prediction of pancreatic cancer (PC).

METHODS

The preoperative coagulation related data, clinicopathological information, and survival data of patients with pancreatic tumor were collected retrospectively. Mann Whitney U test, Kaplan-Meier analysis, and Cox proportional hazards regression model were applied to analyze the differences of coagulation indexes between benign and malignant tumors, as well as the roles of these indexes in PC prognosis prediction.

RESULTS

Compared with benign tumors, the preoperative levels of some traditional coagulation and fibrinolysis (TCF) indexes (such as TT, Fibrinogen, APTT, and D-dimer) were abnormally increased or decreased in patients with pancreatic cancer, as well as Thromboelastography (TEG) parameters (such as R, K, α Angle, MA, and CI). Kaplan Meier survival analysis based on resectable PC patients showed that the overall survival (OS) of patients with elevated α angle, MA, CI, PT, D-dimer, or decreased PDW was markedly shorter than other patients; moreover, patients with lower CI or PT have longer disease-free survival. Further univariate and multivariate analysis revealed that PT, D-dimer, PDW, vascular invasion (VI), and tumor size (TS) were independent risk factors for poor prognosis of PC. According to the results of modeling group and validation group, the nomogram model based on independent risk factors could effectively predict the postoperative survival of PC patients.

CONCLUSION

Many abnormal CF parameters were remarkably correlated with PC prognosis, including α Angle, MA, CI, PT, D-dimer, and PDW. Furthermore, only PT, D-dimer, and PDW were independent prognostic indicators for poor prognosis of PC, and the prognosis prediction model based on these indicators was an effective tool to predict the postoperative survival of PC.

Anoikis-Related Gene Signature for Prognostication of Pancreatic Adenocarcinoma: A Multi-Omics Exploration and Verification Study

Anoikis, a form of apoptosis that occurs due to detachment of cells from the extracellular matrix, has been linked to the development of cancer in several studies. However, its role in pancreatic cancer remains incompletely understood. In this study, we utilized univariate Cox regression and LASSO regression analyses to establish a prognostic model for pancreatic adenocarcinoma based on anoikis-related genes in the TCGA database. Additionally, we performed univariate and multifactorial Cox analyses of protein expression results for TCGA pancreatic adenocarcinoma. We further explored the difference in immune infiltration between the high-risk and low-risk groups and verified the expression of the screened genes using quantitative real-time PCR (qRT-PCR). Our findings indicate that numerous anoikis-related genes are linked to pancreatic adenocarcinoma prognosis. We identified seven prognostic genes (MET, DYNLL2, CDK1, TNFSF10, PIP5K1C, MSLN, GKN1) and validated that their related proteins, such as EGFR and MMP2, have a significant impact on the prognosis of pancreatic adenocarcinoma. Based on clustering analyses of the seven prognostic genes, patients could be classified into three distinct categories, for which somatic mutations varied significantly across the groups. High-risk and low-risk groups also exhibited significant differences in immune infiltration. All genes were found to be highly expressed in pancreatic cancer cell lines (ASPC-1, CFPAC-1) as compared to a normal pancreatic cell line (HPDE). Based on the seven anoikis-related genes, we formulated a robust prognostic model with high predictive accuracy. We also identified the significant impact of KRAS, P53, and CDKN2A mutations on the prognosis of this fatal disease. Therefore, our study highlights the crucial role of anoikis in the development of the pancreatic adenocarcinoma tumor microenvironment.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

The expression characteristics of transmembrane protein genes in pancreatic ductal adenocarcinoma through comprehensive analysis of bulk and single-cell RNA sequence

Background

Transmembrane (TMEM) protein genes are a class of proteins that spans membranes and function to many physiological processes. However, there is very little known about TMEM gene expression, especially in cancer tissue. Using single-cell and bulk RNA sequence may facilitate the understanding of this poorly characterized protein genes in PDAC.

Methods

We selected the TMEM family genes through the Human Protein Atlas and characterized their expression by single-cell and bulk transcriptomic datasets. Identification of the key TMEM genes was performed through three machine learning algorithms: LASSO, SVM-RFE and RF-SRC. Then, we established TMEM gene riskscore and estimate its implication in predicting survival and response to systematic therapy. Additionally, we explored the difference and impact of TMEM gene expression in PDAC through immunohistochemistry and cell line research.

