A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer

Bioinformatic Analyses and Integrated Machine Learning to Predict prognosis and therapeutic response Based on E3 Ligase-Related Genes in colon cancer

Purpose: Colorectal cancer is the third most common cause of cancer death worldwide. We probed the correlations between E3 ubiquitin ligase (E3)-related genes (ERGs) and colon cancer prognosis and immune responses. Methods: Gene expression profiles and clinical data of patients with colon cancer were acquired from the TCGA, GTEx, GSE17537 and GSE29621 databases. ERGs were identified by coexpression analysis. WGCNA and differential expression analysis were subsequently conducted. Consensus clustering identified two molecular clusters. Differential analysis of the two clusters and Cox regression were then conducted. A prognostic model was constructed based on 10 machine learning algorithms and 92 algorithm combinations. The CIBERSORT, ssGSEA and TIMER algorithms were used to estimate immune infiltration. The OncoPredict algorithm and The Cancer Immunome Atlas (TCIA) predicted susceptibility to chemotherapeutic and targeted drugs and immunotherapy sensitivity. CCK-8, scratch-wound and RT‒PCR assays were subsequently conducted. Results: Two ERG-associated clusters were identified. The prognosis and immune function of patients in cluster A were superior to those of patients in cluster B. We constructed a prognostic model with perfect predictive capability and validated it in internal and external colon cancer datasets. We discovered significant discrepancies in immune infiltration and immune checkpoints between different risk groups. The group with high-risk had a reduced half-maximal inhibitory concentration (IC50) for some routine antitumor drugs and reduced susceptibility to immunotherapy. In vitro experiments demonstrated that the ectopic expression of PRELP inhibited the migration and proliferation of CRC cells. Conclusions: In summary, we identified novel molecular subtypes and developed a prognostic model, which will help a lot in the advancement of better forecasting and therapeutic approaches.

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.

A narrative review on therapeutic potential of naringenin in colorectal cancer: Focusing on molecular and biochemical processes

Colorectal cancer (CRC) is a common and highly metastatic cancer affecting people worldwide. Drug resistance and unwanted side effects are some of the limitations of current treatments for CRC. Naringenin (NAR) is a naturally occurring compound found in abundance in various citrus fruits such as oranges, grapefruits, and tomatoes. It possesses a diverse range of pharmacological and biological properties that are beneficial for human health. Numerous studies have highlighted its antioxidant, anticancer, and anti-inflammatory activities, making it a subject of interest in scientific research. This review provides a comprehensive overview of the effects of NAR on CRC. The study's findings indicated that NAR: (1) interacts with estrogen receptors, (2) regulates the expression of genes related to the p53 signaling pathway, (3) promotes apoptosis by increasing the expression of proapoptotic genes (Bax, caspase9, and p53) and downregulation of the antiapoptotic gene Bcl2, (4) inhibits the activity of enzymes involved in cell survival and proliferation, (5) decreases cyclin D1 levels, (6) reduces the expression of cyclin-dependent kinases (Cdk4, Cdk6, and Cdk7) and antiapoptotic genes (Bcl2, x-IAP, and c-IAP-2) in CRC cells. In vitro CDK2 binding assay was also performed, showing that the NAR derivatives had better inhibitory activities on CDK2 than NAR. Based on the findings of this study, NAR is a potential therapeutic agent for CRC. Additional pharmacology and pharmacokinetics studies are required to fully elucidate the mechanisms of action of NAR and establish the most suitable dose for subsequent clinical investigations.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence

Background

This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.

Methods

Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted.

Results

In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group.

Conclusions

The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

CD72, a new immune checkpoint molecule, is a novel prognostic biomarker for kidney renal clear cell carcinoma

BACKGROUND

The incidence and mortality of clear cell carcinoma of the kidney increases yearly. There are limited screening methods and advances in treating kidney renal clear cell carcinoma (KIRC). It is important to find new biomarkers to screen, diagnose and predict the prognosis of KIRC. Some studies have shown that CD72 influences the development and progression of colorectal cancer, nasopharyngeal cancer, and acute lymphoid leukemia. However, there is a lack of research on the role of CD72 in the pathogenesis of KIRC. This study aimed to determine whether CD72 is associated with the prognosis and immune infiltration of KIRC, providing an essential molecular basis for the early non-invasive diagnosis and immunotherapy of KIRC.

