Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, durable clinical benefit is limited to a subset of patients. Long non-coding RNAs (lncRNAs) have emerged as important regulators of tumor biology and immune responses and may serve as novel biomarkers to predict prognosis and response to ICIs.

Breast cancer outcomes improve substantially with earlier detection, yet mammography performance can be limited in dense breasts and may lead to false-positive investigations. Circulating microRNAs (miRNAs) and lipidomic/metabolomic signatures have emerged as promising minimally invasive biomarkers that could complement imaging for early-stage detection. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of circulating microRNAs and lipidomic/metabolomic biomarkers for early breast cancer detection and to explore between-study heterogeneity.

Osimertinib has reshaped the treatment of EGFR-mutated non-small cell lung cancer (NSCLC), but its high cost raises persistent questions about value for money.

This systematic review aimed to evaluate the relationship between miRNAs dysregulation and the occurrence of the metastatic process associated with EMT in OSCC.

Survival outcomes among patients with World Health Organization grade III gliomas and IDH-mutant (IDHmt) gliomas treated with immunotherapy (IT) remain understudied, with most research focusing on grade IV (G4) glioblastomas or IDH wild-type (IDHwt) tumors.

Metastatic colorectal cancer (mCRC) with microsatellite instability-high or mismatch repair deficiency (MSI-H/dMMR) demonstrates limited responsiveness to conventional cytotoxic chemotherapy but increased susceptibility to immune checkpoint inhibition. Although randomized phase III trials have shown superiority of immune checkpoint inhibitors (ICIs) in the first-line setting, a comprehensive synthesis of survival outcomes across key molecular and clinical subgroups remains warranted.

Salivary gland tumors represent a  rare and morphologically heterogeneous group of neoplasms, which represents a  significant diagnostic challenge for histopathologists. Diagnosis is based primarily on morphological evaluation, but immunohistochemical methods are often a necessary complementary tool. However, due to immunophenotype overlap between different tumor entities, even immunohistochemistry may not always allow an unambiguous diagnosis. In recent years, characteristic genomic alterations, particularly gene fusions, have been identified in a number of salivary gland tumors. These alterations are tightly tumor type specific, and their detection may be crucial in diagnostically difficult cases. In addition, selected genetic changes may have prognostic and/ or potential therapeutic significance in the era of personalized medicine. The aim of this review article is to summarize current knowledge in this area.

In line with current guidelines, risk stratification in papillary thyroid microcarcinoma (PTMC) has shifted from simple diameter-based assessments to evaluation of individual tumor biology. Therefore, this study aimed to investigate how baseline clinical and molecular risk factors influence aggressive PTMC phenotypes.

Recurrence after curative treatment remains a major challenge in early-stage non-small cell lung cancer (NSCLC). Molecular-based prediction models may improve risk stratification and support personalized surveillance strategies. To identify and evaluate molecular-based risk prediction models for recurrence and survival following curative treatment of early-stage NSCLC. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines and a published PROSPERO protocol. MEDLINE, EMBASE, and the Cochrane Library were searched for studies published between January 2000 and December 2023. Eligible studies reported performance metrics of molecular-based models predicting recurrence-free survival, cancer-specific survival, or overall survival following curative treatment for NSCLC. Data extraction was performed using the CHARMS framework, risk of bias was assessed using PROBAST, and model performance metrics were pooled using random-effects meta-analysis. Of 2447 records identified, five studies met the inclusion criteria. All models used Cox proportional hazards regression. Molecular predictors included mRNA expression profiles (n = 3), long noncoding RNAs (n = 1), and DNA methylation biomarkers (n = 1). Internal validation studies reported AUC values ranging from 0.66 to 0.89, with a pooled AUC of 0.77 (95% CI = 0.66-0.90; I2 = 88.4%). Two externally validated models reported AUC values of 0.68-0.74, with a pooled AUC of 0.72 (95% CI = 0.68-0.75; I2 = 0%). Two studies were considered high risk of bias due to inappropriate handling of missing data. Molecular-based prediction models demonstrate moderate-to-good discriminative performance for recurrence and survival in early-stage NSCLC. However, broader external validation and improved methodological rigor are required before routine clinical implementation. Trial Registration: PROSPERO identifier: CRD42024599641.