Results

5 key TMEM genes (ANO1, TMEM59, TMEM204, TMEM205, TMEM92) were selected based on the single-cell analysis and machine learning survival outcomes. Patients stratified into the high and low-risk groups based on TMEM riskscore, were observed with distinct overall survival in internal and external datasets. Moreover, through bulk RNA-sequence and immunohistochemical staining we verified the protein expression of TMEM genes in PDAC and revealed TMEM92 as an essential regulator of pancreatic cancer cell proliferation, migration, and invasion.

Conclusion

Our study on TMEM gene expression and behavior in PDAC has revealed unique characteristics, offering potential for precise therapeutic approaches. Insights into molecular mechanisms expand understanding of PDAC complexity and TMEM gene roles. Such knowledge may inform targeted therapy development, benefiting patients.

CEP55: an immune-related predictive and prognostic molecular biomarker for multiple cancers

BACKGROUND

Centrosomal protein 55 (CEP55) plays a significant role in specific cancers. However, comprehensive research on CEP55 is lacking in pan-cancer.

METHODS

In-house and multi-center samples (n = 15,823) were used to analyze CEP55 in 33 cancers. The variance of CEP55 expression levels among tumor and control groups was evaluated by the Wilcoxon rank-sum test and standardized mean difference (SMD). The clinical value of CEP55 in cancers was assessed using receiver operating characteristic (ROC) curves, Cox regression analysis, and Kaplan-Meier curves. The correlations between CEP55 expression and the immune microenvironment were explored using Spearman's correlation coefficient.

RESULTS

The data of clustered regularly interspaced short palindromic repeats confirmed that CEP55 was essential for the survival of cancer cells in multiple cancer types. Elevated CEP55 mRNA expression was observed in 20 cancers, including glioblastoma multiforme (p < 0.05). CEP55 mRNA expression made it feasible to distinguish 21 cancer types between cancer specimens and their control samples (AUC = 0.97), indicating the potential of CEP55 for predicting cancer status. Overexpression of CEP55 was correlated with the prognosis of cancer individuals for 18 cancer types, exhibiting its prognostic value. CEP55 expression was relevant to tumor mutation burden, microsatellite instability, neoantigen counts, and the immune microenvironment in various cancers (p < 0.05). The expression level and clinical relevance of CEP55 in cancers were verified in lung squamous cell carcinoma using in-house and multi-center samples (SMD = 4.07; AUC > 0.95; p < 0.05).

CONCLUSION

CEP55 may be an immune-related predictive and prognostic marker for multiple cancers, including lung squamous cell carcinoma.

Golgi-apparatus genes related signature for predicting the progression-free interval of patients with papillary thyroid carcinoma

BACKGROUND

We aimed to build a novel model with golgi apparatus related genes (GaGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).

METHODS

We performed a bioinformatic analysis of integrated PTC datasets with the GaGs to identify differentially expressed GaGs (DE-GaGs). Then we generated PFI-related DE-GaGs and established a novel GaGs based signature. After that, we validated the signature on multiple external datasets and PTC cell lines. Further, we conducted uni- and multivariate analyses to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC.

RESULTS

We identified 260 DE-GaGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic glycoprotein biosynthetic process. Consequently, we established and optimized a novel 11 gene signature that could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.78, and the relevant nomogram had a C-index of 0.79. Also, it was closely related to the pivotal clinical characters of and anaplastic potential in datasets and PTC cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC's PFI.

CONCLUSION

The GaGs signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty

BACKGROUND

In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery.

METHODS

The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort.

RESULTS

The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116.

CONCLUSION

In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study

BACKGROUND

A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the treatment of pancreatic cancer.

METHODS

In this study, GTEx dataset and TCGA dataset were merged together for differential gene expression analysis. Univariate Cox regression and Lasso regression were used to screen variables in the TCGA dataset. Screening the optimal prognostic assessment model is then performed by gaussian finite mixture model. Receiver operating characteristic (ROC) curves were used as an indicator to assess the predictive ability of the prognostic model, the validation process was performed on the GEO datasets.

