MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification.

Background: Immunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis. Method: In this paper, a notable Multiplex IHC Histopathological Image Classification (MIHIC) dataset is created based on manual annotations by pathologists, which is publicly available for exploring deep learning models to quantify variables associated with the TIME in lung cancer. The MIHIC dataset comprises of totally 309,698 multiplex IHC stained histological image patches, encompassing seven distinct tissue types: Alveoli, Immune cells, Necrosis, Stroma, Tumor, Other and Background. By using the MIHIC dataset, we conduct a series of experiments that utilize both convolutional neural networks (CNNs) and transformer models to benchmark IHC stained histological image classifications. We finally quantify lung cancer immune microenvironment variables by using the top-performing model on tissue microarray (TMA) cores, which are subsequently used to predict patients' survival outcomes. Result: Experiments show that transformer models tend to provide slightly better performances than CNN models in histological image classifications, although both types of models provide the highest accuracy of 0.811 on the testing dataset in MIHIC. The automatically quantified TIME variables, which reflect proportions of immune cells over stroma and tumor over tissue core, show prognostic value for overall survival of lung cancer patients. Conclusion: To the best of our knowledge, MIHIC is the first publicly available lung cancer IHC histopathological dataset that includes images with 12 different IHC stains, meticulously annotated by multiple pathologists across 7 distinct categories. This dataset holds significant potential for researchers to explore novel techniques for quantifying the TIME and advancing our understanding of the interactions between the immune system and tumors.

Pan-Cancer Profiling and Digital Pathology Analysis Reveal Negative Prognostic Biomarker ZPR1 Associated with Immune Infiltration and Treatment Response in Hepatocellular Carcinoma.

Purpose: ZPR1 is a zinc finger-containing protein that plays a crucial role in neurodegenerative diseases, lipid metabolism disorders, and non-alcoholic fatty liver disease. However, the expression pattern, prognostic value, and treatment response of ZPR1 in pan-cancer and hepatocellular carcinoma (HCC) remain unclear. Patients and Methods: Pan-cancer expression profiles and relevant clinical data were acquired from UCSC Xena platform. Pan-cancer expression, epigenetic profile, and clinical correlation analysis for ZPR1 were performed. We next explored the prognostic significance and potential biological functions of ZPR1 in HCC. Furthermore, the relationship between ZPR1 and immune infiltration and treatment response was investigated. Finally, quantitative immunohistochemistry (IHC) analysis was applied to assess the correlation of ZPR1 expression and immune microenvironment in HCC tissues using Qupath software. Results: ZPR1 was differentially expressed in most tumor types and significantly up-regulated in HCC. ZPR1 showed hypo-methylated status in most tumors. Pan-cancer correlation analysis indicated that ZPR1 was closely associated with clinicopathological factors and TMB, MSI, and stemness index in HCC. High ZPR1 expression could be an independent risk factor for adverse prognosis in HCC. ZPR1 correlated with immune cell infiltration and therapeutic response. Finally, IHC results suggested that ZPR1 correlated with CD4, CD56, CD68, and PD-L1 expression and is a promising pathological diagnostic marker in HCC. Conclusion: Immune infiltrate-associated ZPR1 could be considered a novel negative prognostic biomarker for therapeutic response in HCC.

Research Progress on Therapeutic Targeting of Cancer-Associated Fibroblasts to Tackle Treatment-Resistant NSCLC.

Non-small cell lung cancer (NSCLC) accounts for most lung cancer cases and is the leading cause of cancer-related deaths worldwide. Treatment options for lung cancer are no longer limited to surgery, radiotherapy, and chemotherapy, as targeted therapy and immunotherapy offer a new hope for patients. However, drug resistance in chemotherapy and targeted therapy, and the low response rates to immunotherapy remain important challenges. Similar to tumor development, drug resistance occurs because of significant effects exerted by the tumor microenvironment (TME) along with cancer cell mutations. Cancer-associated fibroblasts (CAFs) are a key component of the TME and possess multiple functions, including cross-talking with cancer cells, remodeling of the extracellular matrix (ECM), secretion of various cytokines, and promotion of epithelial-mesenchymal transition, which in turn provide support for the growth, invasion, metastasis, and drug resistance of cancer cells. Therefore, CAFs represent valuable therapeutic targets for lung cancer. Herein, we review the latest progress in the use of CAFs as potential targets and mediators of drug resistance for NSCLC treatment. We explored the role of CAFs on the regulation of the TME and surrounding ECM, with particular emphasis on treatment strategies involving combined CAF targeting within the current framework of cancer treatment.

Analysis of the Relationship between the Expression Level of TTR and APOH and Prognosis in Patients with Colorectal Cancer Metastasis Based on Bioinformatics.

The expression of TTR and apolipoprotein H (APOH) genes and their relationship with prognosis in patients with colorectal cancer (CRC) metastasis by using bioinformatics analysis techniques are explored. The expression profiles of related genes in patients with CRC metastasis are retrieved from the Gene Expression Omnibus (GEO) database. The core genes transthyretin (TTR) and APOH are screened by constructing protein-protein interaction (PPI) network, and the corresponding patient data of 327 patients are extracted and included in the metastasis group. The TTR and APOH genes of 300 patients without CRC metastasis are screened and included in the control group. The relationship between the expression levels of TTR and APOH and the clinicopathological parameters of patients with CRC metastasis is analyzed. Kaplan-Meier survival curve is drawn to observe the influence of overexpression and low expression of TTR and APOH on the prognosis and survival of patients in the metastatic group. Receiver operating characteristic (ROC) curve is drawn to observe the prognostic efficacy of combined TTR and APOH detection in patients with CRC metastasis. The experimental results show that bioassay can confirm the close relationship between TTR, APOH, and patients with CRC metastasis. Regular detection of serum TTR and APOH expression can effectively assess the patient's condition and take measures to improve the prognosis of the patients.