Pancreatic cancer is among the most aggressive malignancies, and late diagnosis remains a key challenge. For a systematic review of pancreatic cancer diagnosis and prognosis, Scopus and Web of Science databases were used for the period from 2016 to 2026. The search query included the following keywords and their combinations: pancreatic cancer, diagnosis, early detection, prognosis, biomarkers, metabolomic profiling, CA19-9, microbiome, metagenomic changes, circulating tumor DNA, genomic analysis. Inclusion criteria included only articles published in English. Exclusion criteria included case reports and studies that did not examine pancreatic cancer. Our analysis demonstrates that integrating multi-omics data, particularly combining traditional CA19-9 with circulating tumor DNA (ctDNA) and metabolomic profiles (lipids, amino acids, carbohydrates), significantly improves diagnostic accuracy. Microbiome composition and genomic alterations further refine risk stratification and prognostic assessment. The synergistic use of these biomarkers may facilitate the development of screening, early diagnosis, risk stratification, and treatment optimization. However, the introduction of new diagnostic approaches into clinical practice requires additional verification, standardization and prospective clinical studies.

Ginsenoside Rg3, a rare protopanaxadiol-type saponin enriched during the heat processing of Panax ginseng, has attracted increasing attention as a multitarget anticancer agent. This systematic review examines the anticancer potential of Rg3 through comprehensive searches of the PubMed and Web of Science databases, with a focus on peer-reviewed preclinical and clinical studies. The therapeutic efficacy of Rg3 is critically influenced by its stereochemical configuration, concentration-dependent bidirectional regulation, and pharmacokinetic constraints, including poor oral bioavailability, rapid clearance, and gut microbiota-mediated metabolism. Nanocarrier-based and targeted delivery systems have substantially improved its pharmacokinetic profile and tumor accumulation, supporting its further development for anticancer applications. Within this pharmacological context, Rg3 exhibits broad-spectrum anticancer activity across multiple solid tumors, including hepatocellular carcinoma, melanoma, lung, ovarian, breast, colon, gastric, and prostate cancers, as well as osteosarcoma, renal cancer, lung adenocarcinoma, glioblastoma, gallbladder, nasopharyngeal, cervical, and pancreatic cancers, and the hematological malignancy multiple myeloma. Mechanistically, Rg3 suppresses cancer progression through coordinated regulation of proliferation, apoptosis, autophagy, ferroptosis, angiogenesis, epithelial-mesenchymal transition, cancer stemness, immune evasion, and redox homeostasis, primarily involving the PI3K/AKT/mTOR, NF-[Formula: see text]B, MAPK, Wnt/[Formula: see text]-catenin, EGFR, and p53 pathways. These effects reflect transferable network-level mechanisms rather than tumor type-restricted actions. Moreover, Rg3 demonstrates synergistic effects with chemotherapy, radiotherapy, targeted therapy, and immunotherapy, while reversing drug resistance and attenuating treatment-related toxicity in multiple cancer models and clinical settings. Overall, this review systematically integrates current evidence on the pharmacokinetics, anticancer spectrum, molecular mechanisms, synergistic combinations, immunomodulatory effects, and clinical applications of Rg3, providing a concise framework for the rational development of Rg3-based combination strategies in precision cancer therapy.

ROS1 rearrangements occur in approximately 1%-2% of non-small cell lung cancer and define a molecular subset characterized by marked sensitivity to tyrosine kinase inhibitors (TKIs). Although ROS1 inhibitors demonstrate high response rates, the potential impact of prior systemic chemotherapy exposure before ROS1 inhibition on subsequent ROS1-TKI efficacy has not been systematically evaluated.

HER2 amplification identifies a subgroup of colorectal cancer with poorer prognosis and resistance to anti-EGFR therapy. We conducted a systematic review and a meta-analysis of available data on anti-HER2 treatments (HER2Tx) in mCRC patients (pts).