RESULTS

Gaussian finite mixture model was then used to build 5-gene signature (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3). Receiver operating characteristic (ROC) curves suggested the 5-gene signature performed well on both the training and validation datasets.

CONCLUSIONS

This 5-gene signature performed well on both our chosen training dataset and validation dataset and provided a new way to predict the prognosis of pancreatic cancer patients.

A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer

Background

Gamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence, we aimed to develop a reliable gene signature based on GABA pathways-related genes (GRGs) to predict the survival prognosis of breast cancer patients.

Methods

GABA-related gene sets were acquired from the MSigDB database, while mRNA gene expression profiles and corresponding clinical data of breast cancer patients were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox regression analysis was used to identify prognostic-associated GRGs. Subsequently, LASSO analysis was applied to establish a risk score model. We also constructed a clinical nomogram to perform the survival evaluation. Besides, ESTIMATE and ssGSEA algorithms were used to assess the immune cell infiltration among the risk score subgroups.

Results

A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort. The risk score was significantly related to the overall survival of breast cancer patients, which could predict the survival prognosis of breast cancer patients independently of other clinical features. Breast cancer patients in the low-risk score group exhibited higher immune cell infiltration levels.

Conclusion

A novel prognostic model containing five GRGs could accurately predict the survival prognosis and immune infiltration of breast cancer patients. Our findings provided a novel insight into investigating the immunoregulation roles of GRGs.

Integrated analysis identifies oxidative stress-related lncRNAs associated with progression and prognosis in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common cancers in the world. Oxidative stress reactions have been reportedly associated with oncogenesis and tumor progression. By analyzing mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA), we aimed to construct an oxidative stress-related long noncoding RNA (lncRNA) risk model and identify oxidative stress-related biomarkers to improve the prognosis and treatment of CRC.

RESULTS

Differentially expressed oxidative stress-related genes (DEOSGs) and oxidative stress-related lncRNAs were identified by using bioinformatics tools. An oxidative stress-related lncRNA risk model was constructed based on 9 lncRNAs (AC034213.1, AC008124.1, LINC01836, USP30-AS1, AP003555.1, AC083906.3, AC008494.3, AC009549.1, and AP006621.3) by least absolute shrinkage and selection operator (LASSO) analysis. The patients were then divided into high- and low-risk groups based on the median risk score. The high-risk group had a significantly worse overall survival (OS) (p < 0.001). Receiver operating characteristic (ROC) and calibration curves displayed the favorable predictive performance of the risk model. The nomogram successfully quantified the contribution of each metric to survival, and the concordance index and calibration plots demonstrated its excellent predictive capacity. Notably, different risk subgroups showed significant differences in terms of their metabolic activity, mutation landscape, immune microenvironment and drug sensitivity. Specifically, differences in the immune microenvironment implied that CRC patients in certain subgroups might be more responsive to immune checkpoint inhibitors.

CONCLUSIONS

Oxidative stress-related lncRNAs can predict the prognosis of CRC patients, which provides new insight for future immunotherapies based on potential oxidative stress targets.

High expression of LOC541471, GDAP1, SOD1, and STK25 is associated with poor overall survival of patients with acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is an aggressive heterogeneous hematological malignancy with remarkably heterogeneous outcomes. This study aimed to identify potential biomarkers for AML risk stratification via analysis of gene expression profiles.

METHODS

RNA sequencing data from 167 adult AML patients in the Cancer Genome Atlas (TCGA) database were obtained for overall survival (OS) analysis, and 52 bone marrow (BM) samples from our clinical center were used for validation. Additionally, siRNA was used to investigate the role of prognostic genes in the apoptosis and proliferation of AML cells.