METHODS

Using TCGA, GTE, GEO, and ImmPort databases, we obtained the differentially expressed mRNA (DEmRNA) associated with the prognosis and immunity of KIRC patients. We used the Kruskal-Wallis test to identify clinicopathological parameters associated with target gene expression. We performed univariate and multivariate COX regression analyses to determine the effect of target gene expression and clinicopathological parameters on survival. We analyzed the target genes' relevant functions and signaling pathways through enrichment analysis. Finally, the correlation of target genes with tumor immune infiltration was explored by ssGSEA and Spearman correlation analysis.

RESULTS

The results revealed that patients with KIRC with higher expression of CD72 have a poorer prognosis. CD72 was associated with the Pathologic T stage, Pathologic stage, Pathologic M stage, Pathologic N stage, Histologic grade in KIRC patients, Laterality, and OS event. It was an independent predictor of the overall survival of KIRC patients. Functional enrichment analysis showed that CD72 was significantly enriched in oncogenic and immune-related pathways. According to ssGSEA and Spearman correlation analysis, CD72 expression was significantly associated with tumor immune cells and immune checkpoints.

CONCLUSION

Our study suggests that CD72 is associated with tumor immunity and may be a biomarker relevant to the diagnosis and prognosis of KIRC patients.

Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers

Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.

A meta-analysis of tissue microbial biomarkers for recurrence and metastasis in multiple cancer types

Background. Local recurrence and distant metastasis are the main causes of death in patients with cancer. Only considering species abundance changes when identifying markers of recurrence and metastasis in patients hinders finding solutions.Hypothesis. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.Aim. This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.Method. One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3 years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3 years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).Results. Tumour tissue bacterial composition differed significantly among 11 cancers. Among them, the tissue microbiome of four cancers, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), showed relatively good performance in predicting recurrence and metastasis in patients, with areas under the receiver operating characteristic curve (AUCs) of 0.78, 0.74, 0.91 and 0.93, respectively. Considering both species abundance changes and microbial interactions for the four cancers, a combination of nine genera (Niastella, Schlesneria, Thioalkalivibrio, Phaeobacter, Sphaerotilus, Thiomonas, Lawsonia, Actinobacillus and Spiroplasma) performed best in predicting patient survival.Conclusion. Taken together, our results imply that comprehensive consideration of microbial abundance changes and microbial interactions is helpful for mining bacterial markers that carry prognostic information.

Establishment and validation of a ferroptosis-related signature predicting prognosis and immunotherapy effect in colon cancer

Background

Ferroptosis, a novel form of regulating cell death, is related to various cancers. However, the role of ferroptosis-related genes (FRGs) on the occurrence and development of colon cancer (CC) needs to be further elucidated.

Method

CC transcriptomic and clinical data were downloaded from TCGA and GEO databases. The FRGs were obtained from the FerrDb database. The consensus clustering was performed to identify the best clusters. Then, the entire cohort was randomly divided into the training and testing cohorts. Univariate Cox, LASSO regression and multivariate Cox analyses were used to construct a novel risk model in training cohort. The testing and the merged cohorts were performed to validate the model. Moreover, CIBERSORT algorithm analyze TIME between high- and low- risk groups. The immunotherapy effect was evaluated by analyzing the TIDE score and IPS between high- and low- risk groups. Lastly, RT-qPCR were performed to analyze the expression of the three prognostic genes, and the 2-years OS and DFS between the high- and low- risk groups of 43 clinical CC samples to further validate the value of the risk model.