The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subject to sampling bias and other complications. Radiomics has emerged as a non-invasive tool to predict these mutations from standard medical images. In this systematic review and meta-analysis, we aimed to evaluate the diagnostic accuracy and methodological quality of radiomics models for predicting key genetic mutations in CRC.

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have revolutionized the prognosis for patients with EGFR-mutant lung cancer. The emergence of the T790M resistance mutation compromises the efficacy of EGFR-TKI therapy. Therefore, assessing EGFR T790M mutation status during non-small cell lung cancer (NSCLC) treatment is crucial for improving NSCLC prognosis.

The prognostic significance of EGFR mutations in resected non-small cell lung cancer (NSCLC) remains uncertain despite their established predictive value in advanced disease. We assessed the prognostic impact of EGFR mutation status on recurrence and disease-free survival (DFS) in resected stage I-III NSCLC.

ObjectiveThis systematic review and meta-analysis evaluates the diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in Chinese patients with lung adenocarcinoma.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and prospectively registered in the International Prospective Register of Systematic Reviews (CRD420251273027), a systematic search of PubMed, Embase, Web of Science, the Cochrane Library, Scopus, China National Knowledge Infrastructure, Wanfang, VIP, and Chinese Biomedical Literature Database was conducted from inception to 31 October 2025. Two reviewers independently screened studies, extracted data, and assessed bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random-effects model was used to synthesize the data. Subgroup analyses were conducted for three factors: (a) imaging modality (computed tomography vs. positron emission tomography-computed tomography); (b) algorithm type (deep learning vs. conventional machine learning); and (3) validation strategy (external vs. internal).ResultsThirteen studies encompassing 6628 patients were included. The pooled sensitivity was 71% (95% confidence interval: 68-74), the pooled specificity was 81% (95% confidence interval: 78-84), and the summary area under the curve was 0.85 (95% confidence interval: 0.82-0.88). Deep learning models significantly outperformed conventional machine learning models (area under the curve: 0.871 vs. 0.798; P = 0.012). Computed tomography-based models yielded higher accuracy than positron emission tomography-computed tomography-based models (area under the curve: 0.879 vs. 0.828; P = 0.038). Models validated on independent external cohorts demonstrated superior performance compared with those relying solely on internal validation (area under the curve: 0.922 vs. 0.841; P = 0.006). Imaging modality was a significant source of heterogeneity (P < 0.05). No threshold effect or publication bias was detected.ConclusionMachine learning-based radiomics models exhibit promising diagnostic accuracy for the noninvasive prediction of epidermal growth factor receptor mutations in Chinese patients with lung adenocarcinoma. Computed tomography-based deep learning models subjected to independent external validation represent the current optimal approach. However, the retrospective nature and substantial heterogeneity of the included studies necessitate large-scale, prospective, multicenter trials with standardized workflows before clinical translation.

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase expressed in a subset of patients with non-small cell lung cancer (NSCLC). Since the approval of crizotinib, which was the first ALK tyrosine kinase inhibitor (TKI), the treatment landscape has rapidly evolved with the development of multiple next-generation TKIs and investigational agents. A structured review of clinical trials registered on ClinicalTrials.gov was conducted to identify studies evaluating therapies in ALK-positive NSCLC, including phase I-IV trials in adult populations, regardless of recruitment status. Trials were screened for ALK-specific relevance and key outcomes including progression-free survival (PFS) and objective response rate (ORR) were extracted. Central nervous system (CNS) activity and resistance mutation profiles were collected from published literature when available. A meta-analysis of ORR was conducted on a subset of 41 trials comprising 66 evaluable cohorts with sufficient efficacy data. Studies evaluated first- through third-generation ALK TKIs, combination regimens, and experimental agents. Meta-analysis of 6,382 patients showed a pooled ORR of 58.5% [95% confidence interval=57.3-59.7], with higher response rates in treatment-naive versus pretreated cohorts (71.8% vs 48.4%). Weighted median PFS was 13.8 months in treatment-naive and 9.8 months in pretreated cohorts. CNS activity, summarized from published reports, indicated superior intracranial efficacy of later generation TKIs. ALK-targeted therapies have significantly improved outcomes in ALK-positive NSCLC. However, CNS progression, acquired resistance, and optimal treatment sequencing continue to limit outcomes. This review synthesizes current evidence to guide clinical practice and future investigations.