RESULTS

Co-expression of 103 long non-coding RNAs (lncRNAs) and mRNAs in the red module that were positively correlated with European Leukemia Network (ELN) risk stratification and age was identified by weighted gene co-expression network analysis (WGCNA). After screening by uni- and multivariate Cox regression, Kaplan-Meier survival, and protein-protein interaction analysis, four genes including the lncRNA LOC541471, GDAP1, SOD1, and STK25 were incorporated into calculating a risk score from coefficients of the multivariate Cox regression model. Notably, GDAP1 expression was the greatest contributor to OS among the four genes. Interestingly, the risk score, ELN risk stratification, and age were independent prognostic factors for AML patients, and a nomogram model constructed with these factors could illustrate and personalize the 1-, 3-, and 5-year OS rates of AML patients. The calibration and time-dependent receiver operating characteristic curves (ROCs) suggested that the nomogram had a good predictive performance. Furthermore, new risk stratification was developed for AML patients based on the nomogram model. Importantly, knockdown of LOC541471, GDPA1, SOD1, or STK25 promoted apoptosis and inhibited the proliferation of THP-1 cells compared to controls.

CONCLUSIONS

High expression of LOC541471, GDAP1, SOD1, and STK25 may be biomarkers for risk stratification of AML patients, which may provide novel insight into evaluating prognosis, monitoring progression, and designing combinational targeted therapies.

Dysfunctional epigenetic protein-coding gene-related signature is associated with the prognosis of pancreatic cancer based on histone modification and transcriptome analysis

Emerging evidence suggests that epigenetic alterations are responsible for the oncogenesis and progression of cancer. However, the role of epigenetic reprogramming in pancreatic cancer is still not clear. In this study, we used the limma R package to identify differentially expressed protein-coding genes (PCGs) between pancreatic cancer tissues and normal control tissues. The cell-type identification by the estimating relative subsets of RNA transcripts (CIBERSORT) package was used to quantify relative cell fractions in tumors. Prognostic molecular clusters were constructed using ConsensusClusterPlus analysis. Furthermore, the least absolute shrinkage and selection operator and stepAIC methods were used to construct a risk model. We identified 2351 differentially expressed PCGs between pancreatic cancer and normal control tissues in The cancer genome atlas dataset. Combined with histone modification data, we identified 363 epigenetic PCGs (epi-PCGs) and 19,010 non-epi-PCGs. Based on the epi-PCGs, we constructed three molecular clusters characterized by different expression levels of chemokines and immune checkpoint genes and distinct abundances of various immune cells. Furthermore, we generated a 9-gene model based on dysfunctional epi-PCGs. Additionally, we found that patients with high risk scores showed poorer prognoses than patients with low risk scores (p < 0.0001). Further analysis showed that the risk score was significantly related to survival and was an independent risk factor for pancreatic cancer patients. In conclusion, we constructed a 9-gene prognostic risk model based on epi-PCGs that might serve as an effective classifier to predict overall survival and the response to immunotherapy in pancreatic cancer patients.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Establishment of a prognostic prediction system based on tumor microenvironment of pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is an inflammatory tumor. Tumor microenvironment (TME) plays an important role in the development of PC. This study aims to explore hub genes of TME and establish a prognostic prediction system for PC.

METHODS

High throughput RNA-sequencing and clinical data of PC were downloaded from The Cancer Genome Atlas and International Cancer Genome Consortium database, respectively. PC patients were divided into high- and low-score group by using stromal, immune scores system based on ESTIMATE. Differentially expressed genes between high- and low-score patients were screened and survival-related differentially expressed genes were identified as candidate genes by univariate Cox regression analysis. Final variables for establishment of the prognostic prediction system were determined by LASSO analysis and multivariate Cox regression analysis. The predictive power of the prognostic system was evaluated by internal and external validation.

RESULTS

A total of 210 candidate genes were identified by stromal, immune scores system, and survival analyses. Finally, the prognostic risk score system was constructed by the following genes: FAM57B, HTRA3, CXCL10, GABRP, SPRR1B, FAM83A, and LY6D. In process of internal validation, Harrell concordance index (C-index) of this prognostic risk score system was 0.73, and the area under the receiver operating characteristic curve value of 1-year, 2-year, and 3-year overall survival period was 0.67, 0.76 and 0.86, respectively. In the external validation set, the survival prediction C-index was 0.71, and the area under the curve was 0.81, 0.72, and 0.78 at 1-year, 2-year, and 3-year, respectively.