Results

SLC2A3, CDKN2A, and FABP4 were identified to construct a prognostic signature. Kaplan-Meier survival curves showed that OS between the high- and low-risk groups were statistically significant (p_merged<0.001, p_training<0.001, p_testing<0.001). TIDE score and IPS were higher in the high-risk group (p_TIDE<0.005, p_Dysfunction<0.005, p_Exclusion<0.001, p_mAb-CTLA-4 = 3e-08, p_mAb-PD-1 = 4.1e-10). The clinical samples were divided into high- and low- risk groups according to the risk score. There was a statistical difference in DFS (p=0.0108).

Conclusion

This study established a novel prognostic signature and provided more insight into the immunotherapy effect of CC.

Whole Exome Sequencing Identified Two Single Nucleotide Polymorphisms of Human Leukocyte Antigen-DRB5 in Familial Sarcoidosis in China

BACKGROUND

Sarcoidosis is a multisystem granulomatous disorder whose etiology is related to genetic and immunological factors. Familial aggregation and ethnic prevalence suggest a genetic predisposition and inherited susceptibility to sarcoidosis.

OBJECTIVE

This study aimed to identify suspected risk loci for familial sarcoidosis patients.

METHODS

We conducted whole exome sequencing on two sarcoidosis patients and five healthy family members in a Chinese family for a case-control study. The two sarcoidosis patients were siblings who showed chronic disease.

RESULTS

The Gene Ontology results showed single nucleotide polymorphisms in three genes, including human leukocyte antigen (HLA)-DRB1, HLA-DRB5, and KIR2DL4, associated with both 'antigen processing and presentation' and 'regulation of immune response.' Sanger sequencing verified two nonsynonymous mutations in HLA-DRB5 (rs696318 and rs115817940) located on 6p21.3 in the major histocompatibility complex (MHC) class II beta 1 region. The structural model simulated on Prot- Param protein analysis by the Expert Protein Analysis System predicted that the hydropathy index changed at two mutation sites (rs696318: p.F96L, -1.844 to -1.656 and rs115817940: p.T106N, -0.322 to -0.633), which indicated the probability of changes in peptide-binding selectivity.

CONCLUSION

Our results indicated that two nonsynonymous mutations of HLA-DRB5 have been identified in two sarcoidosis siblings, while their healthy family members do not have the mutations. The two HLA-DRB5 alleles may influence genetic susceptibility and chronic disease progression through peptide mutations on the MHC class II molecule among the two affected family members.

An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD. Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively. Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in "ECM Receptor Interaction," "WNT Signaling Pathway" and "Colorectal Cancer." Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888. Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.

Identification of necroptosis-related genes as prognostic indicators for lower-grade glioma

The purpose of this research is to develop a predictive model based on necroptosis-related genes to predict the prognosis and survival of lower grade gliomas (LGGs) efficiently. To achieve this goal, we searched for differentially expressed necrotizing apoptosis-related genes using the TCGA and CGGA databases. To construct a prognostic model, LASSO Cox and COX regression analyses were conducted on the differentially expressed genes. In this study, three genes were used to develop a prognostic model of necrotizing apoptosis, and all samples were split into high- and low-risk groups. We observed that patients with a high-risk score had a worse overall survival rate (OS) than those with a low-risk score. In the TCGA and CGGA cohorts, the nomogram plot showed a high capacity to predict overall survival of LGG patients. GSEA analysis revealed that the high-risk group was enriched for inflammatory responses, tumor-related pathways, and pathological processes. Additionally, the high-risk score was associated with invading immune cell expression. In conclusion, our predictive model based on necroptosis-related genes in LGG was shown to be effective in the diagnosis and could predict the prognosis of LGG. In addition, we identified possible targets related to necroptosis-related genes for glioma therapy in this study.