Objectives: Tissue-agnostic oncology personalizes treatments based on shared molecular biomarkers, addressing challenges like assay variability, control-arm rigor, and non-proportional hazards. Integrating efficacy, safety, and resistance factors with consistent estimands is essential for evaluating biomarker-matched therapies across histologies. This review aims to quantify and compare their efficacy and safety, and to identify determinants of resistance, using PRISMA-compliant methods. Methods: We conducted a systematic review and random-effects meta-analysis of 38 studies (15,018 participants), employing dual screening, standardized bias assessment, and evaluations of heterogeneity and small-study effects. Hazard ratios (HRs) with 95% CIs were estimated for time-to-event outcomes, and restricted mean survival times were used when the proportional hazards assumption was violated. Results: Trastuzumab deruxtecan improved objective response rates and extended progression-free survival (PFS) and overall survival (OS) in HER2-positive gastric and gastroesophageal junction cancers and in HER2-low metastatic breast cancer, showing longer response durations. In metastatic colorectal cancer with microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR), PD-1 blockade significantly increased PFS and five-year OS despite crossover, with restricted mean survival time gains of about 11 months. In endometrial cancer, dostarlimab combined with chemotherapy improved PFS in both dMMR/MSI-H and mismatch repair-proficient disease and increased OS overall. Encorafenib-based therapies reduced progression and death in BRAF V600E metastatic colorectal cancer. Safety profiles were class-specific: PD-1 inhibitors caused fewer grade 3 or higher adverse events than chemotherapy, whereas trastuzumab deruxtecan was associated with increased interstitial lung disease (ILD) or pneumonitis and higher rates of treatment discontinuation. Conclusion: Biomarker-matched therapies confer significant survival benefits with predictable toxicities. Confidence is strongest for PD-1 inhibitors in MSI-H/dMMR tumors, trastuzumab deruxtecan in HER2-low or HER2-positive cancers, and encorafenib-based regimens in BRAF V600E metastatic colorectal cancer. Implementation should include validated assays (including reconfirmation of HER2 status), prioritize earlier treatment lines where gains are greatest, and require vigilant ILD monitoring. Head-to-head trials and assay standardization, especially for tumor mutational burden, remain priorities.

Circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) have emerged as promising non-invasive biomarkers for the immunotherapy response in advanced non-small cell lung cancer (NSCLC). However, their clinical utility remains uncertain due to variability in findings across studies. We conducted a systematic review and meta-analysis of studies from 2014 to 2024, assessing the predictive value of ctDNA and CTCs in advanced NSCLC patients receiving immunotherapy. Primary outcomes were progression-free survival (PFS) and overall survival (OS). Forty-four studies were included (28 ctDNA, 16 CTC cohorts). High baseline ctDNA was associated with worse OS (pooled HR = 1.38, 95% CIs: 1.17-1.63), while baseline CTC detection predicted worse PFS (pooled HR of 3.65 (95% CIs: 1.58-8.41) and OS (pooled HR = 2.30, 95% CIs: 1.54-3.44). An on-treatment ctDNA decrease or clearance was associated with improved PFS (pooled HR = 0.34, 95% CIs: 0.25-0.47) and OS (pooled HR = 0.33, 95% CIs: 0.24-0.44). Evidence for other ctDNA- and CTC-derived biomarkers (blood tumour mutational burden, genomic alterations, dynamic CTC changes, and CTC PD-L1 expression) was limited or inconsistent. The interpretation of these findings is limited by heterogeneity in assay platforms, biomarker definitions, the analytical threshold, and sampling timepoints across studies. While ctDNA and CTCs show significant potential as predictive biomarkers in advanced NSCLC, further validation is needed in larger prospective studies using standardized assays. At present, ctDNA and CTC monitoring can complement but cannot replace radiological assessments in guiding immunotherapy decisions for NSCLC patients.