CONCLUSION

This prognostic risk score system based on TME demonstrated a good predictive capacity to the prognosis of PC. It may provide information for the treatment strategy and follow-up for patients with PC.

A Genome-Wide Association Study of 2304 Extreme Longevity Cases Identifies Novel Longevity Variants

We performed a genome-wide association study (GWAS) of human extreme longevity (EL), defined as surviving past the 99th survival percentile, by aggregating data from four centenarian studies. The combined data included 2304 EL cases and 5879 controls. The analysis identified a locus in CDKN2B-AS1 (rs6475609, p = 7.13 × 10^-8) that almost reached genome-wide significance and four additional loci that were suggestively significant. Among these, a novel rare variant (rs145265196) on chromosome 11 had much higher longevity allele frequencies in cases of Ashkenazi Jewish and Southern Italian ancestry compared to cases of other European ancestries. We also correlated EL-associated SNPs with serum proteins to link our findings to potential biological mechanisms that may be related to EL and are under genetic regulation. The findings from the proteomic analyses suggested that longevity-promoting alleles of significant genetic variants either provided EL cases with more youthful molecular profiles compared to controls or provided some form of protection from other illnesses, such as Alzheimer's disease, and disease progressions.

A Robust Prognostic Signature of Tumor Microenvironment in Colorectal Cancer

Background: Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study aimed to develop a robust prognostic signature for CRC patients based on TME-related genes. Materials and Methods: Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. The authors used the prognostic genes to construct a robust prognostic model using the least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by a microenvironment cell populations-counter method. Results: Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. Conclusion: The authors established a robust prognostic signature of 25 TME-related genes which may be an effective tool for prognostic prediction and CRC patient stratification to assist in making treatment decisions.

Identification of a novel cell cycle-related risk signature predicting prognosis in patients with pancreatic adenocarcinoma

BACKGROUND

Growing evidence have indicated that cell cycle-related genes (CRGs) play an essential role in the progression of pancreatic adenocarcinoma (PAAD). Nevertheless, the application of CRGs in estimating the prognosis of PAAD patients is still lacking. This study aimed to establish a risk signature based on CRGs that can predict patients' overall survival for PAAD.

METHODS

The expression and corresponding clinical data of PAAD patients from The Cancer Genome Atlas database and 200 cell cycle-related genes from the MSigDB were used for the generation and validation of the signature. LASSO Cox regression was applied to build the prediction model. The diagnostic value of signature was evaluated by receiver operating characteristic curves. Univariate and multivariate regression was used to construct the nomogram providing the clinicians a useful tool.

RESULTS

A total of 103 CRGs were identified. Seven genes (RBM14, SMAD3, CENPA, KIF23, NUSAP1, INCENP, SMC4) with non-zero coefficients in LASSO analysis were used to construct the prognostic signature. The 7-gene signature significantly stratified patients into high- and low-risk groups in terms of overall survival, and the area under the receiver operating characteristic curve of 5-year survival reached 0.749. Multivariate analysis showed that the signature is an independent prognostic factor. We then mapped a nomogram to predict 1-, 3-, and 5-year survival for PAAD patients. The calibration curves indicated that the model was reliable. Finally, we discovered that TP53 and KRAS mutated most frequently in low and high-risk groups, respectively.

CONCLUSION

Our findings suggested that the seven genes identified in this study are valuable prognostic predictors for patients with PAAD. These findings provided us with a novel insight that it is useful for understanding cell cycle mechanisms and for identifying patients with PAAD with poor prognosis.

Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Alterations in DNA repair-related genes (DRGs) are observed in a variety of cancers and have been shown to affect the development and treatment of cancers. The aim of this study was to develop a DRG-related signature for predicting prognosis and therapeutic response in PAAD.