A multi-omics machine learning framework in predicting the recurrence and metastasis of patients with pancreatic adenocarcinoma

Local recurrence and distant metastasis are the main causes of death in patients with pancreatic adenocarcinoma (PDAC). Microbial content in PDAC metastasis is still not well-characterized. Here, the tissue microbiome was comprehensively compared between metastatic and non-metastatic PDAC patients. We found that the pancreatic tissue microbiome of metastatic patients was significantly different from that of non-metastatic patients. Further, 10 potential bacterial biomarkers (Kurthia, Gulbenkiania, Acetobacterium and Planctomyces etc.) were identified by differential analysis. Meanwhile, significant differences in expression patterns across multiple omics (lncRNA, miRNA, and mRNA) of PDAC patients were found. The highest accuracy was achieved when these 10 bacterial biomarkers were used as features to predict recurrence or metastasis in PDAC patients, with an AUC of 0.815. Finally, the recurrence and metastasis in PDAC patients were associated with reduced survival and this association was potentially driven by the 10 biomarkers we identified. Our studies highlight the association between the tissue microbiome and recurrence or metastasis of pancreatic adenocarcioma patients, as well as the survival of patients.

MAGEA11 as a STAD Prognostic Biomarker Associated with Immune Infiltration

Expression of MAGE family member A11 (MAGEA11) is upregulated in different tumors. However, in gastric cancer, the prognostic significance of MAGEA11 and its relationship with immune infiltration remain largely unknown. The expression of MAGEA11 in pan-cancer and the receiver operating characteristic (ROC) and survival impact of gastric cancer were evaluated by The Cancer Genome Atlas (TCGA). Whether MAGEA11 was an independent risk factor was assessed by Cox analysis. Nomograms were constructed from MAGEA11 and clinical variables. Gene functional pathway enrichment was obtained based on MAGEA11 differential analysis. The relationship between MAGEA11 and immune infiltration was determined by the Tumor Immunity Estimation Resource (TIMER) and the Tumor Immune System Interaction Database (TISIDB). Finally, MAGEA11-sensitive drugs were predicted based on the CellMiner database. The results showed that the expression of MAGEA11 mRNA in gastric cancer tissues was significantly higher than that in normal tissues. The ROC curve indicated an AUC value of 0.667. Survival analysis showed that patients with high MAGEA11 had poor prognosis (HR = 1.43, p = 0.034). In correlation analysis, MAGEA11 mRNA expression was found to be associated with tumor purity and immune invasion. Finally, drug sensitivity analysis found that the expression of MAGEA11 was correlated with seven drugs. Our study found that upregulated MAGEA11 in gastric cancer was significantly associated with lower survival and invasion by immune infiltration. It is suggested that MAGEA11 may be a potential biomarker and immunotherapy target for gastric cancer.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients

Background

Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease.

Methods

The Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the "MAFTools" r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted.

Results

We identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib.

Conclusion

We successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.

Supplementary benefits of CT-guided transthoracic lung aspiration biopsy for core needle biopsy

Objective

This study aimed to investigate the diagnostic efficacy of computed tomography (CT)-guided transthoracic lung core needle biopsy combined with aspiration biopsy and the clinical value of this combined routine microbial detection.

Materials and methods

We retrospectively collected the electronic medical records, CT images, pathology, and other data of 1085 patients with sequential core needle biopsy and aspiration biopsy of the same lung lesion under CT guidance in the First Affiliated Hospital of Wenzhou Medical University from January 2016 to January 2021. GenXpert MTB/RIF detection and BD BACTEC™ Mycobacterium/fungus culture were applied to identifying the microbiological results of these patients. We then compared the positive diagnostic rate, false negative rate, and diagnostic sensitivity rate of three methods including core needle biopsy alone, aspiration biopsy alone, and both core needle biopsy and aspiration biopsy.

