AIMC Topic: Biomarkers, Tumor

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SPP1 + macrophages facilitate pancreatic cancer progression via ITGB6-mediated interactions: evidence from integrated multi-omics analysis and experimental validation.

Immunologic research
Basement membranes (BMs) and tumor-associated macrophages (TAMs) are crucial stromal components in pancreatic cancer (PC), critically influencing disease progression. Bulk and single-cell RNA-seq (scRNA-seq) data were acquired from publicly available...

Integrated multi-omics analysis and machine learning refine molecular subtypes and prognosis in hepatocellular carcinoma through O-linked glycosylation genes.

Functional & integrative genomics
O-glycosylation significantly influences cellular physiological processes and disease regulation by modulating the structure, function, and stability of proteins. However, there is a notable gap in research focusing on O-glycosylation in relation to ...

Machine learning-based dynamic CEA trajectory and prognosis in gastric cancer.

BMC cancer
BACKGROUND: Static carcinoembryonic antigen (CEA) levels are well‑established prognostic markers in patients with gastric cancer, but the significance of their dynamic trajectories over time has rarely been reported.

Data-Driven Sustainable Campaigns to Decipher Invasive Breast Cancer Features.

ACS biomaterials science & engineering
The intrinsic complexity of biological processes often hides the role of dynamic microenvironmental cues in the development of pathological states. Microphysiological systems (MPSs) are emerging technological platforms that model dynamics of tissue-...

Comparative analysis of the tumor microenvironment in primary CNS and testicular large B-cell lymphomas using digital image analysis and its implications for immunotherapy.

Human pathology
Primary large B-cell lymphomas of immune-privileged sites, including primary central nervous system lymphoma (PCNSL) and primary testicular lymphoma (PTL), exhibit distinct clinicopathologic features contributing to aggressive behavior and immune eva...

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Nature communications
Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remain...

Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.

Scientific reports
The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic corr...

Diagnostic technologies for neuroblastoma.

Lab on a chip
Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an array of techniques, from blood tests to tumour biopsies. This process is associated with long-term ...

An autoencoder learning method for predicting breast cancer subtypes.

PloS one
Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizi...

Circulating cytokine profiling and clustering identify biomarker predicting efficacy of ICI in combination with chemotherapy.

Cancer letters
The combination of chemotherapy can enhance the efficacy of immune checkpoint inhibitors (ICIs), but requires precise patient stratification and biomarker screening. Cytokines influence immunotherapy outcomes, and multiplex cytokine profiling aids in...