Methods

We constructed a DRG signature using least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCGA training set. GEO datasets were used as the validation set. A predictive nomogram was constructed based on multivariate Cox regression. Calibration curve and decision curve analysis (DCA) were applied to validate the performance of the nomogram. The CIBERSORT and ssGSEA algorithms were utilized to explore the relationship between the prognostic signature and immune cell infiltration. The pRRophetic algorithm was used to estimate sensitivity to chemotherapeutic agents. The CellMiner database and PAAD cell lines were used to investigate the relationship between DRG expression and therapeutic response.

Results

We developed a DRG signature consisting of three DRGs (RECQL, POLQ, and RAD17) that can predict prognosis in PAAD patients. A prognostic nomogram combining the risk score and clinical factors was developed for prognostic prediction. The DCA curve and the calibration curve demonstrated that the nomogram has a higher net benefit than the risk score and TNM staging system. Immune infiltration analysis demonstrated that the risk score was positively correlated with the proportions of activated NK cells and monocytes. Drug sensitivity analysis indicated that the signature has potential predictive value for chemotherapy. Analyses utilizing the CellMiner database showed that RAD17 expression is correlated with oxaliplatin. The dynamic changes in three DRGs in response to oxaliplatin were examined by RT-qPCR, and the results show that RAD17 is upregulated in response to oxaliplatin in PAAD cell lines.

Conclusion

We constructed and validated a novel DRG signature for prediction of the prognosis and drug sensitivity of patients with PAAD. Our study provides a theoretical basis for further unraveling the molecular pathogenesis of PAAD and helps clinicians tailor systemic therapies within the framework of individualized treatment.

SMAD2/3 mediate oncogenic effects of TGF-β in the absence of SMAD4

TGF-β signaling is involved in pancreatic ductal adenocarcinoma (PDAC) tumorigenesis, representing one of the four major pathways genetically altered in 100% of PDAC cases. TGF-β exerts complex and pleiotropic effects in cancers, notably via the activation of SMAD pathways, predominantly SMAD2/3/4. Though SMAD2 and 3 are rarely mutated in cancers, SMAD4 is lost in about 50% of PDAC, and the role of SMAD2/3 in a SMAD4-null context remains understudied. We herein provide evidence of a SMAD2/3 oncogenic effect in response to TGF-β1 in SMAD4-null human PDAC cancer cells. We report that inactivation of SMAD2/3 in SMAD4-negative PDAC cells compromises TGF-β-driven collective migration mediated by FAK and Rho/Rac signaling. Moreover, RNA-sequencing analyses highlight a TGF-β gene signature related to aggressiveness mediated by SMAD2/3 in the absence of SMAD4. Using a PDAC patient cohort, we reveal that SMAD4-negative tumors with high levels of phospho-SMAD2 are more aggressive and have a poorer prognosis. Thus, loss of SMAD4 tumor suppressive activity in PDAC leads to an oncogenic gain-of-function of SMAD2/3, and to the onset of associated deleterious effects.

In pancreatic ductal adenocarcinoma cells and patient tissue, SMAD2/3 is shown to mediate oncogenic effects of TGF-β in the absence of SMAD4.

Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.

Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.

Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.

Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

Identification and Validation of Three-Gene Signature in Lung Squamous Cell Carcinoma by Integrated Transcriptome and Methylation Analysis

Since DNA methylation (DNAm) is associated with the carcinogenesis of various cancers, this study aimed to explore potential DNAm prognostic signatures of lung squamous cell carcinoma (LUSC). First, transcriptomic and methylation profiles of LUSC were obtained from The Cancer Genome Atlas database (TCGA). DNAm-related genes were screened by integrating DNAm and transcriptome profiles via MethylMix package. Subsequently, a prognostic signature was conducted with the least absolute shrinkage and selector operation (LASSO) Cox analysis. This signature combined with the clinicopathological parameters was then utilized to construct a prognostic nomogram via the rms package. A signature based on three DNAm-related genes claudin 1 (CLDN1), ATP-binding cassette subfamily C member 5 (ABCC5), and cystatin A (CSTA) that were hypomethylated and upregulated in LUSC was constructed. Univariate and multivariate Cox regression analysis suggested that this signature, combined with age and TNM.N stage, was significantly correlated with survival rate. Time-dependent receiver operating characteristics and calibration curves suggested the nomogram constructed with age and TNM.N stage variables could accurately evaluate the 3- and 5-year outcome of LUSC. Finally, the average mRNA and protein expression levels of CLDN1, ABCC5, and CSTA in LUSC were verified to be significantly higher than those in paracancerous tissues. Moreover, silencing CLDN1, ABCC5, and CSTA expressions could significantly reduce the carcinogenesis of the A549 cell line. The DNAm-driven prognostic signature consists of CLDN1, ABCC5, and CSTA incorporated with age and TNM. N stage could facilitate the prediction outcome of LUSC.