Results

The pathological results of cutting histopathology and aspiration of cell wax were examined for 1085 patients. The diagnostic rates of cutting and aspiration pathology were 90.1% (978/1085) and 86.3% (937/1085), respectively, with no significant difference (P > 0.05). Considering both cutting and aspiration pathologies, the diagnostic rate was significantly improved, up to 98% (1063/1085) (P < 0.001). A total of 803 malignant lesions were finally diagnosed (803/1085, 74.0%). The false negative rate by cutting pathology was 11.8% (95/803), which was significantly lower than that by aspiration biopsy [31.1% (250/803), P < 0.001]. Compared with core needle biopsy alone, the false negative rate of malignant lesions decreased to 5.6% (45/803) (P < 0.05). Next, the aspirates of the malignant lesions highly suspected of corresponding infection were cultured. The results showed that 16 cases (3.1%, 16/511) were infected with Mycobacterium tuberculosis complex, Aspergillus niger, and Acinetobacter baumannii, which required clinical treatment. 803 malignant tumors were excluded and 282 cases of benign lesions were diagnosed, including 232 cases of infectious lesions (82.3%, 232/282). The diagnostic rate of Mycobacterium/fungus culture for infectious lesions by aspiration biopsy (47.4%) was significantly higher than that by lung core needle biopsy (22.8%; P < 0.001). The diagnostic rate of aspiration biopsy combined with core needle biopsy was 56% (130/232). The parallel diagnostic rate of aspirated biopsy for GenXpert detection and Mycobacterium/fungal culture combined with core needle biopsy was 64.7% (150/232), which was significantly higher than that of lung core needle biopsy alone (P < 0.001). Finally, pulmonary tuberculosis was diagnosed in 90 cases (38.8%) of infectious lesions. Compared with the sensitivity of core needle biopsy to detect tuberculosis (27.8%, 25/90), the sensitivity of aspirating biopsy for GenXpert detection and Mycobacterium/fungal culture was significantly higher, at 70% (63/90) and 56.7% (51/90), respectively. Although there was no significant difference in the sensitivity of aspirated biopsy for GenXpert and Mycobacterium/fungal culture to detect pulmonary tuberculosis, the sensitivity was significantly increased to 83.3% (P < 0.05) when the two tests were combined. Moreover, when aspirated biopsies were combined with GenXpert detection, Mycobacterium/fungus culture, and core needle biopsy, the sensitivity was as high as 90% (81/90).

Conclusion

CT-guided lung aspiration biopsy has a significant supplementary effect on core needle biopsies, which is indispensable in clinical application. Additionally, the combination of aspiration biopsy and core needle biopsy can significantly improve the diagnostic rate of benign and malignant lesions. Aspiration biopsy showed that pulmonary malignant lesions are complicated with pulmonary tuberculosis, aspergillus, and other infections. Finally, the diagnostic ability of lung puncture core needle biopsy and aspiration biopsy combined with routine microbial detection under CT positioning in the diagnosis of pulmonary infectious diseases was significantly improved.

Prognostic and immunological role of cuproptosis-related protein FDX1 in pan-cancer

Background: Cancer is the second cause of death worldwide. Copperoptosis is a new mode of regulated cell death and is strongly associated with metabolic pathways. FDX1 is a key gene that promotes copperoptosis, and its impact on tumor pathogenesis and tumor immune response is indistinct and needs further exploration.

Methods: Data was mined from the Cancer Genome Atlas database, the Broad Institute Cancer Cell Line Encyclopedia database, and the International Cancer Genome Consortium. Survival analyses included the Kaplan–Meier method for calculating the cumulative incidence of survival events and the log-rank method for comparing survival curves between groups. Immune cell infiltration levels were calculated using the Spearman correlation test and correlated with FDX1 expression to assess significance. More correlation analyses between FDX1 expression and mutational markers, such as tumor mutational burden (TMB) and microsatellite instability (MSI), were also examined via Spearman assay to explore the relation between FDX1 expression and the sensitivity of common antitumor drugs.

Results: FDX1 expression was downregulated in most kinds of cancers, and this high expression indicated better overall survival and death-specific survival. For several cancer types, FDX1 expression had a positive correlation with immune cell infiltration, and FDX1 also had a positive correlation with TMB and MSI in some cancer types, linking its expression to the assessment of possible treatment responses.

Conclusion: The correlations between FDX1 expression and cancer in varioustissues, including clear links to cancer survival and prognosis, make FDX1 aninteresting biomarker and potential therapeutic target for cancer surveillance and futureresearch.

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.