The prognostic and immune infiltration role of ITGB superfamily members in non-small cell lung cancer

PURPOSE

We aimed to explore the prognostic value of integrin-β superfamily members (ITGBs) and their role in immune cell infiltration in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

Study cases were acquired from The Cancer Genome Atlas database and The Human Protein Atlas. We then used R package and several online tools to analyze and visualize the roles of ITGBs in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

RESULTS

We found that ITGBs were differentially expressed in NSCLC. In LUAD, high expression of ITGB1 and ITGB4 was an independent risk factor for poor prognosis, and ITGB7 was an independent protective factor for overall survival; in LUSC, high expression of ITGB1, 3, 5, and 6 was associated with poor prognosis, and ITGB8 was an independent protective factor for disease-specific survival. Protein-protein interaction networks for the most associated co-expressed genes revealed the following target genes of ITGBs: PTPRC, ITGAM, and ITGB2 in LUAD and FN1, PTPRC, and ITGB2 in LUSC. Gene ontology analysis revealed that functions related to adhesion, junction, and binding were highly enriched in LUAD and LUSC. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes in LUAD and LUSC.

CONCLUSION

ITGBs were differentially expressed in NSCLC. ITGB1, 4, and 7 and ITGB1, 3, 5, 6, and 8 were found as prognostic markers in LUAD and LUSC, respectively. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes.

Identification of a lncRNA based signature for pancreatic cancer survival to predict immune landscape and potential therapeutic drugs

Pancreatic cancer is one major digestive malignancy with a poor prognosis. Given the clinical importance of lncRNAs, developing a novel molecular panel with lncRNAs for pancreatic cancer has great potential. As a result, an 8-lncRNA-based robust prognostic signature was constructed using a random survival forest model after examing the expression profile and prognostic significance of lncRNAs in the PAAD cohort from TCGA. The efficacy and effectiveness of the lncRNA-based signature were thoroughly assessed. Patients with high- and low-risk defined by the signature underwent significantly distinct OS expectancy. Most crucially the training group’s AUCs of ROC approached 0.90 and the testing group similarly had the AUCs above 0.86. The lncRNA-based signature was shown to behave as a prognostic indicator of pancreatic cancer, either alone or simultaneously with other factors, after combined analysis with other clinical-pathological factors in Cox regression and nomogram. Additionally, using GSEA and CIBERSORT scoring methods, the immune landscape and variations in biological processes between high- and low-risk subgroups were investigated. Last but not least, drug databases were searched for prospective therapeutic molecules targeting high-risk patients. The most promising compound were Afatinib, LY-303511, and RO-90-7501 as a result. In conclusion, we developed a novel lncRNA based prognostic signature with high efficacy to stratify high-risk pancreatic cancer patients and screened prospective responsive drugs for targeting strategy.

Identification of cervical squamous cell carcinoma feature genes and construction of a prognostic model based on immune-related features

As heterogeneity of cervical squamous cell carcinoma (CSCC), prognosis assessment for CSCC patients remain challenging. To develop novel prognostic strategies for CSCC patients, associated biomarkers are urgently needed. This study aimed to cluster CSCC samples from a molecular perspective. CSCC expression data sets were obtained from The Cancer Genome Atlas and based on the accessed expression profile, a co-expression network was constructed with weighted gene co-expression network analysis to form different gene modules. Tumor microenvironment was evaluated using ESTIMATE algorithm, observing that the brown module was highly associated with tumor immunity. CSCC samples were clustered into three subtypes by consensus clustering based on gene expression profiles in the module. Gene set variation analysis showed differences in immune-related pathways among the three subtypes. CIBERSORT and single-sample gene set enrichment analysis analyses showed the difference in immune cell infiltration among subtype groups. Also, Human leukocyte antigen protein expression varied considerably among subtypes. Subsequently, univariate, Lasso and multivariate Cox regression analyses were performed on the genes in the brown module and an 8-gene prognostic model was constructed. Kaplan-Meier analysis illuminated that the low-risk group manifested a favorable prognosis, and receiver operating characteristic curve showed that the model has good predictive performance. qRT-PCR was used to examine the expression status of the prognosis-associated genes. In conclusion, this study identified three types of CSCC from a molecular perspective and established an effective prognostic model for CSCC, which will provide guidance for clinical subtype identification of CSCC and treatment of patients.

Identification and validation of a ferroptosis-related gene signature for predicting survival in skin cutaneous melanoma

PURPOSE

Ferroptosis plays a crucial role in the initiation and progression of melanoma. This study developed a robust signature with ferroptosis-related genes (FRGs) and assessed the ability of this signature to predict OS in patients with skin cutaneous melanoma (SKCM).

METHODS

RNA-sequencing data and clinical information of melanoma patients were extracted from TCGA, GEO, and GTEx. Univariate, multivariate, and LASSO regression analyses were conducted to identify the gene signature. A 10 FRG signature was an independent and strong predictor of survival. The predictive performance was assessed using ROC curve. The functions of this gene signature were assessed by GO and KEGG analysis. The statuses of low-risk and high-risk groups according to the gene signature were compared by GSEA. In addition, we investigated the possible relationship of FRGs with immunotherapy efficacy.

RESULTS

A prognostic signature with 10 FRGs (CYBB, IFNG, FBXW7, ARNTL, PROM2, GPX2, JDP2, SLC7A5, TUBE1, and HAMP) was identified by Cox regression analysis. This signature had a higher prediction efficiency than clinicopathological features (AUC = 0.70). The enrichment analyses of DEGs indicated that ferroptosis-related immune pathways were largely enriched. Furthermore, GSEA showed that ferroptosis was associated with immunosuppression in the high-risk group. Finally, immune checkpoints such as PDCD-1 (PD-1), CTLA4, CD274 (PD-L1), and LAG3 were also differential expression in two risk groups.

CONCLUSIONS

The 10 FRGs signature were a strong predictor of OS in SKCM and could be used to predict therapeutic targets for melanoma.

TRPML3 enhances drug resistance in non-small cell lung cancer cells by promoting Ca2+-mediated lysosomal trafficking

Lysosomes are emerging as versatile signaling hubs that mediate numerous cellular processes, including the development of drug resistance in cancer cells. Transient receptor potential mucolipin 3 (TRPML3), an endolysosomal Ca²⁺-permeable channel, is implicated in regulating lysosomal trafficking during endocytosis and autophagy. However, the role of TRPML3 in cancer progression remains unclear. In this study, we focused on identifying key molecules that modulate exosomal release triggered by lysosomal exocytosis during the development of gefitinib resistance in non-small cell lung cancer (NSCLC). We found that the basal release of exosomes and lysosomal exocytosis is higher in the gefitinib-resistant NSCLC cell line HCC827/GR than in the gefitinib-sensitive NSCLC cell line HCC827. Notably, exosomal release and lysosomal exocytosis were associated with an increase in TRPML3 expression. Lysosomal Ca²⁺ release via TRPML3 was triggered by the gefitinib-mediated elevation of lysosomal pH. Furthermore, TRPML3 deficiency enhanced the gefitinib-mediated increase in sub-G₀ cell population, reduction of cell proliferation, and poly (ADP-ribose) polymerase cleavage. These data demonstrated that TRPML3 is a promising modulator of drug resistance. By sensing the elevation of lysosomal pH, it mediates lysosomal Ca²⁺ release, lysosomal trafficking and exocytosis, and exosomal release. Taken together, our study is the first to report the autonomous defense mechanism developed in NSCLC cells against the small-molecule tyrosine kinase inhibitor gefitinib, leading to acquired drug resistance.